1676
BEN GRUNWALD & JOHN RAPPAPORT
The Wandering Ocer
abstract. “Wandering ocers” are law-enforcement ocers fired by one department, some-
times for serious misconduct, who then find work at another agency. Policing experts hold dispar-
ate views about the extent and character of the wandering-ocer phenomenon. Some insist that
wandering ocers are everywhere—possibly increasingly so—and that they’re dangerous. Others,
however, maintain that critics cherry-pick rare and egregious anecdotes that distort broader reali-
ties. In the absence of systematic data, we simply do not know how common wandering ocers
are or how much of a threat they pose, nor can we know whether and how to address the issue
through policy reform.
In this Article, we conduct the first systematic investigation of wandering ocers and possibly
the largest quantitative study of police misconduct of any kind. We introduce a novel data set of
all 98,000 full-time law-enforcement ocers employed by almost 500 dierent agencies in the
State of Florida over a thirty-year period. We report three principal findings. First, in any given
year during our study, an average of just under 1,100 ocers who were previously fired—three
percent of all ocers in the State—worked for Florida agencies. Second, ocers who were fired
from their last job seem to face diculty finding work. When they do, it takes them a long time,
and they tend to move to smaller agencies with fewer resources in areas with slightly larger com-
munities of color. Interestingly, though, this pattern does not hold for ocers who were fired ear-
lier in their careers. Third, wandering ocers are more likely than both ocers hired as rookies
and those hired as veterans who have never been fired to be fired from their next job or to receive
a complaint for a “moral character violation.” Although we cannot determine the precise reasons
for the firings, these results suggest that wandering ocers may pose serious risks, particularly
given how dicult it is to fire a police ocer. We consider several plausible explanations for why
departments nonetheless hire wandering ocers and suggest potential policy responses to each.
the wandering officer
1677
authors.
Ben Grunwald is Assistant Professor, Duke University School of Law. John Rap-
paport is Assistant Professor and Ludwig and Hilde Wolf Research Scholar, University of Chicago
Law School. The authors thank Guangya Liu at Duke and Morgen Miller, Rafeh Qureshi, and
Bartosz Woda in the Coase-Sandor Institute at the University of Chicago Law School for help
constructing the data set; Dhammika Dharmapala and Richard McAdams for generously sharing
data; Matt Adler, Will Baude, Panka Bencsik, Sam Buell, Mitch Downey, John de Figueredo, Mi-
chael Frakes, Barry Friedman, Brandon Garrett, Roger Goldman, Hiba Hafiz, Emma Kaufman,
Kate Levine, Darrell Miller, Richard Myers, Michael Pollack, Kyle Rozema, Seth Stoughton, and
Samuel Walker for feedback and suggestions; Dylan Demello, Carly Gibbs, Morgan Gehrls, Viraj
Paul, and Catherine Prater for research assistance; and Terry Baker at the Florida Department of
Law Enforcement for assistance interpreting the data. The paper also benefited from presentations
at Duke University School of Law, Gonzaga University School of Law, the American Society of
Criminology Annual Conference, the Law and Society Association Conference, the Criminal Law
Roundtable at UNC School of Law, the Junior Faculty Forum at Richmond School of Law, the
Stockholm Criminology Symposium, the ETH Zurich Conference on Data Science and Law, the
European University Institute for the Rule of Law, and the Law of the Police Conference. John
Rappaport acknowledges The Darelyn A. & Richard C. Reed Memorial Fund for financial support.
the claims of official reason
the yale law journal 129:1676 2020
1678
article contents
introduction 1680
i. the law-enforcement labor market 1691
A. Hiring 1691
B. Discipline 1693
ii. literature review 1698
A. Correlates of Police Misconduct 1698
B. Labor Economics 1702
iii.data 1704
A. Automated Training Management System (ATMS) 1704
B. Supplemental Data Sources 1709
C. Limitations 1710
iv. describing the wandering officer 1711
A. The Law-Enforcement Labor Market in Florida 1712
1. Hirings 1712
2. Separations 1713
B. The Wandering Ocer 1716
1. How Common Are Wandering Ocers? 1716
2. How Easily Do Wandering Ocers Find New Work? 1718
a. Reemployment Rates 1719
b. Time to Reemployment 1722
c. Distance Traveled for Reemployment 1724
d.Number of Subsequent Jobs 1724
3. Where Do Wandering Ocers Go? 1727
a. Agency Size 1727
b. Agency Resources 1728
c. Racial Composition 1729
d.Unemployment 1731
e. Crime 1733
the wandering officer
1679
4.
Do Wandering Ocers Engage in More Misconduct? 1734
a. Firing 1734
b. Complaints 1741
c. Explanations 1747
v. predicting which wandering officers get fired again 1754
vi.mechanisms and reforms 1758
A. Poor Information 1758
B. Unawareness of Risk 1761
C. Inadequate Alternatives 1762
D. Countervailing Benefits 1764
E. Cost Externalization 1767
conclusion 1771
appendix 1772
the claims of official reason
the yale law journal :
introduction
With all that has been said and written about the tragic death of twelve-year-
old Tamir Rice in Cleveland, one fact attracts less attention than it should: the
ocer who fired the fatal shot had been “allowed . . . to resign” from his previ-
ous job in Independence, Ohio, aer suering a “dangerous loss of composure”
during firearms training.
1
According to his supervisors, Tim Loehmann “would
not be able to substantially cope, or make good decisions” in stressful scenarios.
2
A year or so later, however, the Cleveland Police Department failed to review
Loehmann’s personnel file before giving him a gun.
3
Another Ohio department
later hired Loehmann aer he killed Rice and was fired by Cleveland.
4
This story is not unique. Consider what happened in tiny Tulia, Texas. In a
massive early-morning raid on July , , police arrested a full fih of Tulia’s
black adults.
5
Aer parading them across the courthouse lawn in their night-
clothes, Tulia authorities charged the arrestees—roughly forty out of fiy of
whom were black—with felony drug oenses. The evidence in each case con-
sisted of the testimony of a single undercover narcotics ocer, Tom Coleman.
Coleman claimed he had purchased drugs, mostly powder cocaine, from each of
the defendants—over one hundred buys in total. Most were convicted, their sen-
tences ranging from to years. The State crowned Coleman “Lawman of
the Year.”
1. Shaila Dewan & Richard A. Oppel Jr., In Tamir Rice Case, Many Errors by Cleveland Police,
Then a Fatal One, N.Y.
TIMES (Jan. , ) (internal quotation marks omitted), https://
www.nytimes.com////us/in-tamir-rice-shooting-in-cleveland-many-errors-by
-police-then-a-fatal-one.html [https://perma.cc/QYN-YML].
2. Id.
3. Id.
4. Matthew Haag, Cleveland Ocer Who Killed Tamir Rice Is Hired by an Ohio Police Department,
N.Y.
TIMES (Oct. , ), https://www.nytimes.com////us/timothy-loehmann
-tamir-rice-shooting.html [https://perma.cc/V-SDT]. Loehmann ended up withdraw-
ing his application, however, before commencing work. Amir Vera, Ocer Who Shot Tamir
Rice Withdraws Application to Small Police Department in Ohio, CNN (Oct. , ),
https://www.cnn.com////us/tamir-rice-ocer-application/index.html [https://
perma.cc/TST-BHKS]. He continues to contest Cleveland’s decision to terminate him. Jane
Morice, Appeal Filed on Behalf of Cleveland Police Union to Overturn Firing of Timothy Loehmann,
Ex-Cleveland Cop Who Fatally Shot Tamir Rice, C
LEVELAND (Mar. , ), https://
www.cleveland.com/crime///appeal-filed-on-behalf-of-cleveland-police-union-to
-overturn-firing-of-timothy-loehmann-ex-cleveland-cop-who-fatally-shot-tamir-rice.html
[https://perma.cc/UFC-KKW].
5. The following facts are drawn from NATE BLAKESLEE, TULIA: RACE, COCAINE, AND CORRUP-
TION IN A
SMALL TEXAS TOWN (); and Vanita Gupta, Critical Race Lawyering in Tulia,
Te xa s , F
ORDHAM L. REV. ().
the wandering officer
Under pressure from the media and postconviction litigation, Coleman’s
cases later began to crumble. Coleman, it turns out, had never recorded his buys,
nor were there any witnesses; most of the time, there was no corroboration of
any sort. No drugs, money, or weapons had been seized during the raid. Cole-
man’s written reports were vague. He misidentified suspects, some of whom had
rock-solid alibis. And marijuana and crack, not powder cocaine, were the preva-
lent vices in Tulia’s impoverished black community. By , Coleman’s credi-
bility was shredded. He was indicted for perjury. Seeing the writing on the wall,
the prosecutors eventually joined the trial judge in recommending that the con-
victions be vacated. In August , the governor pardoned the Tulia defend-
ants.
Much of what brought Coleman down stemmed from what the New York
Times called his “wretched work history.”
6
His first job was at a jail in the City
of Pecos,
7
where he was “lazy and inattentive at work and in constant danger of
being fired.”
8
He “abruptly quit” and le the state, only to return and find work
as a deputy at the nearby Pecos County Sheri’s Oce.
9
Aer five years there,
Coleman again “abruptly le town . . . owing thousands of dollars in delinquent
bills.”
10
Aer a “brief stint as a jailer” in Denton County, Coleman became a
sheri’s deputy in Cochran County.
11
He lasted about two years there, skipping
town aer the county attorney witnessed him stealing gas from the county
pumps.
12
He owed thousands of dollars to local businesses.
13
The Cochran
County Sheri sent an angry letter about Coleman to the State. “Coleman
should not be in law enforcement,” the sheri wrote, “if he is going to do people
the way he did this town.”
14
At this point, Coleman managed to join the regional task force that sent him
to Tulia. The task force hired Coleman despite a background check revealing that
he “was a discipline problem, that he was ‘too gung ho,’ that he had been accused
of kidnapping his son in a custody battle, . . . and . . . that he had . . . ‘possible
6. Bob Herbert, Kaa in Tulia, N.Y. TIMES (July , ), https://www.nytimes.com//
//opinion/kaa-in-tulia.html [https://perma.cc/DDD-RX].
7. Nate Blakeslee, The Color of Justice, TEX. OBSERVER (June , ), https://
www.texasobserver.org/-the-color-of-justice [https://perma.cc/FX-LNVB].
8. BLAKESLEE, supra note , at .
9. Id.
10. Blakeslee, supra note .
11. Id.
12. Id.; see BLAKESLEE, supra note , at .
13. BLAKESLEE, supra note , at .
14. Blakeslee, supra note .
the yale law journal :
mental problems.’”
15
During Coleman’s tenure with the task force, Cochran
County indicted him for stealing the gas and notified Swisher County, where
Tulia sits.
16
The Swisher County Sheri’s Oce arrested Coleman—during the
undercover operation—but he never faced trial and the charges were dropped.
17
Even aer leaving Tulia, Coleman continued to bounce around. In the eighteen
months aer departing, Coleman worked for three dierent task forces. He was
fired from the third, in Waxahachie, for sleeping with a sex worker who was an
informant for his then-employer.
18
Coleman is the archetypal “wandering ocer,” or what those in policing cir-
cles have called a “gypsy cop.” These are police ocers who are fired or who
resign under threat of termination and later find work in law enforcement else-
where.
19
And although Coleman and Loehmann are prime examples, there are
scores of others. Indeed, as the following examples show, wandering ocers ap-
pear all over.
While William Melendez was working for the Detroit Police Depart-
ment in , local prosecutors alleged that he had leveled false accusa-
tions of drug possession. The federal government later indicted him for
planting evidence, filing bogus reports, and perjury (he was acquitted).
Melendez was forced out of the department in aer his license
lapsed. Two other Michigan municipalities—Highland Park and then
Inkster—put Melendez back on the street. In , while in Inkster,
Melendez brutally beat a motorist about the head, leading to a . mil-
lion civil settlement and two criminal convictions.
20
While working for the St. Louis Police Department in , Eddie Boyd
III pistol-whipped a twelve-year-old girl; a year later, he struck another
child in the face with his gun or handcus before falsifying a report.
15. BLAKESLEE, supra note , at (quoting Pecos County Chief Deputy Sheri Cli Harris).
16. Id. at -; Gupta, supra note , at .
17. BLAKESLEE, supra note , at .
18. Id. at .
19. See, e.g., TOM BARKER, POLICE ETHICS: CRISIS IN LAW ENFORCEMENT (d ed. ); Gordon
Dill, South Carolina Police Shortage Means Employment for “Gypsy” Ocers, N
EWS (Feb. ,
, : PM), https://www.wspa.com/news/south-carolina-police-shortage-means
-employment-for-gypsy-ocers/ [https://perma.cc/KTS-VP] (“A gypsy
cop! That’s been termed an ocer that will jump from agency to agency. They have maybe
agencies under their belt within a year period.” (quoting Florence McCants, South Car-
olina Criminal Justice Academy)).
20. Jim Schaefer & Gina Kaufman, How Problem Cops Stay on Michigan’s Streets, DET. FREE PRESS
(Sept. , ), https://www.freep.com/story/news/local/michigan////how
-problem-cops-stay-street/ [https://perma.cc/CRU-HUB].
the wandering officer
Shortly aer he resigned his position with St. Louis, Boyd was hired in
St. Ann, Missouri, and later, again, in Ferguson.
21
Nicholas Hogan, an ocer with the Tukwila Police Department in
Washington, pepper-sprayed a suspect who was restrained on a gurney
in a hospital in . Hogan was federally indicted for the act and
Tukwila fired him. In , the police department in nearby Snoqualmie
hired him only to fire him later for having an aair with the wife of a
fellow ocer. He was also subsequently incarcerated for the pepper-
spray incident.
22
New Orleans Police Department ocer Carey Dykes was “sued for al-
leged brutality, accused of having sex with a prostitute while on duty
and caught sleeping in his patrol car instead of responding to a shoot-
ing.”
23
An internal aairs investigation found seventeen violations of de-
partment rules. New Orleans fired Dykes in . Later the same year,
Dykes found police work at the Delgado Community College in New
Orleans and then the Orleans Parish Sheri’s Oce.
24
Additional examples abound, each as shocking as the last.
25
Yet the scope and
nature of the wandering-ocer phenomenon are dicult to pin down. Some
21. Timothy Williams, Cast-Out Police Ocers Are Oen Hired in Other Cities, N.Y. TIMES
(Sept. , ), https://www.nytimes.com////us/whereabouts-of-cast-out-police
-ocers-other-cities-oen-hire-them.html [https://perma.cc/DP-WPM].
22. Mike Carter, Jail Time for Ex-Tukwila Cop Who Pepper-Sprayed Handcued Man in Hospital,
S
EATTLE TIMES (Mar. , ), https://www.seattletimes.com/seattle-news/crime/ex
-tukwila-cop-sentenced-to--months-for-pepper-spraying-handcued-man-in-hospital
[https://perma.cc/GWV-ZR].
23. Kimbriell Kelly et al., Forced Out over Sex, Drugs and Other Infractions, Fired Ocers Find Work
in Other Departments, W
ASH. POST (Dec. , ), https://www.washingtonpost.com
/investigations/forced-out-over-sex-drugs-or-child-abuse-fired-ocers-find-work-in
-other-departments////e-da-e-bf-dfc_story.html
[https://perma.cc/MCX-EXK].
24. Id.
25. See, e.g., Anthony Cormier & Matthew Doig, Embattled Ocers Land on Their Feet, HERALD-
T
RIB. (Sarasota, Fla.) (Dec. , ), https://www.heraldtribune.com/news//special
-report-embattled-ocers-land-on-their-feet [https://perma.cc/KFX-CTTG]; Anthony L.
Fisher, Why It’s So Hard to Stop Bad Cops from Getting New Police Jobs, R
EASON (Sept. , ),
https://reason.com/archives////why-its-so-hard-to-stop-bad-cops-from-ge
[https://perma.cc/MWC-HFH]; Jose Gaspar, McFarland’s Hiring of Four Police Ocers
Raises Questions, C
ALIFORNIAN (Nov. , ), http://www.bakersfield.com/news/jose
-gaspar-mcfarland-s-hiring-of-four-police-ocers-raises/article_dfdf-d--ac
-eeedbe.html [https://perma.cc/CFQ-NDCE]; Gary A. Harki, Still in Uniform: Prob-
lem Police Rarely Lose Certification in West Virginia, S
UNDAY GAZETTE-MAIL (Charleston, W.
Va.), Dec. , , at A; David Kroman, “Disqualifying Conduct” Rarely an Obstacle for Fired
Police to Get Rehired, C
ROSSCUT (Apr. , ), https://crosscut.com///fired-ocers
the yale law journal :
experts, from their own experience, or from anecdotes like these, insist that wan-
dering ocers are legion
26
—and possibly increasingly so.
27
Others deny that
wandering ocers exist
28
or discern an exaggerated narrative cobbled together
-can-become-hired-ocers [https://perma.cc/WD-MVK]; Matt Lait, Convicted Cop
Hired as Police Chief, L.A.
TIMES (Feb. , ), http://articles.latimes.com//feb/
/local/me-maywood [https://perma.cc/KQA-ZTGV]; Nomaan Merchant et al., Broken
System Lets Problem Ocers Jump from Job to Job, C
HI. TRIB. (Nov. , ), https://
www.chicagotribune.com/news/nationworld/ct-police-ocer-sexual-misconduct
-investigation--story.html [https://perma.cc/BD-HD]; Christopher N. Osher,
Colorado Laws Allow Rogue Ocers to Stay in Law Enforcement, D
ENV. POST (July , ),
https://www.denverpost.com////colorado-laws-allow-rogue-ocers-to-stay-in
-law-enforcement [https://perma.cc/NQM-SMEM]; Push to Keep “Gypsy Cops” with Ques-
tionable Pasts O the Streets, CBS
NEWS (Sept. , , : AM), https://www.cbsnews.com
/news/gypsy-cops-with-questionable-pasts-hired-by-dierent-departments-lack-of
-oversight-police [https://perma.cc/UCN-UPT]; Casey Toner & Jared Rutecki, The Re-
volving Door: Troubled Ocers Get Frequent Career Chances, WBEZ (Jan. , ), http://
interactive.wbez.org/taking-cover/revolving-door [https://perma.cc/QDT-MX]; Steven
Yoder, How to Keep Bad Cops on the Beat, A
M. PROSPECT (July , ), http://prospect.org
/article/how-keep-bad-cops-beat [https://perma.cc/XR-DQT].
26. See, e.g., BLAKESLEE, supra note , at (“Everybody’s talking about Tom Coleman—well,
there are whole task forces of Tom Colemans out there.” (quoting Barbara Markham, former
narcotics task force ocer)); Roger Goldman & Steven Puro, Revocation of Police Ocer Cer-
tification: A Viable Remedy for Police Misconduct, S
T. LOUIS U. L.J. , () (“Even
when [unfit ocers] are terminated, these ocers oen go to work for other departments
within the state.”); Martha L. Shockey-Eckles, Police Culture and the Perpetuation of the Ocer
Shue: The Paradox of Life Behind ‘The Blue Wall’, H
UMANITY & SOC’Y , () (“In
urban areas such as St. Louis, the ocer who resigns rather than face licensure revocation
typically finds employment in a neighboring municipality with relative ease.”); Richard Ab-
shire, Sheri: Cases Show Staers Not Above Law—Kaufman: He Faults Agencies That Let Ocers
Become “Gypsy Cops,” D
ALLAS MORNING NEWS, Sept. , , at B (“Sheri Byrnes said too
many law enforcement agencies have quietly dismissed problem ocers and not prosecuted
them for criminal conduct, enabling so called ‘gypsy cops’ to go from agency to agency, oen
taking trouble with them.”); Dill, supra note (“It happens every day. It’s happened here. It
happens everywhere.” (quoting Pacolet, South Carolina Police Chief Raymond Webb));
Candice Norwood, Can States Tackle Police Misconduct with Certification Systems?, A
TLANTIC
(Apr. , ), https://www.theatlantic.com/politics/archive///police-misconduct
-decertification/ [https://perma.cc/FDF-XYJ] (“There are many cases around the
country where ocers leave their departments because of misconduct and then they are re-
hired—sometimes knowingly, sometimes not—by other departments.” (quoting Professor
Roger Goldman)).
27. See, e.g., Schaefer & Kaufman, supra note (describing former executive director of the Mich-
igan Commission on Law Enforcement Standards as conceding that the wandering-ocer
phenomenon is “a concern, and could be getting worse because of widespread cuts to police
pay and benefits in recent years”).
28. See, e.g., Sarah Childress, How States Are Moving to Police Bad Cops, FRONTLINE (Apr. , ),
https://www.pbs.org/wgbh/frontline/article/how-states-are-moving-to-police-bad-cops
[https://perma.cc/QCH-GBKD] (“Skeptics of certification . . . argue that no police chief or
the wandering officer
from cherry-picked anecdotes that distort broader realities.
29
When the rhetoric
is swept away, “[i]t is unclear how far-reaching such problems may be.”
30
As
policing expert Samuel Walker has remarked: “It is believed to be a problem
nationwide. The phrase ‘gypsy cops’ has come up. There’s not any solid research
on that. We don’t know how common it is.”
31
The answer matters. If wandering ocers are rampant and dangerous, iden-
tifying and stopping them should be a police-reform priority—especially be-
cause, by their nature, they touch new communities with each move. And
“[p]oor communities,” writes Monica Bell, “are more likely to hire ‘gypsy
cops’ . . . because their resource constraints make it more dicult for them to
discriminate between good and bad ocers.”
32
The answer also matters because,
for many individuals, policing represents—indeed, embodies—“the law.”
33
Law-
enforcement ocers interact with tens of millions of American residents each
year,
34
many of whom have little other contact with the state.
35
And “[t]he be-
sheri would hire an ocer with a tarnished record . . . .”); Heather Goldin, Bill Seeks Licens-
ing for Massachusetts Police Ocers, S
ENTINEL & ENTERPRISE (July , ), https://
www.sentinelandenterprise.com////bill-seeks-licensing-for-mass-police-ocers
[https://perma.cc/JQ-DSC]; id. (“It’s not possible for an ocer [fired for misconduct]
to get another job in civil service.” (quoting Ray McGrath, Legislative Director, International
Brotherhood of Police Ocers)); Yoder, supra note (“[California] doesn’t need to cancel
certificates . . . because its training program and standards for entering the profession are
among the best in the country—rogue ocers are kept out of the force from the get-go.”).
29. See, e.g., Fisher, supra note (“Police representatives maintain that these anecdotes are cherry
picked. [The International Brotherhood of Police Ocers’ legislative director] says those who
support a national database [of ocer decertifications] ‘use these wild examples’ of ‘some-
what outlandish’ cases ‘that happened years ago.’”).
30. Williams, supra note .
31. Harki, supra note ; see Schaefer & Kaufman, supra note (citing former executive director
of the Michigan Commission on Law Enforcement Standards as saying that the Commission
“does not know how many problem cops there are in Michigan, let alone how many jump
from job to job”).
32. Monica C. Bell, Police Reform and the Dismantling of Legal Estrangement, YALE L.J. ,
().
33. Montré D. Carodine, “Street Cred,” U.C. DAVIS L. REV. , () (internal quotation
marks omitted).
34. See Elizabeth Davis et al., Contacts Between Police and the Public, 2015, BUREAU JUST. STAT.
(Oct. ), https://www.bjs.gov/content/pub/pdf/cpp.pdf [https://perma.cc/PA
-XFU].
35. See TOM R. TYLER & YUEN J. HUO, TRUST IN THE LAW: ENCOURAGING PUBLIC COOPERATION
WITH THE
POLICE AND COURTS ().
the yale law journal :
havior of individual police ocers” in these encounters “communicates infor-
mation to members of the public that they use to make judgments about the
nature of legal authority within their society.”
36
If wandering ocers are just scapegoats, however, they may distract from
other, more pressing problems in policing. Aer all, just because some wander-
ing ocers commit misconduct does not mean that, ex ante, they were any more
likely to do so than their peers. Plenty of ocers who have never been fired end
up breaking the rules. In some other labor settings, experts have recognized that
past experience does not predict future performance. “Malpractice claims against
physicians,” for example, “are simply too stochastic to lend them much credence
as an indicator of physician quality or risk.”
37
This Article brings much-needed data to the debate. It presents the first
large-scale empirical investigation of the wandering-ocer phenomenon and
possibly the largest quantitative study of police misconduct of any kind.
38
We
conduct our analysis using a novel data set that begins with employment records
of all , full-time law-enforcement ocers employed in the State of Florida,
covering nearly five hundred agencies, over a thirty-year period. Crucially, our
data permits us to distinguish between ocers who separated from their agen-
cies voluntarily and those who separated because they were fired. And for nearly
two decades, it also identifies ocers who resigned while under investigation.
Although we cannot know precisely why all of these ocers were pushed out—
36. Id. at . Tyler and Huo’s work “suggest[s] that personal experiences generalize to shape
broader views about the law and legal institutions,” id. at , as well as one’s “status in the
democratic community,” Vesla M. Weaver, The Only Government I Know: How the Criminal
Justice System Degrades Democratic Citizenship, B
OS. REV. (June , ), http://
bostonreview.net/us/vesla-m-weaver-citizenship-custodial-state-incarceration [https://
perma.cc/FR-XFNZ]; see also Bell, supra note , at (explaining how current policing
regimes “can operate to eectively banish whole communities from the body politic”); Tom
R. Tyler et al., Street Stops and Police Legitimacy: Teachable Moments in Young Urban Men’s Legal
Socialization, J.
EMPIRICAL LEGAL STUD. , () (asserting that “personal experiences
with the police . . . are [a] key determinant of legal socialization,” which is “the developmental
process by which individuals internalize the norms of the law”). Experiences with the police
“are translated into common stories about who is an equal member of a rule-governed society
and who is subjected to arbitrary surveillance and inquiry.” C
HARLES R. EPP ET AL., PULLED
OVER: HOW POLICE STOPS DEFINE RACE AND CITIZENSHIP ().
37. Michelle M. Mello & Troyen A. Brennan, Deterrence of Medical Errors: Theory and Evidence for
Malpractice Reform, T
EX. L. REV. , () (citing FRANK A. SLOAN ET AL., INSUR-
ING
MEDICAL MALPRACTICE - ()).
38. In a previous study, touted as “perhaps the largest study of police misconduct ever conducted
in the United States,” Robert Kane and Michael White analyzed the careers of roughly ,
New York City ocers (plus a ,-ocer comparison group) across a twenty-year period.
See R
OBERT J. KANE & MICHAEL D. WHITE, JAMMED UP: BAD COPS, POLICE MISCONDUCT, AND
THE
NEW YORK CITY POLICE DEPARTMENT - (). As we explain, our study covers more
ocers, more jurisdictions, and more years than does the Kane and White study.
the wandering officer
something that can be dicult to ascertain, even with access to an agency’s per-
sonnel records—we do have a general indication, such as whether the firing re-
lated to performance, training, or misconduct. The employment records also
contain demographic information about each ocer—such as age, race, sex, and
education—enabling us to describe the wandering ocer in detail.
We report three principal findings. First, wandering ocers—defined as of-
ficers who have been fired from a position in law enforcement or corrections be-
fore landing a law-enforcement job at another agency—are fairly common in ab-
solute terms. In any given year, roughly , full-time law-enforcement ocers
who had been previously fired were working for other Florida agencies. And for
reasons we explain, we suspect that this is a low-end estimate, both for Florida
and for what it implies about other jurisdictions. At the same time, when viewed
in relative terms, the number appears more modest: no more than of ocers
employed in a given year in Florida during the study period were wanderers.
Second, assuming that many fired ocers are seeking new law-enforcement
work and are willing to move to another agency, they seem to face diculty find-
ing employment. Ocers who were fired from their immediately preceding job
subsequently obtain work in Florida law enforcement at half the rate of ocers
who separate voluntarily, and the discrepancy is growing over time. Fired oc-
ers also take much longer to start another job and typically move to smaller agen-
cies with fewer resources in communities with slightly higher proportions of res-
idents of color. Interestingly, most of these discrepancies disappear for ocers
who were fired earlier in their career rather than from their immediately preced-
ing job. We hypothesize that agencies view these ocers as having redeemed
themselves.
Third, wandering ocers are far more likely—than both rookies and veter-
ans who have never been fired—to be fired from their next job. They are also
more likely to receive complaints at the state licensing board for “moral character
violations,”
39
including complaints for violent or sexual misconduct and for in-
tegrity-related misdeeds. We cannot fully rule out the possibility that these ele-
vated risks are due to the characteristics of the agencies that hire wandering of-
ficers or to enhanced monitoring or discipline of these ocers. Perhaps some
agencies even hire wandering ocers on a de facto “probationary” basis, intend-
ing simply to terminate them if problems arise. For reasons we discuss, however,
we are doubtful that these are the principal explanations for our results.
We also explore whether certain ocer or agency characteristics—such as
ocer age or education or agency hiring and training requirements—predict
39. As we describe in more detail below, “moral character violations” can include committing any
felony or certain enumerated misdemeanors (regardless of criminal prosecution) or commit-
ting other specified acts such as using excessive force or making false statements in a court
proceeding. See F
LA. ADMIN. CODE ANN. r. B-.() ().
the yale law journal :
which wandering ocers are most likely to fail. The idea is that agencies might
manage risk by screening for certain ocer characteristics or adopting more
stringent hiring or training requirements in the event that, for whatever reason,
they choose to hire a wandering ocer. Unfortunately, we find little reason for
optimism on this front, although agencies may have information about ocer
or agency characteristics that is more predictive than what we can observe here.
These findings present a puzzle: if wandering ocers are so risky, why do
agencies hire them? Our data do not permit us to isolate a single causal mecha-
nism, and the reality is that several are probably at play. First, agencies may hire
wandering ocers because they fail to identify them as such, either due to inad-
equate background checks or candidates’ deliberate concealment of their disci-
plinary history.
40
The favored solution seems to be improving the existing na-
tional decertification database. This database records decisions by state agencies
to “decertify” ocers, which prevent them from working elsewhere in the same
state. Coverage, however, is spotty. Likewise, in , the President’s Task Force
on
st Century Policing recommended expanding the existing database into a
comprehensive national register to address the problem of ocers who are fired
and decertified in one state and then move to another state and land a job in law
enforcement there.
41
We discuss the importance, but also the substantial limita-
tions, of the national decertification database as a tool to stop wandering ocers,
and we suggest potential improvements.
Second, agencies might know they are hiring a wandering ocer but be un-
aware that such ocers are, in general, risky hires. Some agencies might, for
example, think an ocer who has been fired will be more conscientious than oth-
ers, if firing acts as a deterrent sanction. As one ocial put it, “You think it’s a
second chance so they’ll try hard, which is what they’re telling you.”
42
Moreover,
most agencies probably hire too few wandering ocers to notice that, as our data
40. See, e.g., Goldman & Puro, supra note , at (describing two ocers hired by the West
Palm Beach Police Department despite serious, undiscovered problems at their previous po-
lice departments); Ian Cohen, Questionable Hires, Low Morale Plague Palm Beach Police, P
ALM
BEACH DAILY NEWS (Apr. , ), https://www.palmbeachpost.com/news/
/exclusive-questionable-hires-low-morale-plague-palm-beach-police [https://perma.cc
/MKP-USD] (reporting that the Palm Beach Police Department “ignored or missed red
flags in the applications of its ocers, some of whom had applied [to] and were rejected with
cause from multiple agencies before being accepted by Palm Beach”).
41. See Final Report, PRESIDENT’S TASK FORCE ON ST CENTURY POLICING - (), https://
cops.usdoj.gov/pdf/taskforce/taskforce_finalreport.pdf [https://perma.cc/SPA-DQT]
(quoting National Decertification Index—FAQs, I
NT’L ASS’N DIRECTORS L. ENFORCEMENT
STANDARDS & TRAINING, https://www.iadlest.org/Portals//Files/NDI/FAQ/ndi_faq.html
[https://perma.cc/EAH-QAUQ]).
42. Cormier & Doig, supra note (quoting Lieutenant David Hubbard of the Eustis, Florida
Police Department).
the wandering officer
suggest, their actual pattern of behavior seems to cut the other way. In light of
our new evidence, law-enforcement agencies should be cautious about hiring
wandering ocers. And agencies that do hire them might invest in enhanced
monitoring and support or, alternatively, adopt recidivist penalties designed to
deter misconduct in this high-risk population.
Third, agencies may know that wandering ocers are risky hires but lack
any better alternatives—that is, the wandering ocers they hire may be less risky
than the alternative candidates. Consistent with our finding that wandering of-
ficers tend to move to agencies with fewer resources, cash-strapped agencies—
and particularly those in undesirable locations—may be unable to oer compen-
sation competitive enough to attract candidates of higher quality than the wan-
dering ocers they hire. In that case, and assuming ocers must be hired at all,
the solution may be to improve the pool of candidates by raising salaries or re-
ducing barriers to entry. Certainly, if law-enforcement agencies were sophisti-
cated, for-profit entities that internalized the costs of bad hiring decisions, this
story would be compelling. As we discuss in a moment, however, there are rea-
sons to think that agencies do not internalize these costs.
Ideally, to test this third hypothesis, we would compare each wandering of-
ficer who was hired with the “marginal ocer”—the ocer the agency would
have hired had it decided not to hire the wanderer. Unfortunately, we are unable
to identify the actual marginal ocer with our data. We make progress on this
problem by isolating, for each wandering ocer, a plausible candidate cohort—
a group of ocers who were hired around the same time by nearby agencies with
similar budgetary resources. We find that wandering ocers are still riskier than
this narrower comparator group, providing some evidence that this third hy-
pothesis is, at best, a partial explanation.
Fourth, agencies may know that wandering ocers are risky but hire them
because of the unique benefits they are perceived to bring. Some agencies, for
example, may actually seek out “cowboy” veteran ocers to work the toughest
beats. Given the “band of brothers” ethos that pervades American policing, some
law-enforcement leaders, too, may feel a “warm glow” upon hiring ocers who
have been cast out by other agencies.
43
This may explain the seemingly cavalier
attitudes police chiefs sometimes express toward hiring ocers who have been
fired before. “We believe in redemption,” explained one police chief.
44
“This
43. See, e.g., Barbara E. Armacost, Organizational Culture and Police Misconduct, GEO. WASH. L.
REV. , () (describing how, “[i]n the face of outside criticism, cops tend to circle
the wagons, adopting a ‘code of silence,’ protecting each other, and defending each other’s
actions”).
44. Schaefer & Kaufman, supra note (quoting Police Chief Chester Logan of Highland Park,
Michigan).
the yale law journal :
stu is supposed to follow you forever?” another wondered. “For the rest of your
career? Of course I’m going to give somebody a second chance.”
45
Relatedly,
agencies might hire wandering ocers who are riskier than the alternatives if the
cost is lower. Given that they typically have little if any discretion over salaries,
it is unlikely that police administrators hire wandering ocers because they are
willing to accept lower salaries than similarly experienced candidates who have
never been fired.
46
But wandering ocers may be cheaper than fresh recruits, as
most Florida agencies pay police-academy tuition when they onboard rookie
hires. Compared to rookies, wandering ocers are able to hit the streets more
quickly, too.
Finally, agencies may externalize, and thus discount, the costs of hiring wan-
dering ocers. Although agencies nearly always indemnify ocers against fi-
nancial liability, the ocers themselves enjoy qualified immunity, which, in
practice, protects the agencies as well.
47
Direct municipal liability for negligent
hiring, moreover, is rare.
48
And even when municipalities do end up paying for
harms wandering ocers have caused, whether agencies internalize those costs
depends on the institutional and budgetary niceties of municipal governance.
49
If cost externalization contributes to the hiring of wandering ocers, and we
suspect it does, the appropriate response is to improve existing mechanisms of
accountability. This is in some sense the central challenge of all civil-rights lia-
bility regimes, however; many have tried and failed to accomplish it. Barring
successful accountability reforms, and if future research corroborates our find-
ings, states could consider following Connecticut and banning local agencies
from hiring wandering ocers altogether.
The remainder of the Article proceeds as follows. Part I describes the law-
enforcement labor market. Part II reviews the pertinent literature. Part III de-
scribes our data in detail. Part IV presents our findings about the wandering of-
45. Cormier & Doig, supra note (quoting Police Chief Roberto Fulgueira of Sweetwater, Flor-
ida).
46. See infra Section VI.D.
47. See, e.g., Devon W. Carbado, Blue-on-Black Violence: A Provisional Model of Some of the Causes,
G
EO. L.J. , - ().
48. See MICHAEL AVERY ET AL., POLICE MISCONDUCT LAW AND LITIGATION : (d ed. )
(describing just how dicult it is for plaintis to make out a claim for municipal liability
based on bad hiring).
49. In some jurisdictions, for example, payments come from the general treasury and the agency
is never held accountable. See Joanna C. Schwartz, How Governments Pay: Lawsuits, Budgets,
and Police Reform, UCLA
L. REV. , ().
the wandering officer
ficer. Part V examines whether we can predict which wandering ocers are like-
liest to fail. Part VI considers potential causal mechanisms for the wandering-
ocer phenomenon and corresponding reforms.
i. the law-enforcement labor market
Every year, over fieen thousand individual law-enforcement agencies,
spread across fiy states, hire thousands of ocers.
50
Because of this segmenta-
tion, it is challenging to oer a comprehensive description of the law-enforce-
ment labor market or the features of the system that influence whether a local
agency hires a wandering ocer. In this Part, however, we sketch out general
patterns in the labor market across the states and oer details on Florida—the
site of the current study—as an illustrative example.
A. Hiring
The vast majority of law-enforcement ocers work for county or municipal
agencies; a small number work directly for the state.
51
In nearly every state, to
become a law-enforcement ocer at any level, an applicant must first obtain cer-
tification—essentially an occupational license—from a state-level licensing en-
tity.
52
In most states, this body is called the Peace Ocer Standards and Training
(POST) Board.
53
Certification procedures vary widely from state to state.
54
In Florida, the certifying entity is the Criminal Justice Standards and Train-
ing Commission (CJSTC), which is part of the Florida Department of Law En-
forcement (FDLE).
55
To obtain certification in the state, candidates must clear a
50. BRIAN A. REAVES, U.S. DEP’T OF JUSTICE, BUREAU OF JUSTICE STATISTICS, LOCAL POLICE DE-
PARTMENTS
, : PERSONNEL, POLICIES, AND PRACTICES tbl. ().
51. Id.
52. See MATTHEW J. HICKMAN, POST AGENCY CERTIFICATION PRACTICES, , at () (re-
porting that Hawaii does not have a POST board and the District of Columbia’s POST board
does not certify ocers). On July , , Hawaii enacted legislation to create a POST board,
which was scheduled to finalize its standards and certification process by July , . See
Haw. Sess. Laws .
53. See Roger Goldman & Steven Puro, Decertification of Police: An Alternative to Traditional Rem-
edies for Police Misconduct, H
ASTINGS CONST. L.Q. , - ().
54. See Roger Goldman, Importance of State Law in Police Reform, ST. LOUIS U. L.J. ,
(); Goldman & Puro, supra note , at -.
55. FLA. STAT. ., .() ().
the yale law journal :
basic abilities test, graduate the police academy, and then pass a written certifi-
cation examination.
56
They must also meet certain minimum qualifications re-
garding age, citizenship, and education.
57
Once certified, ocers must undergo
continuing training and education to maintain their certification.
58
State law generally regulates the process by which agencies hire ocers. In
Florida, local agencies must conduct a background investigation and gather doc-
umentation to prove compliance with the statewide minimum qualifications.
59
State law specifies that background investigations “should include information
setting forth the facts and reasons for any of the applicant’s previous separations
from private or public employment or appointment, as the applicant under-
stands them.”
60
Implementing regulations require that local agencies “ver-
ify . . . [p]rior criminal justice employments of the applicant and the facts and
reasons for any prior separations of employment”
61
by “[o]btain[ing] previous
employment data from prior employers.”
62
Local agencies are expected to con-
tact CJSTC to confirm prior employment and discipline.
63
As part of the background investigation in Florida, the hiring agency must
confirm that the candidate has “good moral character.”
64
Under Florida regula-
tions, moral-character violations can include committing any felony or certain
misdemeanors (regardless of criminal prosecution), using excessive force, mis-
using an ocial position to secure a privilege or benefit, participating in sexual
conduct while on duty, engaging in sexual harassment, making false statements
during the job application process, subverting training and testing processes,
and making false statements in a court proceeding.
65
Regulations also provide
that CJSTC is available to assist local agencies in examining moral character:
56. Id. .()-(); id. . (certification examination); id. . (police academy);
id. . (basic abilities test).
57. Id. ..
58. Id. .(), ..
59. Id. .()-(); FLA. ADMIN. CODE ANN. r. B-.() () (documentation); id. r.
B-. (background investigation).
60. FLA. STAT. .().
61. FLA. ADMIN. CODE ANN. r. B-.()(a).
62. Id. r. B-.()(a).
63. See Employment Background Investigative Report, No. CJSTC-, https://www.fdle
.state.fl.us/CJSTC/Documents/Rules-Forms/WordDoc/CJSTC------TR.aspx
[https://perma.cc/LGJ-VXE], cited in F
LA. ADMIN. CODE ANN. r. B-.()(a)().
64. See FLA. STAT. .(); FLA. ADMIN. CODE ANN. r. B-.()(d); see also id. r. B-
.().
65. FLA. ADMIN. CODE ANN. r. B-.().
the wandering officer
upon request, the CJSTC “shall evaluate the qualification of an applicant to de-
termine compliance with ‘good moral character’ pursuant to this rule section.”
66
The CJSTC’s assistance focuses on the applicant’s criminal history, especially
out-of-state or federal court records.
67
Within the constraints set forth by state law, local agencies have fairly broad
discretion over hiring. Such discretion is not absolute, however, as it is oen
subject to civil service requirements and sometimes to provisions of a collective-
bargaining agreement with a police ocers’ union.
68
To facilitate the hiring pro-
cess, most agencies designate certain hiring prerequisites, such as a minimum
age or education level, and a set of screening exams, such as a physical fitness or
driving test or a polygraph examination.
69
Only candidates who satisfy the pre-
requisites and pass the exams are eligible to be hired. Local agencies may aug-
ment, but not diminish, state-law hiring prerequisites.
70
The same is true for
continuing education and training.
71
B. Discipline
Each local agency also administers its own disciplinary process for ocers
who commit crimes or violate agency policy. As with hiring, the agency’s author-
ity over discipline may be circumscribed by civil-service laws or provisions of a
collective-bargaining agreement. Collective-bargaining agreements frequently
provide for arbitration of disciplinary decisions, including termination. Arbitra-
tors commonly order agencies to reinstate terminated ocers.
72
66. Id. r. B-.().
67. Email from Terry Baker, Training & Research Manager, Fla. Dep’t of Law Enf’t, to John Rap-
paport (Aug. , , : AM CDT) (on file with John Rappaport).
68. See SAMUEL WALKER & CHARLES M. KATZ, THE POLICE IN AMERICA -, (th ed. ).
69. See id. at -, -.
70. FLA. STAT. . (). Florida law, for example, requires ocers to be at least nineteen
years old, id. .(),
but a local agency is free to raise the minimum age to twenty-one.
71. Id. ..
72. See Mark Iris, Unbinding Binding Arbitration of Police Discipline: The Public Policy Exception,
V
A. J. CRIM. L. (); Stephen Rushin, Police Disciplinary Appeals, U. PA. L. REV.
(); Kimbriell Kelly et al., Fired/Rehired: Police Chiefs Are Oen Forced to Put Ocers
Fired for Misconduct Back on the Streets, W
ASH. POST (Aug. , ), https://
www.washingtonpost.com/graphics//investigations/police-fired-rehired [https://
perma.cc/EQZ-AB].
the yale law journal :
In forty-five states, the government entity responsible for certifying ocers
also has the power to decertify upon certain conditions.
73
Ocers who have been
decertified are prohibited from working in law enforcement anywhere in the
state. As with certification, the criteria for decertification vary widely among the
states. All states with decertification authority, for example, can decertify for fel-
ony convictions, but only can decertify for failure to meet training or qual-
ification requirements, for general misconduct, for termination for
cause, and for any misdemeanor conviction.
74
Like most of its counterparts,
the CJSTC in Florida has the authority to decertify Florida ocers.
75
Decertifi-
cation can happen when an ocer has committed a felony or a misdemeanor
involving dishonesty (again, regardless of criminal prosecution) or fails to main-
tain good moral character.
76
Note that this standard, detailed above, covers only
fairly egregious types of misconduct.
The CJSTC learns about potentially disqualifying activity through several
channels, but two are particularly important. First, local agencies are required to
notify the CJSTC whenever an ocer separates from employment, “setting forth
in detail the facts and reasons for such separation.”
77
Second, local agencies must
conduct an internal investigation when they have cause to suspect that an ocer
has committed a disqualifying crime or moral character violation.
78
If the
agency’s suspicion is substantiated, the agency must notify the CJSTC.
79
Florida
is one of the more active states in decertifying ocers even though the substan-
tive scope of its decertification authority is not the broadest.
80
73. See HICKMAN, supra note , at (reporting that forty-four states allow decertification of of-
ficers). Aer Hickman wrote, in October , the responsible agency in New York promul-
gated regulations permitting it to decertify ocers. See N.Y.
COMP. CODES R. & REGS. tit. ,
. (). New Jersey does not have an agency that decertifies ocers. Certain criminal
convictions, however, can trigger “forfeiture of oce” by court order, which can, in some
cases, entail permanent disqualification from holding any public oce. See N.J.
STAT. ANN.
C:- (West ); State v. Hupka, A.d , - (N.J. ).
74. HICKMAN, supra note , at . These figures do not cover New York, which adopted decertifi-
cation regulations aer the report was written. See supra note .
75. FLA. STAT. .().
76. Id. .()-(); FLA. ADMIN. CODE ANN. r. B-. ().
77. FLA. STAT. .(); FLA. ADMIN. CODE ANN. r. B-.().
78. FLA. STAT. .(); FLA. ADMIN. CODE ANN. r. B-.().
79. FLA. STAT. .(); FLA. ADMIN. CODE ANN. r. B-.()(b).
80. While Florida is sometimes identified as the second-highest state (aer Georgia) by number
of decertifications, see H
ICKMAN, supra note , at , that observation misses a few key points.
First, Florida is the third-largest state by population and therefore has more ocers than most
other states. Second, most ocers decertified in Florida are corrections ocers, not the law-
enforcement ocers on whom we focus here. In , for example, Florida decertified law-
the wandering officer
Given all of these regulations, how do wandering ocers still manage to find
work? For starters, local agencies do not always conduct thorough background
investigations before hiring.
81
Even when they do, past employers are not always
forthcoming and sometimes conceal the real reasons for an ocer’s separation.
Anecdotal evidence suggests that ocers who commit misconduct are oen al-
lowed to resign, with a guaranteed positive work reference, in exchange for for-
going legal action.
82
Similarly, local agencies do not always notify their state
POST boards about ocer misconduct. Even setting aside cases in which local
agencies disregard mandatory disclosure obligations,
83
reporting to POST is
wholly voluntary in most states.
84
Agencies are reportedly reluctant to disclose
negative employment information—either to other local agencies or state POST
boards—for fear of being sued for defamation.
85
Even more important, as men-
enforcement ocers at a rate of . decertifications per , ocers, which made it the nine-
teenth-most-frequent decertifier per ocer in the country. See Loren T. Atherley & Matthew
J. Hickman, Ocer Decertification and the National Decertification Index, P
OLICE Q. , -
tbl. (). Still, Florida does decertify ocers more frequently than other big states such
as California, Texas, Pennsylvania, North Carolina, Illinois, and Ohio. For examples of states
with apparently broader decertification authority than Florida, see S.D.
CODIFIED LAWS -
-() (), which permits decertification for ocers who “have been discharged from
employment for cause” or “have engaged in conduct unbecoming of a law enforcement of-
ficer”; and W
IS. STAT. ANN. .()(cm) (West ), which authorizes the POST board
to “[d]ecertify law enforcement . . . ocers who terminate employment or are terminated.”
81. See, e.g., Goldman & Puro, supra note , at ; Cohen, supra note ; Dewan & Oppel, supra
note ; Williams, supra note .
82. See, e.g., Goldman, supra note , at ; Cara E. Rabe-Hemp & Jeremy Braithwaite, An Ex-
ploration of Recidivism and the Ocer Shue in Police Sexual Violence, P
OLICE Q. ,
(); Williams, supra note . In Florida, specifically, see Anthony Cormier & Matthew
Doig, Police Agencies Undermine System, H
ERALD-TRIB. (Sarasota, Fla.) (Dec. , , :
AM), http://www.heraldtribune.com/news//special-report-police-agencies
-undermine-system [https://perma.cc/QFA-QEC].
83. See Cormier & Doig, supra note .
84. See HICKMAN, supra note , at .
85. See, e.g., Goldman & Puro, supra note , at ; Steven Puro et al., Police Decertification:
Changing Patterns Among the States, 1985-1995, P
OLICING: INT’L J. POLICE STRATEGIES &
MGMT. , - (); see also J. Hoult Verkeke, Legal Regulation of Employment Reference
Practices, U.
CHI. L. REV. , () (“Providing such negative information creates a
risk of defamation liability while oering few clear benefits to the referring employer. Indeed,
the available empirical evidence suggests that former employers are less likely to reveal em-
ployee misconduct than any other information about the employee.”). Florida law attempts
to ameliorate this and several of the other problems mentioned. See, e.g., F
LA. STAT.
.() () (requiring background checks); id. .()(b)-()(a) (requiring
prior employers to disclose disciplinary history and reasons for separation); id. .()
(providing immunity for disclosure of employment information to a subsequent hiring
the yale law journal :
tioned earlier, many states define the scope of POST-reportable conduct nar-
rowly—twenty states, for example, require a criminal conviction before an ocer
can be decertified.
86
In other words, not all “police misconduct” must be re-
ported even in mandatory-reporting states. In addition, local agencies some-
times learn about prior misconduct and hire the ocers anyway.
87
Ocer mobility across state lines introduces yet another layer of complexity.
A significant problem with state-by-state certification is that an ocer decerti-
fied in one state can move across state lines and obtain certification, and then
employment, in another. In an eort to address this problem, the International
Association of Directors of Law Enforcement Standards and Training con-
structed a national database called the National Decertification Index (NDI).
88
State POST boards are encouraged to enter their decertification decisions into
the database. When a decertified ocer attempts to find employment in another
state, that state’s POST board—or, in some cases, the local hiring agency—can
query the database and review the prior decertification record.
Unfortunately, the NDI is far from watertight. As mentioned, five states plus
the District of Columbia—which collectively employ a significant share of all
law-enforcement ocers nationwide—have no decertification authority.
89
Among the majority of states that do decertify ocers, reporting to the NDI is
voluntary.
90
In , only thirty states contributed to the database; by , that
number rose to thirty-eight.
91
On the back end, only local agencies have per-
mission to query the NDI directly when hiring. The rest must rely on their state
POST boards, only twenty-eight of which say they “always” or “frequently”
agency); id. .() (providing immunity for disclosure to CJSTC). It is unclear how
eective these provisions are and, in any event, many states have no analogs.
86. Fisher, supra note ; Merchant et al., supra note .
87. See HICKMAN, supra note , at (stating that “four POSTs reported that law enforcement
agencies in their state have hired individuals as ocers who had been decertified in another
state”). Consider Tom Coleman as well.
88. See Raymond A. Franklin et al., 2009 Survey of POST Agencies Regarding Certification Practices,
N
AT’L CRIM. JUST. REFERENCE SERV. - (), https://www.ncjrs.gov/pdles/nij
/.pdf [https://perma.cc/BJT-RK].
89. See Goldman, supra note , at ; sources cited supra note .
90. Roger L. Goldman, State Revocation of Law Enforcement Ocers’ Licenses and Federal Criminal
Prosecution: An Opportunity for Cooperative Federalism, S
T. LOUIS U. PUB. L. REV. ,
(); Merchant et al., supra note .
91. HICKMAN, supra note , at .
the wandering officer
query the NDI.
92
In , Florida reported that it “occasionally” queries the na-
tional database.
93
The confluence of all these legal and institutional forces is thought to channel
wandering ocers toward small, understaed, and resource-strapped agen-
cies.
94
Budget constraints impede thorough background checks. They also make
wandering ocers, who may be prepared to settle for modest salaries and more
limited opportunities for professional advancement, more appealing—especially
where agencies must otherwise foot the bill to put rookie hires through the police
academy. And experienced wandering ocers who are already trained and certi-
fied can hit the streets immediately.
92. Id. at .
93. Franklin et al., supra note , at .
94. For versions of this narrative, see, for example, Atherley & Hickman, supra note , at
(“[D]ismissal isn’t always the final word on the matter. Ocers may be rehired by another
jurisdiction, in which case the new jurisdiction inherits another jurisdiction’s problem. This
can be a conscious decision by the hiring agency, especially in small jurisdictions where finan-
cial resources are limited and lateral ocers are simply scarce.” (citation omitted)); Bell, supra
note , at (“[T]he prevalence of very small departments in close proximity to each other
increases the likelihood that an ocer fired from one jurisdiction for serious reasons could
find work as an ocer in another. Poor communities are more likely to hire ‘gypsy cops,’
ocers with spotty work histories who have been fired elsewhere, because their resource con-
straints make it more dicult for them to discriminate between good and bad ocers.”);
Goldman, supra note , at , , which describes the pressures faced by small departments
that lead them to hire previously terminated ocers; Goldman & Puro, supra note , at
(“Although it might seem unusual for a police department to hire an ocer with a past record
of misconduct, the second department is usually located in a poor community that cannot
aord to pay high salaries to its police.”); Shockey-Eckles, supra note , at (“These mu-
nicipalities, although well known for high crime rates and excessive violence, typically oer
low pay and few benefits to their ocers. Hence, they are the very communities willing to
hire gypsy cops when other departments with more resources are unwilling to do so.”); Chil-
dress, supra note , which notes that “some departments still hire [wandering] ocers, par-
ticularly those that are smaller and strapped for cash”; Cormier & Doig, supra note (“Vet-
erans in trouble oen find second chances by heading down the career ladder, to smaller police
forces in need of experience.”); Dill, supra note , which describes eorts to remedy a state
shortage of police ocers “while trying to avoid problem ocers who bounce from depart-
ment to department”; Toner & Rutecki, supra note , which reports that “poorer communi-
ties” in the Chicago suburbs “are also places where ocers with troubled histories and records
of multiple shootings are oen employed”; Williams, supra note (“[S]maller departments
and those that lack sucient funding or are understaed are most likely to hire applicants
with problematic pasts if they have completed state-mandated training, which allows depart-
ments to avoid the cost of sending them to the police academy.”); and Yoder, supra note
(“Even if a background check turns up past rogue behavior, a small department may go ahead
anyway. Such agencies usually are in poor communities that can’t aord high salaries.”).
the yale law journal :
ii. literature review
At least two academic literatures provide helpful background on the wander-
ing-ocer phenomenon. First, a number of studies, mostly in criminology, have
examined the correlates of police misconduct. Second, a large literature in labor
economics describes the dynamics of labor markets, largely for professions and
industries other than policing. We summarize each literature in turn.
A. Correlates of Police Misconduct
Empirical research directly examining law-enforcement hiring and separa-
tion has been fairly limited. Perhaps the most pertinent study concerns the New
York City Police Department (NYPD), the nation’s largest law-enforcement
agency. Criminologists Robert Kane and Michael White examined all involun-
tary separations in the NYPD between and . They identified , of-
ficers who were separated for so-called “career-ending misconduct” during that
period—roughly of the , individuals the NYPD had employed.
95
They
then compared these ocers with randomly selected members of their respective
police academy classes.
96
Using multivariate analyses, Kane and White identified dierences between
the study and comparison groups that served as both risk and protective factors
for misconduct. In particular, black ocers were significantly more likely than
white ocers to be terminated for misconduct.
97
Prior criminal history, docu-
mented problems in prior jobs, civilian complaints, and assignment to busy pa-
trols also significantly predicted misconduct.
98
Ocers with associate’s or bach-
elor’s degrees, in contrast, were less likely to be fired for misconduct, as were
ocers who were older when hired or who were married.
99
Kane and White
concluded that “police departments should continue to invest heavily in pre-em-
ployment screening processes that exclude people who have demonstrated rec-
ords of criminal involvement and employee disciplinary problems” and embrace
95. KANE & WHITE, supra note , at .
96. See id. at -.
97. More precisely, black ocers were more likely to be fired for two out of three types of mis-
conduct. Initially, the same was also true for Hispanic and Asian ocers, but over time, their
separation rates converged with that for white ocers. See id. at .
98. See id. at -, .
99. See id. at -.
the wandering officer
“racial/ethnic diversity and post-secondary educational requirements.”
100
In-
formative as it is, Kane and White’s study was set entirely within a single law-
enforcement agency and does not speak to the lateral movement of ocers
among agencies, our primary interest in this Article.
Kane and White’s findings are largely consonant with the broader literature
examining ocer-level correlates of police misconduct. Some additional re-
search has also found, for example, that past misconduct predicts future prob-
lems.
101
Unlike Kane and White’s study, much of the research focuses specifically
on ocer use of force. Studies find that younger ocers tend to use force more
oen.
102
So do less experienced ocers,
103
although that may be precisely be-
cause they are younger.
104
Research on female ocers is mixed. Studies have
found that female ocers use less force than male ocers in arrest situations
105
and are less likely to shoot suspects
106
but use similar levels of force in general
citizen encounters.
107
100. Id. at .
101. See, e.g., Samuel Carton et al., Identifying Police Ocers at Risk of Adverse Events, PROC.
ND ACM SIGKDD INT’L CONF. ON KNOWLEDGE DISCOVERY & DATA MINING ,
(“[O]cers who are routinely found to have been engaged in an adverse event are likely to
engage in another such event in the future.”); James P. McElvain & Augustine J. Kposowa,
Police Ocer Characteristics and the Likelihood of Using Deadly Force, C
RIM. JUST. & BEHAV.
, () (“[P]revious history of shootings was a very strong predictor of future shoot-
ings.”); Kyle Rozema & Max Schanzenbach, Good Cop, Bad Cop: Using Civilian Allegations to
Predict Police Misconduct, A
M. ECON. J.: ECON. POL’Y , () (finding that past civil-
ian allegations predict future misconduct).
102. See, e.g., JOEL H. GARNER & CHRISTOPHER D. MAXWELL, UNDERSTANDING THE PREVALENCE
AND
SEVERITY OF FORCE USED BY AND AGAINST THE POLICE (); Steven G. Brandl et al.,
Who Are the Complaint-Prone Ocers? An Examination of the Relationship Between Police Ocers’
Attributes, Arrest Activity, Assignment, and Citizens’ Complaints About Excessive Force, J.
CRIM.
JUST. , (); Christopher Chapman, Use of Force in Minority Communities Is Related
to Police Education, Age, Experience, and Ethnicity, P
OLICE PRAC. & RES. , ().
103. See, e.g., Eugene A. Paoline, III & William Terrill, Police Education, Experience, and the Use of
Force, C
RIM. JUST. & BEHAV. , (); William Terrill & Stephen D. Mastrofski,
Situational and Ocer-Based Determinants of Police Coercion, J
UST. Q. , - ().
104. See Chapman, supra note , at (finding that, controlling for age, less experienced ocers
use less force).
105. See GARNER & MAXWELL, supra note ; Amie M. Schuck & Cara Rabe-Hemp, Women Police:
The Use of Force by and Against Female Ocers, W
OMEN & CRIM. JUST. ().
106. See McElvain & Kposowa, supra note , at .
107. See Eugene A. Paoline, III & William Terrill, Women Police Ocers and the Use of Coercion,
W
OMEN & CRIM. JUST. , - () (“[B]oth males and females choose not to invoke
their coercive authority rather similarly (i.e., of the female encounters resulted in no co-
ercion versus for males).”).
the yale law journal :
Education, too, has received sustained attention. Studies have found that of-
ficers with more education use less force
108
and are subject to fewer disciplinary
allegations and founded complaints.
109
In tension with Kane and White’s rec-
ommendation, however, agency-level studies have not found that minimum ed-
ucation requirements reduce misconduct or use of force.
110
One possible explana-
tion is that educational requirements shrink the pool of eligible candidates,
excluding otherwise-promising individuals.
111
Empirical scholars have also closely examined the relationship between cer-
tain hiring requirements and police misconduct. Perhaps the largest body of re-
search examines the capacity of psychological exams to predict ocer perfor-
mance and, in particular, to identify candidates likely to have disciplinary
problems. Many studies find that personality profiles predict performance,
112
108. See, e.g., McElvain & Kposowa, supra note , at ; Jason Rydberg & William Terrill, The
Eect of Higher Education on Police Behavior, P
OLICE Q. , (); Terrill & Mastrofski,
supra note , at , . But see Brandl et al., supra note , at (“None of the analyses
conducted here would lead one to believe that ocers’ race or level of education played a role
in the receipt of excessive use of force complaints.”).
109. See, e.g., Victor E. Kappeler et al., Police Ocer Higher Education, Citizen Complaints and De-
partmental Rule Violations, A
M. J. POLICE , () (“Although ocers with college de-
grees had fewer citizen-initiated complaints and fewer founded complaints for rudeness, they
did not have significantly fewer department-generated complaints for violations of agency
rules and procedures.”); Kim Michelle Lersch & Linda L. Kunzman, Misconduct Allegations
and Higher Education in a Southern Sheri’s Department, A
M. J. CRIM. JUST. , ().
But see Donald M. Truxillo et al., College Education and Police Job Performance: A Ten-Year Study,
P
UB. PERSONNEL MGMT. , () (reporting that police ocers’ education levels
had “an inconsistent relationship with measures of disciplinary action”).
110. See, e.g., David Eitle et al., The Eect of Organizational and Environmental Factors on Police Mis-
conduct, P
OLICE Q. , () (finding that “neither field training nor educational
standards had a statistically discernible association with” police misconduct); Dale W. Willits
& Jerey S. Nowacki, Police Organisation and Deadly Force: An Examination of Variation Across
Large and Small Cities, P
OLICING & SOC’Y , () (reporting that college requirements
and training hours do not have a statistically significant relationship to deadly force inci-
dents).
111. See, e.g., Lisa Kay Decker & Robert G. Huckabee, Raising the Age and Education Requirements
for Police Ocers: Will Too Many Women and Minority Candidates Be Excluded?, P
OLICING:
INT’L J. POLICE STRATEGIES & MGMT. , () (“Not surprisingly, raising the educa-
tional requirements for sworn police applicants to require a four-year college degree would
eliminate a large number of the traditionally successful police applicants.”).
112. See, e.g., Ryan M. Roberts et al., Predicting Postprobationary Job Performance of Police Ocers
Using CPI and MMPI-2-RF Test Data Obtained During Preemployment Psychological Screening,
J.
PERSONALITY ASSESSMENT () (finding correlations between prehire psychological
test results in California and Minnesota and supervisor ratings for police ocers); Anthony
M. Tarescavage et al., Criterion Validity and Practical Utility of the Minnesota Multiphasic Person-
ality Inventory–2–Restructured Form (MMPI–2–RF) in Assessments of Police Ocer Candidates,
the wandering officer
though a fair number reach the contrary conclusion.
113
Numerous studies have
also identified particular psychological profiles that predict whether someone
will be a “problem ocer.”
114
As with educational requirements, however, there
is little evidence that psychological exams improve agency-level outcomes such
as civilian complaints or deaths.
115
The empirical evidence on the eects of training is particularly conflicted.
Various studies have found that training is negatively,
116
positively,
117
or simply
not
118
associated with adverse outcomes such as the use of force, civilian deaths,
civilian complaints, and general misconduct. One plausible explanation is that
the quality of training may matter more than the quantity. Researchers in one
J. PERSONALITY ASSESSMENT , () (finding correlations between prehire psycho-
logical test results in Minnesota and supervisor ratings for police ocers).
113. See, e.g., Suzanne Daniels & Emily King, The Predictive Validity of MMPI-2 Content Scales for
Small-Town Police Ocer Performance, J.
POLICE & CRIM. PSYCHOL. , (); Beth A.
Sanders, Using Personality Traits to Predict Police Ocer Performance, P
OLICING: INT’L J. PO-
LICE
STRATEGIES & MGMT. , (). These studies oen measure ocer performance
using ratings given by supervisors, an imperfect outcome measure. See Steven Falkenberg et
al., An Examination of the Constructs Underlying Police Performance Appraisals, J.
CRIM. JUST.
, (). Additional studies using more objective outcomes—such as internal investi-
gations, involuntary termination, turnover, and disciplinary complaints—are similarly mixed.
Compare, e.g., Martin Sellbom et al., Identifying MMPI-2 Predictors of Police Ocer Integrity and
Misconduct, C
RIM. JUST. & BEHAV. , (), with, e.g., Jose M. Cortina et al., The
“Big Five” Personality Factors in the IPI and MMPI: Predictors of Police Performance, P
ERSON-
NEL
PSYCHOL. , ().
114. See, e.g., Michael J. Cuttler & Paul M. Muchinsky, Prediction of Law Enforcement Training Per-
formance and Dysfunctional Job Performance with General Mental Ability, Personality, and Life His-
tory Variables, C
RIM. JUST. & BEHAV. , (); Charles D. Sarchione et al., Prediction of
Dysfunctional Job Behaviors Among Law Enforcement Ocers, J.
APPLIED PSYCHOL. ,
().
115. See, e.g., Liqun Cao et al., A Test of Lundman’s Organizational Product Thesis with Data on Citizen
Complaints, P
OLICING: INT’L J. POLICE STRATEGIES & MGMT. , (); Brad W.
Smith, Structural and Organizational Predictors of Homicide by Police, P
OLICING: INT’L J. PO-
LICE
STRATEGIES & MGMT. , ().
116. See, e.g., EMILY G. OWENS ET AL., PROMOTING OFFICER INTEGRITY THROUGH EARLY ENGAGE-
MENTS AND
PROCEDURAL JUSTICE IN THE SEATTLE POLICE DEPARTMENT (); Cao et al., su-
pra note , at .
117. See, e.g., William C. Bailey, Less-Than-Lethal Weapons and Police-Citizen Killings in U.S. Urban
Areas, C
RIME & DELINQ. , (); Hoon Lee et al., An Examination of Police Use of
Force Utilizing Police Training and Neighborhood Contextual Factors: A Multilevel Analysis, P
O-
LICING
: INT’L J. OF POLICE STRATEGIES & MGMT. ().
118. See, e.g., Lee et al., supra note ; Willits & Nowacki, supra note .
the yale law journal :
study, for example, found that roughly a quarter of the variation in ocers’ com-
plaint rates was attributable to the identity of their field training ocers, sug-
gesting that qualitatively bad training may hurt more than it helps.
119
B. Labor Economics
Although it does not focus on policing, specifically, a large body of research
in economics examines the dynamics of labor markets. A number of papers, for
example, study the costs to employers of employee turnover or “churn.” These
costs include the expense of temporarily covering the departing employee’s du-
ties, such as through overtime for other workers; replacement costs, such as
screening new applicants; training costs, including on-the-job training and uni-
forms; lost productivity for the departing employee, who may spend his last days
writing exit memos or laboring with reduced morale; damaged morale for other
workers; and lost institutional knowledge.
120
The costs of churn appear to vary
by region and industry
121
and may, in some settings, be partially or wholly oset
by the benefits of hiring new workers.
122
Nevertheless, one recent literature re-
view concludes that, on average, the cost of replacing an employee is roughly
one-fih of the employee’s salary (excluding the very highest-paid jobs).
123
High turnover costs may, therefore, discourage law-enforcement agencies from
hiring and firing wandering ocers.
A related literature explores the employee-side costs of job separation and
unemployment. One consistent finding concerns the “unemployment scar”: dis-
placed and nonemployed workers suer long-term earnings losses.
124
Such
119. See Ryan M. Getty et al., How Far from the Tree Does the Apple Fall? Field Training Ocers, Their
Trainees, and Allegations of Misconduct, C
RIME & DELINQ. , ().
120. See Heather Boushey & Sarah Jane Glynn, There Are Significant Business Costs to Replacing Em-
ployees, C
TR. FOR AM. PROGRESS (Nov. , ), https://cdn.americanprogress.org
/wp-content/uploads////CostofTurnover.pdf [https://perma.cc/DR
-YRCK].
121. See id. at -.
122. See Zeynep Ton & Robert S. Huckman, Managing the Impact of Employee Turnover on Perfor-
mance: The Role of Process Conformance, O
RG. SCI. , () (mentioning “improvement
of matches between employees and firms over time” and increased eort by replacement em-
ployees).
123. Boushey & Glynn, supra note , at .
124. See, e.g., Louis S. Jacobson et al., Earnings Losses of Displaced Workers, AM. ECON. REV. ,
() (finding that high-tenure workers who separate from distressed firms suer
longer-term earnings losses averaging per year). See generally William J. Carrington &
Bruce Fallick, Why Do Earnings Fall with Job Displacement?,
INDUS. REL. () (re-
viewing the literature).
the wandering officer
workers, for example, may leave the work force or move to firms that pay lower
wages.
125
Unsurprisingly, earnings losses are greater for workers whom firms
exercise discretion to fire than for workers displaced by, say, a plant closing—the
former reveals important information about worker quality.
126
Fired workers
also experience longer unemployment spells.
127
Based on these findings from the
labor economics literature, we expect that wandering ocers will take longer to
land new jobs than other ocers and that they will tend to move to less desirable
agencies.
The concept of “wandering workers” is not itself novel. Journalists have, for
example, penned numerous stories about clergy or teachers who, following mis-
conduct, leave one parish or school and find work in another.
128
As far as we can
tell, however, the labor-economics literature has not focused on most of the core
questions that concern us here: which firms hire displaced workers and how do
those workers fare upon reemployment? The closest study of which we are aware
examines the market for financial advisers.
129
The authors find that seven per-
cent of working financial advisers have misconduct records, and roughly one-
third of these are repeat oenders.
130
Approximately half of these advisers lose
their jobs aer misconduct, yet forty-four percent of them are rehired by other
firms within a year—they are, in our terminology, “wandering financial advis-
ers.”
131
This is true even though advisers with prior misconduct are five times as
likely as the average adviser to commit misconduct in the future.
132
Still, advisers
125. See, e.g., Fatih Guvenen et al., Heterogeneous Scarring Eects of Full-Year Nonemployment,
A
M. ECON. REV.: PAPERS & PROCEEDINGS , () (finding heterogeneous scarring ef-
fects from one year’s nonemployment, resulting primarily from a higher incidence of future
nonemployment rather than lower earnings conditional on working); Kristiina Huttunen et
al., How Destructive Is Creative Destruction? Eects of Job Loss on Job Mobility, Withdrawal and
Income, J.
EURO. ECON. ASS’N , () (finding that displacement increases the prob-
ability of leaving the labor force by and, for workers who remain, moderately depresses
earnings due to movement between firms).
126. See Robert Gibbons & Lawrence F. Katz, Layos and Lemons, J. LAB. ECON. , ().
127. Id.
128. See, e.g., Tara Isabella Burton, Scathing Report Reveals 300 Pennsylvania Catholic Priests Sexually
Abused over 1,000 Children, V
OX (Oct. , ), https://www.vox.com////
/catholic-sex-abuse-priest-crisis-pennsylvania-report [https://perma.cc/GEW-HZG];
Martha Irvine & Robert Tanner, Sexual Misconduct Plagues US Schools, W
ASH. POST
(Oct. , ), http://www.washingtonpost.com/wp-dyn/content/article///
/AR.html [https://perma.cc/MHD-P].
129. Mark Egan et al., The Market for Financial Adviser Misconduct, J. POL. ECON. ().
130. Id. at , .
131. Id. at .
132. Id. at .
the yale law journal :
who commit misconduct do experience an elevated likelihood of industry exit,
longer gaps between employment stints, and, for those who find new jobs, lower
compensation at smaller, less desirable firms.
133
Observing that some firms em-
ploy substantially more wandering advisers than others and that misconduct is
more common in wealthy, elderly, and less educated counties, the authors hy-
pothesize that “misconduct may be targeted at customers who are potentially less
financially sophisticated.”
134
To our knowledge, there is no prior quantitative empirical work on wander-
ing police ocers. In the following parts, we report the results of the first sys-
tematic exploration of this phenomenon.
Where possible, we examine how the
correlates of misconduct described above interact with wandering-ocer status
and other outcomes of interest.
iii. data
Our primary source of data is the Automated Training Management System
(ATMS), a database constructed and maintained by the FDLE. We supplement
ATMS with a range of other data sources, including an annual survey of Florida
law-enforcement agencies, the United States Census, the Uniform Crime Re-
ports, and others.
A. Automated Training Management System (ATMS)
ATMS compiles information on employment, termination, complaints, dis-
cipline, and demographics for every law-enforcement and corrections ocer
hired in the State of Florida. Our ATMS extract runs up through June .
We rely on three ATMS data sets in particular. First, the “employment” data
set is structured at the ocer-employment level. This means that the same of-
ficer can appear in multiple rows, one for each job he has held. The raw employ-
ment data set has , observations. We drop a substantial number of them.
First, given our focus on wandering law-enforcement ocers, we drop all em-
ployment positions categorized by ATMS as “corrections,” “corrections proba-
tion,” “concurrent” (both law enforcement and corrections), “civilian,” “instruc-
tor,” and “auxiliary.”
135
Doing so removes approximately , rows from the
data. Second, we drop all rows for part-time ocers, focusing only on full-time
133. Id. at -. The finding concerning the gap between employment stints is driven by advisers
who are not rehired; conditional on being rehired, advisers recently disciplined for miscon-
duct find work marginally faster than other advisers. See id. at -.
134. Id. at ; see id. at -.
135. When we include concurrent ocers, the core results of the Article do not change.
the wandering officer
employment. Third, we drop all employment associated with a very small num-
ber of ocers whose full-time employment dates across two or more positions
overlap by more than days. Fourth, we drop all employment stints beginning
aer because we have only six months of data for . Fih, we exclude all
employment stints that ended before due to concerns about whether ATMS
is comprehensive in earlier years.
136
Sixth, we drop all stints that end with oc-
ers “transferring within agency” to other full-time law-enforcement positions
because these are not true separations for our purposes.
137
Before dropping these
stints, however, we assign their start dates to the subsequent employment stints
(to which the ocers transferred) to account for the full length of employment.
Aer applying each of these sample restrictions, the employment data set
contains the , full-time law-enforcement job stints that spanned at least
one day between and ; they correspond to , unique ocers. Most
of our analyses, however, focus on the , of those stints that began in or
aer and that correspond to , unique ocers in unique agencies.
Among those agencies, are police departments, are sheris’ oces, and
are other agencies, which include state-level, university, school, port, and
other kinds of law-enforcement agencies. Table presents descriptive statistics
at the agency-year level. The bottom row indicates that, on average, agencies
were closed for operation in roughly fieen percent of agency-years; we thus
drop those agency-years from the rest of the descriptive statistics reported in the
table.
138
On average, agencies in the data set employed a mean of and a me-
dian of full-time ocers on at least one day in each year. They hired a mean
of . and a median of full-time ocers per year, and experienced a mean of
separations and a median of .
136. Our coverage concerns stem from a substantial increase in the volume of hirings in the years
before , which suggests that the database may not have been capturing all employment
stints during that period. To be clear, although we exclude stints that ended before from
our analytic data set, we still use those stints to determine ocers’ firing histories. To illus-
trate, suppose a law-enforcement ocer was fired from a job in and then found work
again in . For purposes of our analysis, this ocer would have only one row in our data
set—for the employment stint beginning in —which would indicate that the ocer had
been previously fired.
137. In contrast, when an ocer transfers within agency from a full-time law-enforcement posi-
tion to a part-time or non-law-enforcement position, we keep the employment stint and treat
it as a voluntary separation because the subsequent stint does not meet the eligibility criteria
for our sample. In some cases, multiple full-time law-enforcement ocers “transfer within
agency” on the same day to full-time law-enforcement positions in a dierent agency, likely
because the initial agency was absorbed by another agency. We assume that these are not true
separations. We therefore assign the start date and other relevant information from the first
stint to the second and drop the initial stint from the data.
138. We assume an agency is closed in a given year if it has no full-time law-enforcement ocers
employed on any day in that year.
the yale law journal :
TABLE 1.
agency-year-level descriptive statistics, 1988-2016
Mean Median SD Min. Max.
Officers Employed 98.0 27 241.7 1 3,272
Hirings 8.4 4 16.30 0 353
Separations 5.0 2 8.90 0 237
Voluntary Separations 4.2 2 7.20 0 125
Firings 0.78 0 1.65 0 28
Firings for Misconduct 0.54 0 1.18 0 20
Complaints 0.61 0 1.94 0 85
Agency Closed 0.15 0 0.36 0 1
The employment database provides a range of information on each job, in-
cluding agency name, start and end date, and cause of separation. That last var-
iable is our principal variable of interest. A separation can be either voluntary or
involuntary; it is simply the end of an employment stint, regardless of the rea-
son. We refer to involuntary separations as “terminations,” “involuntary termi-
nations,” or, more colloquially, “firings.” In total, the variable measuring cause
of separation has thirty-seven dierent code values. Unfortunately, some of
those values have not been used consistently over time. In consultation with the
FDLE, we have grouped the codes to produce two cause-of-separation measures
that correspond to salient separation categories—voluntary and involuntary sep-
arations, or quitting and getting fired—and minimize inconsistencies in data col-
lection over time.
139
To refine the broad category of involuntary separations, we develop two
measures of firing. Our first, and narrower, measure captures terminations for
“moral character violations” or violations of a local agency’s policy. For conven-
ience, we refer to these terminations as firings for “misconduct.”
140
Agency pol-
icy violations may include things like insubordination or failing to follow orders.
139. For the frequency distributions of these codes among law-enforcement positions beginning
between and and their inclusion in our cause-of-separation measures, see infra Ta-
ble A.
140. There is no legal authority or academic consensus on the definition of “police misconduct.”
See K
ANE & WHITE, supra note , at -.
the wandering officer
Theoretically, they can also include more minor oenses, such as not having
one’s uniform pressed—agency policy manuals are hey tomes. Generally speak-
ing, however, the types of agency policy violations that warrant termination are
serious or represent the culmination of a pattern of misconduct. It is, aer all,
notoriously dicult to fire a police ocer.
141
The agencies we studied experi-
enced an average of . firings for misconduct per year, accounting for roughly
. of all separations.
A second, and broader, measure captures all instances in which ocers are
fired for cause. This measure includes firings for misconduct, but it also includes
terminations for training and performance problems. It does not, however,
count involuntary separations due to downsizing or the dissolution of an agency,
which together account for no more than . of all separations in the data. As
Table shows, the agencies in our data set saw an average of almost . firings
for cause per year during the study period, accounting for roughly . of all
separations. We define the remaining . of the separations as voluntary sep-
arations.
One significant methodological problem in any study of police employment
and misconduct is that ocers who are under investigation are frequently al-
lowed to resign before being terminated involuntarily.
142
Fortunately, at least af-
ter , ATMS tracks ocers who resign “in lieu of separation” or “while being
investigated” for misconduct.
143
We include these separations in our firing
measures.
The vast majority of employment stints are easy to define: they are cleanly
marked by a start and an end date. But there are some edge cases that complicate
that seemingly simple line. Perhaps most important, as mentioned earlier, in
141. See, e.g., Kelly et al., supra note ; Tess Owen, Why It’s Hard to Fire Cops, VICE NEWS (Oct.
, ), https://www.vice.com/en_us/article/bjyxw/why-is-it-so-hard-for-cops-to-get
-fired [https://perma.cc/YD-RPSL]; Mike Riggs, Why Firing a Bad Cop Is Damn Near Im-
possible, R
EASON (Oct. , ), https://reason.com////how-special-rights-for-law
-enforcement-m [https://perma.cc/SKQ-RWC].
142. See, e.g., Jeremy Gorner, 2 Chicago Cops Resign Aer Facing Firing for O-Duty Trac Dispute
That Led to Gunfire, C
HI. TRIB. (Mar. , , : PM), https://www.chicagotribune.com
/news/breaking/ct-met-chicago-cop-trac-dispute-police-board--story.html
[https://perma.cc/VC-WU]; see also Cara E. Trombadore, Police Ocer Sexual Miscon-
duct: An Urgent Call to Action in a Context Disproportionately Threatening Women of Color,
H
ARV. J. RACIAL & ETHNIC JUST. , () (“Thus, an ocer accused of sexual miscon-
duct can resign before an investigation is completed, and then be hired by another department
where he may continue the behavior.”).
143. We suspect that FDLE was already counting these cases under another, more generic firing
code before because we do not observe any bump in the total number of firings and
misconduct-related terminations in that year.
the yale law journal :
some cases a labor arbitrator will reverse a firing decision, forcing the agency to
reinstate the fired ocer—oen months or years later. Unfortunately, there is no
code in the ATMS database that indicates when a fired ocer is reinstated aer
arbitration. Nevertheless, in a small number of cases—roughly of all firings
of full-time law-enforcement ocers from to —we do observe fired
ocers beginning their next employment stint in the same agency that fired
them. Based on communications with FDLE sta, we suspect that many of these
cases represent arbitral reversals.
144
Arbitral reversals pose not only a data challenge but also a conceptual one.
For reinstated ocers, is the initial term of employment—or what we call the
“prefiring stint”—and the period aer reinstatement—the “postfiring stint”—
one employment with a pause in the middle? Or is it two separate employments?
Should the initial firing count as a firing when the department is ultimately
forced to reinstate the ocer? The answers likely vary depending on the research
question we seek to answer. Accordingly, for each of our analyses, we indicate
how we handle ocers who are fired and then rehired by the same agency.
We use our firing measures to construct professional-history variables,
which indicate whether an ocer was fired from his last job or from an earlier
job. In constructing these variables, we include firings not only from law-en-
forcement positions but also from employment stints in corrections positions.
That is, although our subject is law-enforcement ocers, specifically—and we
have dropped corrections ocers from the employment data set—we use the
corrections-related data to check whether the law-enforcement ocers we study
previously worked in corrections positions from which they separated involun-
tarily.
In addition to the employment data set, we use an ATMS data set containing
state-level “moral character” complaints against ocers. Most of these com-
plaints were initiated and investigated by the local agencies that employed the
ocers named. Under Florida law, if an agency has cause to believe that an of-
ficer has committed a moral character violation, it must investigate. If the agency
sustains the allegation, it must submit its findings to the FDLE, which will then
initiate a state-level complaint. The consistency with which local agencies inves-
tigate and report complaints to FDLE likely varies.
145
The FDLE also has the
power to initiate complaints on its own, which are included in the data set. We
use the ATMS complaint data to compute the number of complaints filed against
ocers during each of their employment stints. We do this using the date on
144. See Email from Terry Baker, supra note .
145. See Anthony Cormier & Matthew Doig, Special Report: How Florida’s Problem Ocers
Remain on the Job, H
ERALD-TRIB. (Sarasota, Fla.) (Dec. , , : AM), https://
www.heraldtribune.com/article/LK//News//SH [https://perma.cc
/AW-TJK].
the wandering officer
which complaints were initiated, which will typically succeed the date on which
the alleged misconduct occurred. That said, based on communications with the
FDLE, we suspect that, in most cases, these two dates are close in time. These
moral-character complaints are rare—agencies experienced an average of
roughly . complaints per year. Each complaint in the data set is also tagged
with “oense codes” that indicate the substantive nature of the misconduct al-
leged, and some complaints are associated with multiple oense codes. We use
these codes to identify the subset of complaints that include any allegation of
violent or sexual misconduct or misconduct that implicates the ocer’s integrity.
Finally, ATMS contains an ocer-level database that provides demographic
information including race, gender, age, and education. Ocer race is desig-
nated as white, black, Hispanic, Asian, or other, and we use these terms through-
out. We merge these demographic data with the employment data.
B. Supplemental Data Sources
We supplement the ATMS database with several other data sources to lever-
age additional information on the agencies that employ wandering ocers. To
collect information on agency hiring and training requirements, we obtained
from the FDLE all data from the Criminal Justice Agency Profile (CJAP), an an-
nual survey of all law-enforcement agencies in Florida that has run from to
.
146
We extract from CJAP information on hiring and training requirements
for all municipal police agencies and sheris’ oces in the state. We do not cap-
ture data for these variables for other law-enforcement agencies, such as those
in schools, universities, or ports. In total, unique police departments and
sheris’ oces appear in at least one year of the survey. Some agencies are not
present every year, either because they formed aer or dissolved before
, or because they did not respond to the survey. We create an agency-year
panel data set in which every agency that appears at least once has a row for each
of the twenty years of our study—, rows in total.
With respect to hiring prerequisites, CJAP gathers information on minimum
age, minimum education, prior criminal-justice experience, and tobacco use.
CJAP also collects information on whether each agency requires a driving his-
tory, an in-person interview, a physical fitness test, a polygraph examination, a
psychological examination, a written exam, a swimming test, a vision test, or a
voice-stress analysis. It also records the length of any probationary employment
146. We are grateful to Guangya Liu for her heroic eorts to extract and process the relevant vari-
ables from CJAP.
the yale law journal :
period.
147
To compute a rough estimate of each agency’s overall hiring strin-
gency in a given year, we create a composite measure combining these require-
ments.
148
On average, agencies have a hiring-stringency score of . on our scale.
The average score increased from to , from . to ..
CJAP also collects information on ongoing training requirements. These in-
clude training on chemical agents, defensive tactics, driving, firearms, and first
aid. In addition, CJAP records the length of the training period required of new
ocers under a field training ocer (FTO).
149
As with hiring, we combine the
training requirements to create a composite measure of the stringency of an
agency’s training regimen.
150
On average, agencies have a hiring-stringency
score of .. Agencies’ average composite-training score increased from to
, from . to ..
We also supplement the data with additional sources of information, which
we describe in greater detail below. First, using Google Maps, we geocoded the
longitude and latitude coordinates of law-enforcement agencies to measure the
distance that wandering ocers travel from one job to the next. Second, we ob-
tained agency-level annual crime data from the FBI’s Uniform Crime Reports.
Third, we gathered county- and city-level information on the racial and ethnic
composition of resident populations and unemployment rates from the United
States Census and Bureau of Labor Statistics. Finally, we obtained information
on county- and city-level law-enforcement expenditures from the Florida De-
partment of Financial Services.
C. Limitations
Although our data is rich and reasonably comprehensive, there are important
limitations worth noting. One substantive limitation is that the separation codes
147. Descriptive statistics for these variables are available in Table A.
148. We construct our composite score by giving an agency one point for every hiring requirement.
To maximize the length of our observation period, we do not include hiring variables that
were excluded from one or more years of the survey. This leads us to drop driving history,
voice-stress analysis, and the swimming test. Three variables—age, education, and probation-
ary period—are not binary. We therefore specify a threshold at which to assign an agency a
point for these requirements. Based on the statistics reported in Table A, we assign agencies
a point for requiring ocers to be older than nineteen, to have an associate’s degree or college
credit, and to undergo more than twelve months of probationary employment.
149. Descriptive statistics for these variables are available in Table A.
150. To construct the composite-training score, we give agencies one point for requiring training
in chemical agents, defensive tactics, driving, firearms, or first aid every six months or year.
We give them an additional point for requiring more than twelve months of training under
an FTO.
the wandering officer
we use to construct our cause-of-separation measures (listed in Table A) reveal
only the general reason for each separation, such as a failure to complete training
requirements, budgetary constraints, misconduct, or a voluntary departure.
Within the all-important category of firings for misconduct, we are not able to
identify the specific nature of the misconduct, such as excessive force, embezzle-
ment, substance abuse, and so on. That said, even if we had more specific infor-
mation on the ocial reason for termination, it would remain dicult in many
cases to determine reliably the actual, underlying conduct at issue. As just one
example, NYPD ocer Martin Tisdale shot and killed a woman during a strug-
gle over his firearm, fled the scene, and then disposed of the firearm. The ocial
reason for his termination was recorded as “failure to safeguard a weapon.”
151
Other limitations relate to the geographic scope of our data, all of which are
drawn from the State of Florida. We cannot, for example, observe ocers who
were fired out of state and then obtained law-enforcement work in Florida. That
our data is limited to one state also raises questions about the external validity of
our results—that is, the extent to which our conclusions generalize to other lo-
cations. It is certainly possible that law-enforcement labor markets vary from
state to state in ways that implicate our research questions. Indeed, we note be-
low some reasons to believe that wandering ocers may be relatively more prev-
alent in some other states,
152
except for Connecticut, which bans their hiring al-
together.
153
At the same time, we are unaware of any reason to think that Florida
is idiosyncratic in pertinent respects.
A national study, it bears noting, would not be practicable at this time. Alt-
hough we have not conducted an exhaustive survey, we are unaware of any other
state that collects and makes available the type of data contained in Florida’s
ATMS. There are, moreover, methodological advantages to working within a
single state. For one thing, any state-level covariates, such as state law or other
state characteristics, are held constant, simplifying statistical analysis and reduc-
ing the risk of omitted variable bias. In addition, that a single entity (a state
agency) collects all of the critical data significantly mitigates concerns about data
consistency.
iv. describing the wandering officer
Thousands of law-enforcement ocers begin and end jobs in Florida each
year. Some are hired for the first time; some for the third or fourth. Some sepa-
151. KANE & WHITE, supra note , at -.
152. See infra Section IV.B..
153. See infra Section VI.E.
the yale law journal :
rate voluntarily to retire or change careers; others are fired for grievous miscon-
duct. Section IV.A presents an overview of the law-enforcement labor market in
Florida, describing hiring patterns and then separations. Section IV.B details the
prevalence, characteristics, movement patterns, and behavior of wandering of-
ficers.
A. The Law-Enforcement Labor Market in
Florida
1. H
irings
The law-enforcement labor market in Florida is large. As the black line in
Figure reveals, Florida agencies hired between , and , full-time oc-
ers in most years between and .
154
In general, trends in hiring appear
to track conditions in the wider American economy. The number of ocers hired
each year dropped dramatically during the economic recession in and .
It then began rising again until it peaked in the late s and fell during the
Great Recession, from to . Since then, the number has been steadily
rising.
154. To give a complete picture of the ATMS database over time, all of the data we report in this
subsection include both the pre- and postfiring terms for ocers who were fired and then
rehired by the same agency for their next job. As noted, the postfiring term likely represents
an employment stint resulting from an arbitrator’s decision to reinstate the ocer. See supra
Section III.A.
the wandering officer
FIGURE 1.
total number of law-enforcement officers hired and separated, 1988-2016
Men held the
vast majority of full-time law-enforcement jobs in Florida from
to —roughly . Most jobs also went to white ocers——with
to black ocers and to Hispanic ocers. Educational information is
available for about of the jobs in the employment database. Of those,
were held by ocers with only a high school education. Another were held
by ocers with an associate’s degree and by ocers with a bachelor’s de-
gree. Just were held by ocers with a master’s degree.
155
2. Separations
Turning to separations, Figure shows that, in most years from to ,
about , to , ocers separated from their jobs, whether voluntarily or
involuntarily. Unsurprisingly, the number of separations tracks the number of
hires, rising in most years except during recessions, when fewer jobs are availa-
ble.
155. Although our data identify Asian ocers and ocers with doctoral degrees, we do not report
them here because they are too scarce. We also decline to report figures where ocer charac-
teristics are unknown or where ocer race is coded as “Other.”
the yale law journal :
In Figure , we disaggregate separations by cause.
156
From to , an
average of of separations were involuntary—meaning that the ocer was
fired—while the remaining were voluntary. The proportion of involuntary
separations fell in the s, from roughly to , but has remained rela-
tively stable since then. The same basic pattern characterizes firings for miscon-
duct, specifically.
FIGURE 2.
proportion of separations by cause of separation, 1988-2016
Table b
reaks down by demographic characteristics the proportions of sep-
arations that were firings and firings for misconduct, respectively. The most
striking observation is that black ocers were most likely to be fired, both in
general and for misconduct: of all separations involving black ocers were
firings and were firings for misconduct. Those rates were substantially
higher than the rates for Hispanic ocers—who were fired of the time and
fired for misconduct of the time—and white ocers—who were fired
of the time and fired for misconduct of the time. To be clear, these figures do
156. To give a complete picture of separations in the ATMS database, throughout this subsection
we count all involuntary separations, including those in the pre- and postfiring terms for of-
ficers who were fired and then rehired by the same agency for their next job.
the wandering officer
not necessarily imply any problem with the relative performance of black oc-
ers. Black ocers, for example, may be fired more oen due to discrimination in
the disciplinary process
157
or because they are disproportionately assigned duties
that present elevated opportunities for misconduct.
158
Educa
tional background is also correlated with involuntary termination, at
least within the of employment stints for which education data are availa-
ble. Ocers with only a high school education are most likely to be fired, and
the rate of firing generally decreases with higher educational attainment. Ocers
without a four-year college degree are also most likely to be fired for misconduct.
TABLE 2.
cause of separation by demographic groups, 1988-2016
Demographics n Fired
Fired for
Misconduct
Race
White 69,103
11.7%
8.6%
Black 8,228 21.6% 14.8%
Hispanic 8,323
17.8% 11.7%
Gender
Male 75,946
13.3%
9.7%
Female 11,155 13.4% 7.6%
Education
High School 4,028 16.9% 8.8%
Associate’s 12,728 12.1% 8.5%
Bachelor’s 22,503 9.5% 6.3%
Master’s 3,637 7.2% 4.7%
Overall 87,116 13.3% 9.5%
157. See, e.g., KANE & WHITE, supra note , at , - (discussing possible heightened scrutiny
of black ocers due to “tokenism”); Kate Levine, Discipline and Policing, D
UKE L.J. ,
- ().
158. See KANE & WHITE, supra note , at ; see also James J. Fyfe, Police Use of Deadly Force: Re-
search and Reform, J
UST. Q. , () (“Disparities in on-duty shooting rates were
attributable largely to racial dierences in rank and assignment.”); William A. Geller & Kevin
J. Karales, Shootings of and by Chicago Police: Uncommon Crisis—Part I: Shootings by Chicago
Police, J.
CRIM. L. & CRIMINOLOGY , () (similar).
the yale law journal :
B. The Wandering Ocer
Despite the salience of wandering ocers, we have remarkably little system-
atic data about them. In this Section, we attempt to answer four basic questions
that frequently arise in public discourse about police misconduct. First, how
common are wandering ocers? Second, how easy is it for them to land new
jobs? Third, where do they go? And fourth, are they really a problem, in the sense
that they cause more harms than other ocers to the communities they police?
We take each of these questions in turn.
Before we begin, a brief definitional note. There is no legal or even informal
consensus definition to tell us who, exactly, counts as a wandering ocer. We
therefore adopt an expansive definition: a wandering ocer is someone who was
fired from at least one full-time law-enforcement or corrections position in the
State of Florida and later lands another full-time law-enforcement job in the
state. At times, we also break wandering ocers into two smaller groups—those
who were fired from their last job and those who were fired from a job earlier in
their employment history—because the results for these groups dier in im-
portant respects.
1. How Common Are Wandering Ocers?
In absolute numbers, wandering ocers are fairly common. From to
, an average of roughly , full-time law-enforcement ocers who had
previously been fired, and just under ocers who had been fired for mis-
conduct, were employed by new agencies in any given year.
159
Figure depicts
the number of wandering ocers employed over time. As the dark and light gray
lines show, the number of wandering ocers employed throughout the state in
any given year has been relatively stable over time, with a slight decrease in re-
cent years. The black line also shows that the number of all ocers—divided by
ten to depict all three lines on the same axis—has been increasing steadily over
time. In , just under , full-time ocers had been previously fired and
just over had been fired for misconduct. To be clear, these counts include
only wandering ocers, not all ocers who worked in law enforcement aer hav-
ing been fired. If we include ocers who were rehired by the same agency that
fired them, the counts are even higher: from to , on average, roughly
, and ocers were employed who had previously been fired or fired for
misconduct, respectively.
159. In this Section we exclude from our ocer counts the post-firing stints of ocers who were
fired and then rehired by the same agency (before being hired at any other agency). While
these ocers have previously been fired, they are not wandering ocers because they have
not, as of yet, moved to another agency.
the wandering officer
1717
FIGURE 3.
number of employed officers by professional history, 1988-2016
When viewed in relative terms, the prevalence of wandering ocers seems
more limited. Almost 3% of all ocers employed in any given year were wander-
ing ocers previously fired and just 2% were wandering ocers previously fired
for misconduct. As Figure 4 shows, the relative proportion of wandering ocers
has fallen gradually over time. This is partly because—as we observed Figure 3—
the total number of ocers employed has increased. By 2016, just over 2% of all
ocers employed were wandering ocers who had ever been fired and 1.4%
were wandering ocers who had been fired for misconduct.
FIGURE 4.
percent of all employed officers who are wandering officers, 1988-2016
the yale law journal :
Whether there are many wandering ocers or few, therefore, may be in the
eye of the beholder. In absolute terms, Florida law-enforcement agencies employ
many wanderers: in recent years, roughly , wandering ocers who had pre-
viously been fired and about who had been fired for misconduct. In total,
these ocers likely interact with hundreds of thousands of civilians each year.
160
Y
et when viewed in relative terms, we see that the proportion of wandering of-
ficers is small and decreasing gradually over time.
Still, we suspect that our figures underestimate the actual number of wan-
dering ocers for at least three reasons. First, because our data is limited to Flor-
ida agencies, we are unable to identify wandering ocers who were previously
fired from law-enforcement agencies in other states. Second, a small number of
ocers may successfully obscure their employment history, even within the
Florida market. Some who have been fired may simply lie and get away with it.
Others may have negotiated an apparently “voluntary” separation in exchange
for separating without a legal fight.
161
Third, other states may have more wan-
dering ocers than Florida does. Florida has a robust statewide data system
tracking ocer employment and requires hiring agencies to investigate appli-
cants’ employment history. Although it may be possible for an applicant to con-
ceal a prior firing, it is probably not easy. Florida also decertifies more ocers
than many other states, and a decertified ocer cannot subsequently gain em-
ployment with any Florida agency. Because our estimate of the volume of wan-
dering ocers is likely a lowball—both for Florida, specifically, and for other
states—it is hard to conclude that wandering ocers are a negligible phenome-
non.
2.
How Easily Do Wandering Ocers Find New Work?
A second core question is how much diculty ocers have finding work af-
ter being fired.
162
In this Section, we probe this question along several dimen-
sions, including how oen fired ocers land a new job, how long it takes them
to do so (assuming they are looking for work), how far they have to travel, and
how many dierent jobs they tend to hold. We count as reemployment only full-
160. Drawing upon ocial Florida data and prior academic research, Jordan Blair Woods recently
estimated that law-enforcement ocers in Florida have conducted between . million and
. million trac stops annually during the past decade. See Jordan Blair Woods, Policing,
Danger Narratives, and Routine Trac Stops, M
ICH. L. REV. , (). If wandering
ocers conducted a proportional number of these stops—three percent—they would have
conducted between , and , stops each year.
161. See supra note and accompanying text.
162. See, e.g., Shockey-Eckles, supra note , at (asserting that fired ocers who resign rather
than face license revocation find work “with relative ease”).
the wandering officer
time law-enforcement jobs in public agencies in Florida. We exclude jobs that
end with a firing followed immediately by reemployment with the same agency,
a pattern that, as mentioned earlier, likely reflects reinstatement by an arbitrator
rather than the ocer’s eorts and success on the job market.
a. Reemployment Rates
First, do ocers who have been fired obtain law-enforcement work less oen
than ocers who have not been? Interestingly, the answer depends on when
during their careers they were fired. To explore this issue, we exclude all employ-
ment stints ending aer to allow all ocers in the data at least three years
to obtain a new position.
As the bottom row of Panel A in Table reports, from to , of
ocers who separated and had never been fired obtained a new full-time law-
enforcement position in Florida. In contrast, just of ocers who were fired
from their last job secured a new position, which represents a statistically signif-
icant dierence.
163
Yet ocers who were fired further back in their employment
history obtained a new job at a rate similar to ocers who had never been
fired.
164
The rest of Table shows that the same basic results hold across ocer
characteristics and when, in Panel B, we group ocers by their history of firings
for misconduct, specifically.
165
163. p < .. All of the statistical tests in Section IV.B. are two-sided t-tests clustered at the
person and agency level.
164. While the dierence is substantively small, it is statistically significant at the . level. Ideally,
we would also calculate rehiring rates while excluding ocers who have decided to retire, to
concentrate on the subset of separated ocers who were most plausibly looking for law-en-
forcement work. We do not have data on retirements for fired ocers, however. As an imper-
fect alternative, we exclude ocers who were over fiy years of age at the time of separation;
in a separate analysis, we exclude ocers with at least twenty-five years of full-time service at
the time of separation. The basic pattern of results for both analyses is substantively similar
to the results we report in the text.
166. Sample sizes are shown in parentheses.
the yale law journal :
TABLE 3.
rehiring rates by demographic groups and professional history, 1988-2013
166
166. Sample sizes are shown in parentheses.
Panel A: Fired Panel B: Fired for Misconduct
Never
Fired
Fired,
Last Job
Fired,
Earlier
Job
Never
Fired
Fired,
Last Job
Fired,
Earlier
Job
Race
White
37%
(51,538)
18%
(6,702)
41%
(2,498)
37%
(53,908)
15%
(5,177)
42%
(1,653)
Black
40%
(5,073)
14%
(1,451)
48%
(414)
40%
(5,626)
12%
(1,074)
44%
(238)
Hispanic
49%
(5,164)
19%
(1,123)
51%
(304)
47%
(5,607)
17%
(795)
52%
(189)
Gender
Male
39%
(54,841)
17%
(8,283)
44%
(2,928)
39%
(57,692)
15%
(6,431)
45%
(1,929)
Female
35%
(7,926)
16%
(1,151)
32%
(311)
34%
(8,506)
13%
(717)
27%
(165)
Education
High
School
56%
(1,664)
19%
(284)
60%
(60)
54%
(1,813)
19%
(160)
60%
(35)
Associate’s
36%
(9,359)
20%
(1,249)
40%
(453)
36%
(9,816)
17%
(940)
42%
(305)
Bachelor’s
41%
(16,996)
21%
(1,657)
45%
(596)
41%
(17,732)
18%
(1,174)
45%
(343)
Master’s
52%
(2,739)
31%
(187)
55%
(126)
52%
(2,849)
24%
(136)
58%
(67)
All
39%
(62,780)
17%
(9,436)
43%
(3,239)
38%
(66,212)
15%
(7,149)
44%
(2,094)
the wandering officer
W
e next examine reemployment rates over time. Figure shows the propor-
tion of ocers who obtained a new job within three years, conditional on their
professional history of firings.
167
We limit our rehiring measure to three years to
address a potential censoring problem: ocers who separated in the last few
years had less time to secure a new job. If we did not limit the rehiring measure
in this way, censoring might severely deflate the rehiring rate in the last few years
relative to years prior.
As Figure shows, the lower reemployment rates we observe for fired ocers
date back at least to the late s. Moreover, the reemployment rate for ocers
fired from their last job fell in the early s, from to roughly . That
number fell again around , probably because—as we showed above—law-
enforcement hiring generally fell throughout the state during the Great Reces-
sion. Since then, the reemployment rate for fired ocers appears to have in-
creased slightly and may continue to do so if the drop was driven primarily
by the economic downturn.
FIGURE 5.
proportion of separations in which officer obtains subsequent employment
within three years, by professional history of firing, 1988-2013
167. See Figure A for similar results on firings for misconduct, specifically.
the yale law journal :
We cannot be sure why reemployment rates are so dierent for ocers who
were fired from their most recent job versus ocers who were fired from a job
further back in their employment history. One possible explanation is that the
subset of ocers who landed an intervening job were initially fired for conduct
that, on average, was less egregious than the ocers who were fired from their
last job. Another possibility is that wandering ocers who voluntarily separated
from their last job seek employment in law enforcement at higher rates than
those who were fired from their last job. A third potential explanation is that
law-enforcement agencies believe at least some previously fired ocers are “re-
deemed” if they have held at least one other job in the intervening period without
having been fired. We interrogate this “redemption” story in further detail be-
low.
b. Time to Reemployment
To assess how much fired ocers may struggle to obtain new employment,
we also consider how long it takes them to secure their next job. Folk wisdom
says not long. Our data, however, show something dierent.
As the bottom row of Table reports, ocers who were fired or fired for
misconduct from their most recent job who later obtain another job take sub-
stantially longer to do so, on average, than ocers who have never been fired or
fired for misconduct—more than days longer.
168
In Table A, we report the
median time to reemployment rather than the mean. The same basic pattern is
present, but the dierence is even starker.
169
These findings are driven, at least
partially, by the fact that ocers who separate voluntarily oen do not leave until
they have another job lined up.
170
Once again, as the rest of Table shows, fir-
ings further back in time are substantially less important and there is relatively
little variation in these patterns across demographic groups.
168. p < ..
169. The median time for ocers fired ( days) or fired for misconduct ( days) from their
previous job is over times longer than for ocers who have never been fired ( days) or
fired for misconduct ( days).
170. We can see, for example, that many ocers who separate voluntarily begin a new employment
stint the day aer finishing the last one.
the wandering officer
TABLE 4.
mean time to new employment by demographic groups and professional
history, 1988-2013
171
Panel A: Fired Panel B: Fired for Misconduct
Never
Fired
Fired,
Last Job
Fired,
Earlier
Job
Never
Fired
Fired,
Last Job
Fired,
Earlier
Job
Race
White
400
(19,280)
685
(1,175)
443
(1,024)
402
(19,977)
782
(800)
454
(702)
Black
317
(2,049)
703
(210)
209
(198)
325
(2,224)
729
(129)
213
(104)
Hispanic
316
(2,519)
787
(212)
402
(154)
322
(2,649)
910
(137)
473
(99)
Gender
Male
378
(21,451)
694
(1,441)
407
(1,288)
380
(22,327)
785
(986)
428
(867)
Female
430
(2,750)
767
(180)
355
(98)
435
(2,892)
886
(92)
402
(44)
Education
High
School
416
(937)
859
(55)
665
(36)
423
(977)
1,038
(30)
789
(21)
Associate’s
370
(3,401)
674
(247)
483
(179)
372
(3,538)
785
(162)
535
(127)
Bachelor’s
375
(6,982)
686
(347)
295
(270)
378
(7,236)
760
(210)
286
(153)
Master’s
440
(1,436)
774
(58)
586
(69)
439
(1,491)
897
(33)
854
(39)
All
384
(24,205)
703
(1,621)
403
(1,386)
386
(25,223)
794
(1,078)
426
(911)
171. Sample sizes are shown in parentheses.
the yale law journal :
c. Distance Traveled for Reemployment
We also probe how much wandering ocers may struggle to find new work
by examining how far they travel to obtain their next job. The conventional wis-
dom is that wandering ocers obtain jobs at agencies that are relatively far away
and so have less (oen informal) information about their past conduct. Surpris-
ingly, we find little evidence of such dierential movement.
172
Among ocers who have never been fired, those who separate and find new
employment move to an agency that is, on average, forty-two miles away from
their last job. Similarly, ocers who were fired from their last job or some job
further back in their employment history move to agencies that are, on average,
forty-four and forty-five miles away, respectively. The dierence in median dis-
tances was just slightly larger.
173
We also find little dierence in the movement
patterns of ocers who were fired for misconduct, specifically, either from their
most recent job or earlier.
d. Number of Subsequent Jobs
Finally, we examine how many full-time jobs wandering ocers hold over
the course of their careers and, perhaps more important, aer their first firing.
As a baseline for comparison, Figure shows the proportion of ocers who,
over the course of an entire career, worked a particular number of full-time
jobs.
174
One thing is clear: most ocers move around very little. The vast ma-
jority hold no more than one full-time job, and virtually all of them—roughly
—hold no more than two.
172. To obtain geographic coordinates, we geocoded the names of each agency using an R package
called “ggmaps” that automatically runs queries on Google Maps. In total, we were able to
obtain geographic coordinates for of the agencies in our sample. Most of the agencies we
could not geocode are state-level agencies with ambiguous (and potentially multiple) geo-
graphic locations. In total, we are missing geographic distance information for of all em-
ployment stints from which an ocer separated and later obtained another job.
173. Ocers who have never been fired move a median of fourteen miles. Ocers who were fired
from their last job move a median of twenty miles, while ocers fired further back in their
employment history move a median of seventeen miles.
174. We do not count the postfiring stint for ocers who were fired and then rehired by the same
agency.
the wandering officer
Of course, not all ocers who work more than one job are wandering oc-
ers; many were never fired at all. The top panel of Figure shows the proportion
of ocers who, aer having been fired, worked a particular number of subsequent
full-time jobs; the bottom panel presents the same information for ocers who
were fired for misconduct. Both are consistent with what we already know: the
vast majority of fired ocers—around —never secure another job. Moreo-
ver, very few—fewer than —obtain more than one additional full-time job.
Virtually none obtain more than three. Wandering ocers “jump[ing] from
agency to agency” who “may have agencies under their belt within a year
period” therefore appear to be exceedingly rare, if not apocryphal, at least as far
as our data can detect.
175
FIGURE 6.
number of full-time positions held by unique officers, 1988-2016
175. Dill, supra note .
the yale law journal 129:1676 2020
1726
FIGURE 7.
number of full-time positions held by unique officers after being fired for
the first time, 1988-2016
Taken tog
ether, our data suggest that ocers who were fired from their most
recent job may face significant challenges in securing new law-enforcement work
in Florida. They are half as likely as other ocers to obtain a new position and
it takes them twice as long to do so. Moreover, the vast majority of ocers who
were fired hold a very small number of full-time positions throughout their ca-
reer—virtually all of them have fewer than three. Interestingly, we also find evi-
dence that firings from earlier in an ocer’s career appear to pose a much smaller
obstacle to finding a new job.
the wandering officer
3. Where Do Wandering Ocers Go?
If fired ocers struggle to find work, they might seek employment at smaller
agencies, which may have fewer resources, or at agencies that serve socioeco-
nomically disadvantaged communities of color with higher crime. Various ac-
counts advance these hypotheses.
176
To test them empirically, we compare, for
each wandering ocer, characteristics of the previous agency in the year of sep-
aration to characteristics of the hiring agency in the year of hiring. We exclude all
years prior to because, for most of our agency-level variables, we lack data
before then. As in the previous Section, we also exclude jobs that end with a
firing followed by reemployment with the same agency, which likely reflects re-
instatement by an arbitrator rather than the discretionary decisions of ocers
and agencies on the market.
Our data bear out some, but not all, of the hypotheses about the movement
patterns of wandering ocers. In particular, we find that wandering ocers tend
to migrate to agencies with fewer resources in communities with slightly higher
proportions of residents of color. Ocers who were just fired tend to move to
smaller agencies as well. We find no evidence, however, that wandering ocers
move to areas with more unemployment or crime.
a. Agency Size
Ocers who were fired from their last job move to smaller agencies on aver-
age. We calculate agency size using the ATMS employment data set by counting
the number of unique full-time law-enforcement ocers employed in each
agency each year. From to , ocers who had never been fired and who
landed a new job moved, on average, from agencies with roughly ocers to
agencies with roughly ocers—representing a relative increase of nearly .
In contrast, ocers who were fired or fired for misconduct from their previous
job and who obtained a new position moved from agencies with and of-
ficers to agencies with and ocers, respectively—relative decreases of
and . These dierences between the increase experienced by ocers
who had never been fired, on the one hand, and the decreases experienced by
wandering ocers, on the other, are both large and statistically significant.
177
Ocers who were fired at some point before their most recent separation
actually move in the opposite direction. These ocers went from agencies with,
176. See sources cited supra note .
177. p < .. All of the statistical tests in Section IV.B. are two-sided t-tests clustered at the
person and agency level.
the yale law journal :
on average, ocers to agencies with ocers—a relative increase of
roughly . This increase is statistically significantly dierent from the change
in agency size experienced by ocers who had never been fired.
178
Note, how-
ever, that ocers who were fired at some point continue to work at smaller agen-
cies, on average, than ocers who had never been fired.
b. Agency Resources
Wandering ocers appear to migrate toward agencies with fewer resources.
To examine this issue, we collected data on law-enforcement expenditures by
every county and municipality in Florida from the Florida Department of Finan-
cial Services from to . The data set, therefore, contains information
only on municipal police departments and sheris’ oces and not state-level,
university, school, or port agencies.
179
Our expenditure figures include “personal
services” and “operating costs”—which cover salaries and benefits—but exclude
“capital outlays.”
180
Ocers who had never been fired and landed a new job moved, on average,
from agencies with operating budgets of . million to agencies with budgets
of . million—a relative increase of . In contrast, ocers who were fired
from their last job moved, on average, from agencies with . million budgets
to agencies with . million budgets—which represents a decline that is
statistically significantly dierent from the change experienced by ocers who
had never been fired.
181
Similarly, ocers who were fired for misconduct from
their last job moved, on average, from agencies with . million budgets to
agencies with . million budgets—a . decline that is statistically signifi-
cantly dierent from the change experienced by ocers who had never been
178. p < .. When we do the same comparison for firings for misconduct, however, the results
are not statistically significantly dierent.
179. We are missing expenditure data for roughly of all employment stints, but nearly all of
those missing observations—almost —involve an ocer moving to or from a law-en-
forcement agency that is neither a sheri’s oce nor a police department. Another limitation
of our data is that they are reported by municipalities and counties, not by the law-enforce-
ment agencies themselves. Some municipalities contract with other municipalities or counties
for policing services. Our estimates of agency budgets may exclude funds provided to an
agency by another municipality.
180. We exclude capital outlays because they are spiky over time and because, while they poten-
tially support an agency for many years, we observe only the year in which the money was
spent and not the years in which the benefits of the purchase actually accrued.
181. p < ..
the wandering officer
fired for misconduct.
182
As before, the experience of ocers who were fired fur-
ther back in their employment history more closely resembles that of ocers
who had never been fired. Ocers who were fired further back in their career
moved, on average, from agencies with . million-dollar budgets to agencies
with . million-dollar budgets—a relative increase of that is not statis-
tically significantly dierent from the change experienced by ocers who had
never been fired.
The same basic patterns emerge when we examine agencies’ budgetary dol-
lars per ocer, though the dierences are smaller in magnitude.
183
Ocers who
had never been fired and obtained a new job moved, on average, from agencies
with , per ocer to agencies with , per ocer—a relative increase
of . In contrast, ocers fired from their last job moved, on average, from
agencies with , per ocer to agencies with , per ocer—a relative
decline of , which is statistically significantly dierent from the change ex-
perienced by ocers who had never been fired.
184
Similarly, ocers fired for
misconduct from their most recent job moved, on average, from agencies with
, per ocer to agencies with , per ocer—a relative decline of
that is statistically significantly dierent from the change experienced by ocers
who had never been fired.
185
Yet again, the behavior of ocers who were fired
further back in their employment history more closely resembles the behavior of
ocers who had never been fired. Ocers who were fired at some point further
back in their employment history moved, on average, from agencies with
, per ocer to agencies with , per ocer, which is not statistically
significantly dierent from the increase experienced by ocers who had never
been fired.
c. Racial Composition
Our data suggest that wandering ocers tend to move to areas with slightly
higher proportions of residents of color. We use municipal measures of race and
ethnicity from the Census for municipal agencies, and county-level measures for
182. p < ..
183. For this measure, we divide the agency’s budget by the total number of ocers employed in
that same year. Because some law-enforcement agencies also employ correctional ocers, we
include these ocers in this estimate of agency size. We also find the same basic patterns when
we examine budgetary dollars per resident rather than per ocer.
184. p < ..
185. p < ..
the yale law journal :
all other agencies.
186
Although county-level estimates are available annually, mu-
nicipal estimates are not. We therefore use decennial measures from , ,
and . We apply the data from each of those Census years to all subsequent
years until the next decennial census.
187
From to , ocers who had never been fired and found new work
moved, on average, from agencies in areas with black populations averaging
. to agencies in areas with black populations averaging .—a relative de-
crease of . By contrast, ocers who were fired from their last job and found
new work moved, on average, from agencies in areas with black populations of
. to agencies in areas with black populations of .—a relative increase
of , which is a statistically significant dierence from the change experienced
by ocers who had never been fired.
188
Similarly, ocers who were fired for
misconduct moved from agencies in areas with black populations of . to
agencies in areas with black populations of .—a relative increase of ,
which is statistically significantly dierent from the change experienced by oc-
ers who had never been fired for misconduct.
189
There is no statistically signifi-
cant dierence between the movement patterns of ocers who were fired further
back in their career—whether or not for misconduct—and ocers who had never
been fired. The same basic patterns emerge if we examine population data for
Hispanic residents.
190
As we have shown so far, our data support certain aspects of the conventional
wisdom about wandering ocers—many move to smaller agencies, with fewer
resources, in communities with slightly more residents of color. But not every
186. Our race data is missing for roughly of all employment stints because it is mostly limited
to municipal police departments and sheris’ oces. Indeed, roughly of all the missing
observations come from employment stints in which an ocer moves from or to an agency
that is neither a municipal police department nor a sheri’s oce.
187. In other words, we assign data to all years between and ; we assign data
to all years between and ; and we assign data to all years aer .
188. p < ..
189. p < ..
190. Ocers who had never been fired moved, on average, from agencies in areas with His-
panic populations to agencies in areas with . Hispanic populations—a relative decrease
of . Ocers who were fired from their last job moved in the other direction, from agencies
in areas with . Hispanic populations to agencies in areas with . Hispanic popula-
tions—a relative increase of , which is statistically significantly dierent from the change
experienced by ocers who had never been fired (p < .). Similarly, ocers fired for mis-
conduct for their last job moved from agencies in areas with . Hispanic populations to
agencies in areas with . Hispanic populations—a relative increase of , which is not
statistically significantly dierent from the change experienced by ocers who had never been
fired for misconduct. And once again, ocers who were fired further back in their employ-
ment history behaved similarly to ocers who had never been fired.
the wandering officer
element of the conventional wisdom is borne out by the data. We turn to those
other elements next.
d. Unemployment
Contrary to the conventional wisdom, we find little evidence that wandering
ocers move to agencies in communities with higher unemployment, which we
use as a rough proxy for socioeconomic well-being.
191
This result might appear
at odds with our earlier finding that wandering ocers move to agencies in areas
with larger communities of color, but the magnitude of the change in racial com-
position was quite small.
We use two measures of unemployment rates, which produce similar
(though not identical) results. Our first measure is annual county-level unem-
ployment rates from to , which are available from the Bureau of Justice
Statistics Current Population Survey.
192
During that period, ocers who had
never been fired and secured a new position moved, on average, from agencies
in counties with an unemployment rate of . to agencies in counties with a
rate of .. Ocers who were fired from their most recent job and secured sub-
sequent employment moved from agencies in counties with an unemployment
rate of ., on average, to agencies in counties with a rate of .—a change
that is statistically significantly dierent from the change experienced by ocers
who had never been fired.
193
Ocers who were fired for misconduct experienced
a similar increase, moving from agencies in counties with an unemployment rate
of ., on average, to agencies in counties with an average rate of nearly .—
a change that is also statistically significantly dierent from that of ocers who
had never been fired.
194
While these changes are statistically significant, they are
191. See, e.g., John P. Crank, The Influence of Environmental and Organizational Factors on Police Style
in Urban and Rural Environments, J.
RES. CRIME & DELINQ. , () (using unem-
ployment rate as one of two measures of community economic conditions in examining their
relationship with policing style); Vickie L. Shavers, Measurement of Socioeconomic Status in
Health Disparities Research, J.
NAT’L MED. ASS’N , - () (listing unemploy-
ment rate as a “commonly used” measure of community socioeconomic status in public-health
research); see also Bell, supra note , at (asserting that “[p]oor communities are more
likely to hire ‘gypsy cops’”).
192. Our unemployment data is missing for roughly of all employment stints because it is
limited to municipal police departments and sheris’ oces. Indeed, roughly of all the
missing cases come from employment stints in which an ocer moves from or to an agency
that is neither a municipal police department nor a sheri’s oce.
193. p < ..
194. p < ..
the yale law journal :
substantively very small.
195
When we look at the data for firings further back in
an ocer’s career, once again we find little dierence between ocers who have
and have not been fired. If anything, ocers who were fired earlier in their career
appear to go to agencies in counties with slightly lower unemployment.
Our county-level estimates of unemployment, however, may mask variation
within counties, especially as municipal agencies, which serve municipalities,
make up the majority of all agencies in our data set. To address this problem, we
construct a second measure of unemployment that assigns municipal-level un-
employment data to municipal agencies. Unfortunately, municipal-level data is
available only back to . We therefore assign all agencies the relevant county-
level or municipal-level unemployment rate from for all years.
196
This ap-
proach is not ideal, as it requires the strong assumption that any changes in the
unemployment rate over time are constant across all localities. In other words,
in unmasking spatial variation in unemployment rates within counties, we are
forced to mask temporal variation. Still, we think the analysis useful as a check
on our results above.
Based on this second measure, ocers who had never been fired and landed
another job moved, on average, from agencies in localities with an unemploy-
ment rate of . to agencies in localities with an unemployment rate of ..
Ocers who were fired from their last job moved, on average, from agencies in
localities with an unemployment rate of . to agencies in localities with a rate
of ., while ocers who were fired for misconduct from their last job moved
from agencies in localities with a rate of . to those with a rate of .. Nei-
ther of these latter changes is statistically significantly dierent from the change
experienced by ocers who had never been fired. Finally, ocers who were fired
or fired for misconduct further back in their career moved from agencies in lo-
calities with roughly . unemployment to agencies in localities with the same
rate.
Thus, our first and second measures of unemployment point to similar an-
swers. The first measure suggests that wandering ocers migrate toward agen-
cies in communities with very slightly more unemployment than other ocers—
from about . to .—while our second measure finds no evidence at all of
any dierence in migration patterns for wandering ocers.
195. Indeed, a . or . change in the county-level unemployment rate is well within the or-
dinary range of annual fluctuations for the counties in our data set.
196. Our measure of unemployment data is missing for of all employment stints from
to . Roughly of the missing cases come from employment stints in which an ocer
moves from or to an agency that is neither a municipal police department nor a sheri’s oce.
the wandering officer
e. Crime
We also find little evidence that wandering ocers are more likely than other
ocers to migrate toward areas with more crime. We measure crime using data
from the FBI’s Uniform Crime Reports that capture “oenses known to the po-
lice” from to .
197
As it turns out, both wandering and non-wandering
ocers tended to move to agencies with less crime, and, if anything, wandering
ocers tended to experience larger decreases in crime from one job to the next—
although the dierence in the size of these drops was not statistically significant.
From to , ocers who had never been fired and landed a new job
moved, on average, from an agency with , violent crimes per , resi-
dents annually to an agency with , such crimes—a relative decrease of .
Ocers who were fired from their last job moved, on average, from an agency
with , violent crimes per , residents to an agency with , such
crimes—a relative decrease of , which is not statistically significantly dier-
ent from the decrease experienced by ocers who had never been fired. Ocers
who were fired for misconduct from their last job experienced an even greater
decrease, moving from agencies with , violent crimes per , residents
to agencies with , such crimes—a relative decrease of , which is also not
statistically significantly dierent from the change experienced by ocers who
had never been fired for misconduct.
198
Once again, ocers who were fired from
a job earlier in their employment history behaved similarly to ocers who had
never been fired.
199
Taken together, these results suggest that wandering ocers
do not move to agencies in communities with more crime.
197. Our crime data begin in because a large number of agencies did not report crime data in
. Even between and , we are missing crime data for roughly of our employ-
ment stints. But nearly all of those missing observations——involve an ocer moving to
or from a law-enforcement agency that is neither a sheri’s oce nor a police department.
Both the crime and population data are at the agency level.
198. We observe a similar pattern using property crime. Ocers who had never been fired and
found a new job moved from an agency with , property crimes per ,, on average,
to an agency with , such crimes, a relative decrease of . Ocers who were fired from
their most recent job moved from agencies with , property crimes per , to agen-
cies with , such crimes—a relative decrease of . Similarly, ocers who were fired for
misconduct from their last job and found new work moved from agencies with an average of
, property crimes per , to agencies with ,—a relative decrease of .
199. Ocers who were fired further back in their career moved, on average, from an agency with
, violent crimes per , to an agency with , such crimes—a relative decrease of
, which is similar to the decrease experienced by ocers who had never been fired. And
ocers who were fired for misconduct at some point before their most recent job moved from
agencies with an average of , violent crimes per , to agencies with ,—a relative
the yale law journal :
4. Do Wandering Ocers Engage in More Misconduct?
Many worry that when wandering ocers move, they “tak[e] trouble with
them.”
200
In the absence of systematic data, however, analysts have not known
whether this fear is justified. In this Section, we examine employment and dis-
ciplinary data to assess whether wandering ocers seem to pose heightened risks
to the communities they are hired to serve. We find that they do. We then con-
sider potential explanations for these findings.
Because one goal of our analysis is to enable critical evaluation of the choice
to hire wandering ocers, we focus on employment stints in which agencies ac-
tually exercise their discretion to bring an ocer onto the force. Throughout this
Section, therefore, we drop the post-firing employment stint for ocers who are
fired and then rehired by the same agency. As noted, many of these stints likely
represent cases in which an arbitrator forced the agency to reinstate the ocer
against its will.
a. Firing
We consider first whether wandering ocers are fired more oen than other
ocers. We begin by examining firing rates across groups of ocers with dier-
ent professional histories.
201
As Panel A in Table shows, from to ,
202
ocers who had never been fired and who secured a new position were subse-
quently fired . of the time and fired for misconduct . of the time.
203
In
contrast, ocers who were fired from their most recent position and landed a
new job were fired and fired for misconduct, respectively, . and . of the
time. This is more than twice as oen, and the dierence is statistically signifi-
cant.
204
Ocers who voluntarily separated from their last position but who had
been fired at some point earlier in their career were subsequently fired and fired
for misconduct . and . of the time, respectively. This is roughly
more oen than ocers who had never been fired, and the dierence is again
decrease of , which is not statistically significantly dierent from the change experienced
by ocers who had never been fired for misconduct.
200. Abshire, supra note , at B.
201. We do not break out these firing rates by demographic categories due to small sample sizes.
202. We exclude stints that began aer to ensure we have at least three years of follow-up for
every observation. The results are substantively similar if we start our analysis in rather
than , as we did in the previous Section due to data limitations. See Table A.
203. The denominator includes both employment stints that have ended and those that have not
yet ended.
204. p < ..
the wandering officer
statistically significant.
205
Because wandering and non-wandering ocers may
remain in their jobs for dierent lengths of time, we also report firing rates
within three-year windows (as well as one- and five-year windows in Table A),
and the results are substantively similar. The same basic pattern holds in Panel
B when we examine ocers’ history of firings for misconduct, specifically.
206
TABLE 5.
subsequent firing by professional history, 1988-2013
207
205. p < ..
206. Ocers who had never been fired for misconduct and who secured a new position were sub-
sequently fired of the time and fired for misconduct just . of the time. In contrast,
ocers who were fired for misconduct from their most recent position were fired of the
time and fired for misconduct . of the time—more than twice as oen as ocers who had
never been fired (p < .). Ocers who voluntarily separated from their last position but
had been fired for misconduct at some point earlier in their career were subsequently fired
of the time and fired for misconduct . of the time—roughly more oen than
never-fired ocers (p < .). Once again, the same pattern emerges when we examine fir-
ings within three-year windows (and one- and five-year windows in Table A).
207. As noted earlier, although our focus is on law-enforcement ocers, our variables measuring
whether an ocer was previously fired include any past firings from a position in corrections
as well. A very small number of law-enforcement rookies—ocers who are working for the
first time in a law-enforcement position—were previously fired from corrections positions.
We count these ocers as rookies when we estimate firing rates in Table , but excluding them
has very little eect on the result.
Fired
Fired for
Misconduct
n Ever
3
Years
Ever
3
Years
Panel A:
Firings
Never 29,888 8.7% 4.5% 6.6% 3.0%
Fired, last job 1,969 18.4% 11.2% 13.8% 7.9%
Fired, earlier job 1,631 14.7% 7.5% 11.1% 5.6%
Panel B:
Firings for
Misconduct
Never 31,182 9.0% 4.7% 6.7% 3.1%
Fired, last job 1,297 20.0% 12.2% 15.3% 9.0%
Fired, earlier job 1,009 15.0% 8.1% 11.2% 6.2%
Panel C:
Rookie
54,476 10.5% 5.8% 7.2% 3.2%
the yale law journal :
Earlier, we found that ocers who have been fired are far more likely to se-
cure a new job if they voluntarily separate from at least one intervening position.
We hypothesized that hiring agencies might view these ocers as rehabilitated.
The results just reported, however, imply a story of only partial redemption: of-
ficers who were fired from a job further back in their career are less likely to be
fired than ocers who were fired from their most recent position. Perhaps this
is unsurprising, as these ocers have been double-screened: two dierent agen-
cies have made the decision to hire them since they were fired. Still, these ocers
are substantially more likely to be fired than ocers who have never been fired
before.
Of course, when hiring, law-enforcement agencies do not simply choose
among veteran ocers. They might also decide to hire a rookie who has never
had a full-time job in law enforcement before. Ocers hired as rookies therefore
oer another potentially helpful performance benchmark. As Table reports in
Panel C, rookies are fired . of the time and fired for misconduct . of the
time. This makes them substantially less risky than wandering ocers—both
those who were fired from their most recent job and those who were fired from
an earlier one.
208
Ocers hired as rookies tend to be slightly riskier than veterans
who have never been fired, but the dierences are not consistently statistically
significant across our specifications.
So far, we have compared the firing rates of wandering ocers to those of all
veterans who have never been fired and all ocers hired as rookies. Yet
employment markets may vary across both space and time. To account for such
potential local variations, we next narrow our analysis both geographically and
temporally. The idea is to capture, as best we can, the subset of ocers who may
have applied for each job, or at least to approximate the type and quality of
ocers who were likely to have been in the candidate pool.
Our approach is to match each wandering ocer with all nonwandering
ocers who were hired within miles and up to days aer the wandering
ocer’s hiring date.
209
The results are similar when we change the time window
to days or year and the geographic limit to or miles. We exclude all
208. All but one of the dierences between ocers hired as rookies and wandering ocers are
statistically significant. The sole exception is when we compare firings within one year for
ocers hired as rookies and ocers who had been fired prior to their most recent job.
209. We do not allow ocers from the same agency to serve as comparators for each other. Each
wandering ocer is matched, on average, to roughly one hundred comparators. Because some
wandering ocers are matched with more comparator ocers than others, we weight each
comparator ocer by the inverse of the number of comparator ocers assigned to the same
wandering ocer. In other words, for a wandering ocer with twenty-five comparators, we
weight each of the comparators /.
the wandering officer
observations for the law-enforcement agencies for which we were unable to
obtain geographic coordinates.
210
As a result, the data set we use for this analysis
diers somewhat from the data set used previously.
In Table , Panel A presents firing rates based on professional history. As a
basis for comparison, the first row reports firing rates for all ocers who had
never previously been fired. Overall, these ocers were fired of the time.
The next row reports firing rates for wandering ocers who had been fired from
their immediately preceding job. Similar to our results in Table , these ocers
ended up being fired . of the time—almost more oen. More
important, the following row reports the firing rate for the never-fired ocers
who were chosen as matched comparators based on location and timing. These
ocers were fired . of the time—slightly more than all ocers who had
never been fired, but still far less than the wandering ocers.
211
Matching
comparator ocers to wandering ocers based on geography and timing thus
appears to have slightly increased the firing rate of the comparison group, but
the comparator ocers are still fired at rates much lower than the wandering
ocers are.
The remaining rows in Panel A conduct the same analysis for wandering
ocers who were fired from a job further back in their employment history. The
results are substantively similar.
212
In the remaining columns of Panel A, the
same basic pattern also appears for firings within a three-year window and
subsequent firings for misconduct. Panel B shows similar results when we define
wandering ocers as ocers who had been fired for misconduct, specifically.
210. The vast majority of these agencies are state-level agencies for which assigning a specific lo-
cation is not straightforward.
211. The statistical significance tests we report here are two-sided t-tests based on cluster-robust
standard errors clustered at the person level. The dierence in firing rate between these wan-
dering ocers and matched comparators is statistically significant at the . level.
212. The dierence in firing rate between these wandering ocers and matched comparators is
again statistically significant at the . level.
the yale law journal :
TABLE 6.
subsequent firing by professional history for matched comparators based
on timing and geography, 1988-2013
Fired Fired for Misconduct
Ever 3 Years Ever 3 Years
Panel A:
Firings
Never
10.0% 5.4% 7.1% 3.3%
Fired,
last job
Wanderer 18.6% 11.1% 14.0% 8.0%
Comparator 10.8% 6.0% 7.9% 3.8%
Fired,
earlier job
Wanderer 14.6% 7.4% 11.1% 5.7%
Comparator 10.2% 5.6% 7.1% 3.3%
Panel B:
Firings for
Misconduct
Never
10.1% 5.5% 7.2% 3.3%
Fired,
last job
Wanderer 20.2% 12.1% 15.7% 9.2%
Comparator 10.9% 6.0% 8.1% 3.9%
Fired,
earlier job
Wanderer 15.3% 8.3% 11.5% 6.5%
Comparator 10.5% 5.7% 7.4% 3.5%
We can narrow our comparator pool even further if we assume that
applicants similarly evaluate the desirability of working at specific agencies and
that agencies similarly evaluate the desirability of job applicants. If so, at least
within local labor markets, more desirable candidates are likely to be hired by
more desirable agencies.
213
Under this logic, we can further narrow our analysis
to the pool of candidates who were likely vying for the same job by matching
wandering ocers not only based on timing and geography but also based on
whether they were hired by a similarly desirable agency.
Designing an objective measure of agency desirability poses two challenges.
The first is that we need to know what agency characteristics ocers value.
Unfortunately, there is little relevant empirical evidence in the policing literature.
Moreover, dierent ocers may value agency characteristics dierently. As just
one example, some ocers may prefer to work at agencies with high levels of
213. See David Card et al., Workplace Heterogeneity and the Rise of West German Wage Inequality,
Q.J.
ECON. , - () (finding that more educated workers in Germany tend to be
hired by higher-paying employers and that this correlation increased over time from the s
to the late s).
the wandering officer
crime—to be close to the action—while others may prefer agencies in safer areas.
The second challenge is that we need observable measures of agency
characteristics—measures that ideally vary over time, given the longitudinal
nature of our study.
Given these constraints, we proxy for agency desirability using annual
expenditures per ocer. The assumption is that job candidates prefer to work at
agencies with more money to spend on each ocer—for salaries, benefits, perks,
and other organizational resources.
214
Because the expenditure data start in
, we drop all employment stints that began before that year. We then use
agency expenditure data to compute, within each year,
215
the percentile rank of
each agency in terms of expenditures per ocer. We group the agencies into
quintiles—with the bottom twenty percent in the first quintile, the next twenty
percent in the second quintile, and so on, until the fih quintile, which includes
the top twenty percent in each year. We then use these quintile scores to select
comparator ocers who were hired within ninety days and fiy miles of the
wandering ocer by an agency within the same desirability quintile.
216
As we
show in Table A, our basic results do not change when we match on agency
desirability.
This geographic, temporal, and agency-desirability matching process helps
us get closer to capturing a picture of the “marginal ocer”—the ocer the
agency would have hired had it not hired the wandering ocer. Ideally, we
would compare wandering ocers and marginal ocers more directly. Without
data on job applicants,
217
however, it is impossible to identify the actual marginal
ocers, and we cannot probe this question any more closely.
The foregoing analysis has revealed consistent evidence that wandering
ocers are fired at significantly higher rates than both veterans who have never
214. To obtain a comprehensive measure of the number of ocers employed by an agency, we
count both law-enforcement ocers and correctional ocers.
215. Because some agencies’ expenditures vary substantially from year to year, we also calculated,
for each agency, average expenditures per ocer over the entire period, to . We then
reran our analysis using this time-invariant measure of agency desirability. The results are
similar.
216. Because matching on desirability quintiles substantially reduces the pool of potential compar-
ator ocers, out of the , wandering ocers who were hired from to who
had previously been fired were not matched with any comparators. Also, of the , of-
ficers hired during this period who had previously been fired for misconduct did not receive
a match. We drop these unmatched wandering ocers from this analysis. The remaining
wandering ocers were matched with an average of about twenty-one comparators.
217. We did not try to collect applicant data because it would require obtaining sensitive personnel
records from each of the hundreds of law-enforcement agencies in Florida, likely an impossi-
ble task.
the yale law journal :
been fired and rookies. One lingering question is whether this relationship—
between wandering-ocer status and firing rates—is mediated by other
observable ocer characteristics. Might it be, for example, that wandering
ocers tend to be young and that youth predicts a higher firing rate? If so, what
appears to be a “wandering-ocer eect” might really be a “youth eect.”
To explore this possibility, Table reports a series of linear probability
models, at the employment-stint level, on our variable measuring firings within
three years.
218
Model essentially replicates the results in Table because it
contains only the professional-history variables: whether an ocer was fired
from his last job, was fired from a job further back in his employment history,
or was hired as a rookie. Veterans who have never been fired are the comparison
group. The model confirms that ocers who were fired from their last job and
ocers fired further back in their employment history are subsequently fired .
and . percentage points more oen, respectively, than ocers who have never
been fired.
In Model , we add some ocer-level demographic variables: age and
gender.
219
The addition of these variables has little eect on the coecients for
either type of wandering ocer, meaning that the predictive power of being a
wandering ocer is not merely driven by these demographic characteristics.
Model adds educational attainment to the model, but this step is,
unfortunately, more complicated because the variable is frequently missing. The
coecient for ocers who were fired from their last job falls slightly, while the
coecient for ocers who were fired from an earlier job falls dramatically, by
roughly two-thirds. It is possible, however, that dropped observations due to
missing data, rather than the predictive power of educational attainment, are
driving these dierences. To test this possibility, in Model we replicate Model
but drop all observations for which we lack education data. The results are
similar to Model . This shows that the change in coecients we observed in
Model is likely due to the loss of observations from missing data rather than to
the eect of the education variables.
220
In the end, therefore, we find little
evidence that observable ocer-level variables mediate the heightened firing rate
that wandering ocers experience.
218. We report the results of linear probability models (or linear regressions) because the magni-
tudes of their coecients are easy to interpret. The results are substantively similar, however,
when we use logistic regression, which better fits the binary structure of the dependent vari-
able. The results are also substantively similar when we fit the model on our variable measur-
ing firings for misconduct within three years.
219. We divide age by ten so that the coecients are not rounded to zero.
220. The fact that the results change dramatically thus suggests that the education variable is not
missing randomly.
the wandering officer
TABLE 7.
regression models on firing within three years, 1988-2013
221
Model 1 Model 2 Model 3 Model 4
Intercept
0.045**
[0.002]
0.053**
[0.006]
0.044**
[0.007]
0.025**
[0.005]
Fired from
Last Job
0.067**
[0.007]
0.067**
[0.007]
0.063**
[0.010]
0.064**
[0.010]
Fired from
Earlier Job
0.029**
[0.007]
0.030**
[0.007]
0.011
[0.009]
0.011
[0.009]
Rookie
0.012**
[0.002]
0.011**
[0.002]
0.010**
[0.002]
0.011**
[0.002]
Age
-0.002
[0.001]
0.004**
[0.001]
0.004**
[0.001]
Male
-0.002
[0.003]
-0.007*
[0.003]
-0.005
[0.003]
Associate’s
-0.009*
[0.005]
Education Bachelor’s
-0.024**
[0.004]
Master’s
-0.034**
[0.005]
n 87,964 87,948 46,974 46,974
b. Complaints
As an alternative measure of ocer performance, we also consider com-
plaints filed with the state licensing board—the CJSTC—alleging “moral char-
acter violations” as defined by Florida law. As detailed above,
222
these complaints
typically begin as civilian or internal aairs allegations investigated by a local
221. An asterisk (*) denotes an estimate that is statistically significant at the . level; two aster-
isks (**) denote an estimate that is statistically significant at the . level. While our thresh-
old of statistical significance throughout the paper is ., we also note estimates that are
statistically significant at the . level with a dagger (†). Cluster-robust standard errors clus-
tered at the person- and agency-level are reported in brackets.
222. See supra Section III.A.
the yale law journal :
agency. If the local agency sustains the allegation and the oense implicates the
ocer’s “moral character,” the agency must submit its findings to the FDLE,
which opens a “complaint” and begins an independent disciplinary process. The
FDLE also has the power to initiate complaints on its own, which are included
in the data set. Once again, our results suggest that wandering ocers may pose
significant risks.
Table shows the rate at which ocers received complaints conditional on
their professional history. Because the FDLE appears to have begun consistently
recording complaints in , our analyses of complaints include only employ-
ment stints beginning between and . As in our analysis of firings, we
exclude job stints in which an ocer is employed by an agency that had fired the
ocer in his immediately preceding job. Panel A shows that ocers who had
never been fired and who secured a new position received an average of .
complaints during their next job. In contrast, ocers who were fired from their
last job received an average of . complaints, almost ninety percent more, a
dierence that is statistically significant.
223
Ocers who voluntarily separated
from their previous job but had been fired at some point earlier in their career
received an average of . complaints, which is also statistically significantly
more than ocers who had never been fired.
224
223. p < ..
224. p < ..
the wandering officer
TABLE 8.
number of complaints by professional history and type, 1993-2013
All
Violent/
Sexual
Integrity
n Ever
3
Years
Ever
3
Years
Ever
3
Years
Panel A:
Firings
Never 24,711 0.07 0.02 0.02 0.01 0.02 0.01
Fired,
last job
1,394 0.13 0.07 0.04 0.02 0.06 0.03
Fired,
earlier jobs
1,295 0.12 0.05 0.03 0.01 0.05 0.02
Panel B:
Firings for
Misconduct
Never 25,686 0.07 0.02 0.02 0.01 0.02 0.01
Fired,
last job
934 0.16 0.08 0.04 0.03 0.07 0.03
Fired,
earlier jobs
780 0.14 0.05 0.03 0.01 0.06 0.02
Panel C:
Rookie
44,584 0.08 0.03 0.02 0.01 0.03 0.01
One question is whether the additional complaints that wandering ocers
receive concern the types of misconduct the public finds most troubling. While
we are constrained by the relatively small number of complaints in the data, we
are able to break complaints into broad categories based on the character of the
misconduct alleged. Almost a quarter of the complaints are for violent or sexual
conduct (including implied violence), the most common allegations of which are
“excess force,” “assault,” “battery – domestic violence,” and “sex oense.”
225
An-
other third are integrity-related complaints, the most common allegations of
which are “false statements,” “perjury,” “misuse of public position,” and
225. In our primary specification, we exclude from this category complaints concerning prostitu-
tion, sex on duty, intimidation, harassing communication, sexual harassment, resisting an of-
ficer, unprofessional relationships, and the manufacture, possession, or transportation of ob-
scene materials. The results, however, are substantively similar when we include these
complaints. Details of the coding scheme are available from the authors upon request.
the yale law journal :
“fraud.”
226
We also create a category for drug-related allegations, which account
for just of the complaints. This is too few to support any reliable results,
but also indicates that our general results, reported above, are not driven by
drug-related oenses.
As Table shows, ocers who had never been fired and who secured a new
position received an average of . complaints for violent or sexual conduct
during their next job. In contrast, ocers who were fired from their last job re-
ceived an average of ., roughly twice as many, a dierence that is statistically
significant.
227
Ocers who voluntarily separated from their previous job but had
been fired at some point earlier in their career received an average of . com-
plaints for violent or sexual conduct, which is statistically significantly dierent
from ocers who had never been fired.
228
We find a similar pattern of results for
integrity-related complaints.
Because employment stints vary in length, Table also reports the average
number of complaints against ocers within a three-year window, and the re-
sults are similar (as are the results within one- and five-year windows, reported
in Table A).
229
Furthermore, as Panel B of Table shows, all of these same pat-
terns hold if we examine ocers who have been fired for misconduct instead.
Here, too, ocers hired as rookies provide another useful performance
benchmark for wandering ocers. As shown in Panel C of Table , ocers hired
as rookies receive a roughly similar number of complaints as veterans who have
never been fired
230
—and fewer than wandering ocers.
231
As before, we next match each wandering ocer with the nonwandering
ocers who were hired within miles and fewer than days aer the
226. We exclude traditional the oenses and oenses involving stolen property, which do not
necessarily implicate truthfulness or the abuse of an ocial position.
227. p < ..
228. p < ..
229. The one exception is that, for violent and sexual complaints, the dierence between ocers
who have never been fired and ocers who voluntarily separated from their last job but who
were fired earlier in their career is not statistically significant within a one-, three-, or five-
year window.
230. When we count all complaints, all violent or sexual complaints, or all integrity-related com-
plaints incurred during the employment stint, ocers hired as rookies receive statistically sig-
nificantly more complaints than veterans who have never been fired, at a threshold of p < .,
though the dierences are substantively small. In the time-limited comparisons, most of the
dierences are not statistically significant.
231. The dierences are always statistically significantly dierent for ocers who were fired from
their last job (p < .). They are also statistically significant for ocers who were fired fur-
ther back in their employment history when we count all complaints and integrity-related
complaints (p < .), but not when we count only violent or sexual complaints.
the wandering officer
wandering ocer’s hiring date.
232
The results, reported in Table , follow the
now-familiar pattern: wandering ocers receive, on average, . complaints—
about more than all ocers with no prior history of firing (. complaints)
and the comparator ocers (. complaints).
233
The same pattern of results
holds for wandering ocers who were fired from a job earlier in their
employment history, for complaints received within a three-year window, and
for wandering ocers who were fired for misconduct, specifically. The same
patterns also hold when we count only violent or sexual complaints or integrity-
related complaints.
234
And, as reported in Table A, the same basic pattern holds
when we match comparator ocers not only on timing and geography but also
on agency desirability.
TABLE 9.
number of complaints by professional history for matched comparators
based on timing and geography, 1993-2013
All Violent/Sexual Integrity
Ever
3
Years
Ever
3
Years
Ever
3
Years
Panel A:
Firings
Never
0.08 0.03 0.02 0.01 0.03 0.01
Fired,
last job
Wanderer 0.14 0.07 0.04 0.02 0.06 0.03
Comparator 0.08 0.03 0.02 0.01 0.03 0.01
Fired,
earlier job
Wanderer 0.12 0.05 0.03 0.01 0.06 0.02
Comparator 0.07 0.03 0.02 0.01 0.03 0.01
Panel B: Firings
for Misconduct
Never
0.08 0.03 0.02 0.01 0.03 0.01
Fired,
last job
Wanderer 0.16 0.08 0.05 0.03 0.07 0.03
Comparator 0.08 0.03 0.02 0.01 0.03 0.01
Fired,
earlier job
Wanderer 0.14 0.06 0.03 0.01 0.07 0.02
Comparator 0.07 0.03 0.02 0.01 0.03 0.01
232. The results are similar when we expand the time window to days or one year and when
we expand the geographic limit to or miles.
233. The statistical significance tests we report here are two-sided t-tests based on cluster-robust
standard errors clustered at the person level. The dierence in complaints received by these
wandering ocers and their matched comparators is statistically significant at the . level.
234. The dierences are not always statistically significant, however, for violent and sexual com-
plaints within a three-year window.
the yale law journal :
Finally, as with our firing measures, we consider whether the higher number
of complaints against wandering ocers is mediated by other observable ocer
characteristics. Table reports a series of linear-regression models that mirror
those described in Table .
235
The basic results are the same: we find no evidence
that adding ocer age, gender, or education to the models decreases the size of
the coecient on the professional-history variables. Dierences in coecients
among the models appear to be due to the loss of observations from missing data
rather than to the eect of the independent variables. We therefore find no
evidence that observable ocer-level characteristics mediate the heightened risk
associated with hiring wandering ocers.
TABLE 10.
regression models on number of complaints within three years, 1993-2013
236
Model 1 Model 2 Model 3 Model 4
Intercept
0.024**
[0.001]
0.033**
[0.003]
0.025**
[0.004]
0.019**
[0.004]
Fired from
Last Job
0.044**
[0.008]
0.043**
[0.008]
0.038**
[0.010]
0.039**
[0.010]
Fired from
Earlier Job
0.023**
[0.007]
0.024**
[0.006]
0.01
[0.009]
0.01
[0.009]
Rookie
0.002
[0.001]
-0.001
[0.002]
0
[0.002]
0.001
[0.002]
Age
-0.005**
[0.001]
-0.001
[0.001]
-0.001†
[0.001]
Male
0.007**
[0.002]
0.004†
[0.002]
0.005*
[0.002]
235. We report the results of linear models because the magnitudes of their coecients are easy to
interpret. The results are substantively similar, however, when we use negative binomial re-
gression.
236. An asterisk (*) denotes an estimate that is statistically significant at the . level; two aster-
isks (**) denote an estimate that is statistically significant at the . level. While our thresh-
old of statistical significance throughout the paper is ., we also note estimates here that are
statistically significant at the . level with a dagger (†). Cluster-robust standard errors clus-
tered at the person- and agency-level are reported in brackets.
the wandering officer
Education
Associate’s
-0.001
[0.003]
Bachelor’s
-0.008**
[0.002]
Master’s
-0.011**
[0.003]
n
71,984 71,971 40,436 40,436
c. Explanations
Taken together, our results suggest that wandering ocers are significantly
more likely than comparable ocers—either ocers hired as rookies or veterans
who have never been fired from a Florida law-enforcement agency—to be fired
and to incur complaints of serious misconduct. What explains these findings? At
least four hypotheses strike us as plausible.
First, agencies may scrutinize wandering ocers more rigorously than other
ocers by monitoring them more closely or applying a lower threshold for ini-
tiating the disciplinary process. To put the point most forcefully, agencies might
even hire wandering ocers on a de facto “probationary” basis, intending simply
to fire them if problems arise. Although this is possible, it strikes us as unlikely
that it could fully explain the sizable gaps in the rates at which ocers are fired
and incur complaints.
As an initial matter, employee turnover, or “churn,” is typically expensive: a
recent review of the labor economics literature found that the median cost of
replacing a worker is roughly one-fih of the worker’s salary.
237
This makes less
plausible the notion that agencies would hire wandering ocers on a probation-
ary rationale, particularly as the agencies that hire wandering ocers tend to be
more poorly resourced. At the same time, the literature emphasizes that the costs
of turnover vary by industry, region, and other factors, and they may be lower
for Florida law enforcement than this general evidence suggests.
238
237. See Boushey & Glynn, supra note , at . We have not found any research on the cost of
turnover in law enforcement specifically. For potential analogs, see G
ARY BARNES ET AL., THE
COST OF TEACHER TURNOVER IN FIVE SCHOOL DISTRICTS: A PILOT STUDY - () (estimat-
ing the cost of teacher turnover in five school districts in at , to ,); and
Michelle I. Graef & Erick L. Hill, Costing Child Protective Services Sta Turnover, C
HILD
WELFARE , () (estimating the cost of turnover for a child protective services
worker in a midwestern state in at ,).
238. Where labor requires “knowledge exploitation” (i.e., implementation, execution) rather than
“knowledge exploration” (i.e., discovery, innovation), however—as seems generally true of
the yale law journal :
To probe this first hypothesis more closely, we consider a series of additional
empirical checks. None of these checks is conclusive on its own but, collectively,
they suggest that heightened scrutiny cannot fully explain our results. To begin
with, we found earlier that wandering ocers receive not only more complaints
in general, but also more complaints specifically about violent or sexual miscon-
duct. While certainly possible, it seems less likely that heightened scrutiny could
drive those results, as agencies presumably take violent and sexual conduct more
seriously than other misconduct regardless of which ocers are accused.
Our complaint data oer another way to test the heightened-scrutiny hy-
pothesis. The FDLE, a statewide entity independent from the local agencies that
employ law-enforcement ocers, itself initiates roughly one-third of the com-
plaints in our data set. These include complaints opened in response to an of-
ficer’s arrest, a news report, a verified citizen complaint tendered to the FDLE
directly, or a problem FDLE sta discovered while auditing local-agency rec-
ords.
239
If heightened scrutiny by employing agencies explained why wandering
ocers receive more complaints, we would expect the gap in complaint rates to
disappear in this subset of the data (because, again, the employing agencies do
not trigger these complaints). The data does not bear this out. We reran our
analysis of complaints on the FDLE-initiated subset. As Table A shows, the
results are substantively similar to when we use all complaints: across each of the
specifications, wandering ocers were roughly twice as likely as ocers who had
never been fired to receive a complaint initiated by the FDLE.
240
Next, we examine firing and complaint rates across agencies of varying size.
What little has been written on the topic suggests that smaller agencies are less
policing—the average net eects of turnover are expected to be negative. See Ton & Huckman,
supra note , at -, .
239. The ATMS database contains seven codes that define the source of a complaint: adavit of
separation, arrest hit notification, FDLE sta documentation, internal investigation, newspa-
per, other, and verifiable complaint. Based on conversations with the FDLE, we define a com-
plaint as initiated by the FDLE (and not the ocer’s agency) if its code is anything other than
adavit of separation or internal investigation. As noted, see supra Section III.A, we have data
only on the date a complaint was opened and not the date on which the alleged misconduct
occurred. It is possible that, in some cases, there is a nontrivial gap between the date a com-
plaint was opened and the date of the alleged misconduct.
240. The dierence between never-fired ocers and ocers who were fired from their last job is
statistically significant across all comparisons at p < .. The dierence between never-fired
ocers and ocers who were fired further back in their employment history is significant
across all comparisons at p < ..
the wandering officer
likely than their larger counterparts to have the resources and organizational ap-
paratus to closely monitor and remediate particular ocers.
241
Thus, if wander-
ing ocers are fired more frequently or receive more complaints than other of-
ficers because they face heightened scrutiny, we would expect these gaps to
narrow or disappear in smaller agencies.
242
We therefore reran our analyses of
firing and complaints aer breaking the data into two groups of agencies, by
size: agencies that employed fewer than sixty-two ocers—the smallest quartile
of employment stints—and agencies that employed sixty-two ocers or more.
We then reran the analysis again using even smaller thresholds: thirty-one oc-
ers and fieen ocers. Across all of these comparisons, our results were substan-
tively similar.
243
In both large and small agencies, ocers who were fired from
their last job were roughly twice as likely to be fired,
244
be fired for miscon-
duct,
245
or receive a complaint
246
than ocers who had never been fired before.
241. See, e.g., STEVEN G. BRANDL, POLICE IN AMERICA () (“[L]arger police departments tend
to have more rules and policies than smaller ones.”); P
RESIDENT’S TASK FORCE ON ST CEN-
TURY
POLICING, supra note , at - (reporting that “small forces oen lack the resources
for training and equipment accessible to larger departments” and encouraging consolidation);
David N. Falcone et al., The Small-Town Police Department, P
OLICING: INT’L J. POLICE STRAT-
EGIES
& MGMT. , () (“Given the low number of [full-time employees] for small-
town police departments, and the near absence of organizational dierentiation, all ocers,
regardless of rank, must carry out general patrol functions.”);
Kevin Johnson, Lack of Training,
Standards Mean Big Problems for Small Police Departments, USA
TODAY (June , , :
PM), https://www.usatoday.com/story/news/nation////small-police-departments
-standards-training/ [https://perma.cc/YFL-LEJG] (“[Q]uestions about leader-
ship, training and basic competence track an array of unmet public safety needs that threaten
small-town policing operations in communities across the country.”); cf. Casey Toner & Jared
Rutecki, 113 Suburban Cop Shootings, Zero Discipline, WBEZ (Jan. , ), https://interac-
tive.wbez.org/taking-cover/zero-discipline [https://perma.cc/CV-WLTT] (“The investi-
gation found little evidence that suburban police agencies do any self-reflection or post-mor-
tem reviews aimed at retraining ocers involved in deadly shootings, a practice oen
employed at larger departments.”).
242. Alternatively, it is possible that police chiefs in smaller agencies are better able to monitor
wandering ocers closely. Our results run contrary to this prediction, too.
243. See infra Table A. To reduce the size of the table, we present only the results for agencies that
are bigger and smaller than fieen ocers at the time the ocer was hired and for firings (but
not firings for misconduct). The results are substantively similar when we break up agencies
based on the thirty-one-ocer and sixty-two-ocer thresholds and when we examine firings
for misconduct.
244. p < ..
245. p < ..
246. p < ..
the yale law journal :
While ocers who were fired further back in their employment history also ex-
perienced heightened rates of firing and complaints—both in small and large
agencies—the dierences were only sometimes statistically significant.
Finally, we probe the heightened-scrutiny hypothesis by examining the tim-
ing of firings and complaints. If wandering ocers are, like probationary em-
ployees, scrutinized more closely than others, this heightened monitoring is
probably not indefinite. Indeed, monitoring can be costly and its expected re-
turns may diminish with time. It seems likely, then, that wandering ocers
blend in with the rest of the workforce at some point. We therefore calculate the
average rate at which ocers are fired and receive complaints during their fourth
through seventh years of employment.
247
As Table A shows, wandering oc-
ers continue to experience higher rates of firings
248
and complaints
249
even dur-
ing those later years, though the gap between wandering and nonwandering of-
ficers is somewhat smaller during this period than during years one through
three.
A second potential explanation for the elevated rates of firing and complaints
that wandering ocers receive is that, even if wandering ocers are not treated
dierently within agencies, they may, on average, take jobs at agencies that are
better at detecting misconduct or more likely to initiate the disciplinary process.
We are doubtful this hypothesis is correct. If anything, agencies that are better
at detecting misconduct or more likely to initiate the disciplinary process are,
because of their high quality, probably less likely to hire wandering ocers in the
first place. While we cannot directly observe the stringency of an agency’s disci-
plinary process, our composite measures of agencies’ hiring and training re-
quirements provide useful proxies. In our data, ocers who were fired from
their last job and land a new position tend to move to agencies with less stringent
247. We also examine the data in one-year increments. The basic results are substantively similar
but noisy due to the limited number of firings and complaints within each period. In calcu-
lating the firing and complaint rates in years four through seven, we count in the denominator
all ocers who had le the agency in years one through three. If instead we drop these ocers
from the analysis, the dierence in firing rates between wandering and nonwandering ocers
is even larger.
248. The firing rate for ocers fired from their last job is statistically significantly dierent from
that of ocers who were never fired at a threshold of .. For ocers fired earlier in their
employment history, the dierence is statistically significant at the . level for all compari-
sons except when we measure both professional history and subsequent firings using firings
for misconduct.
249. The number of complaints for ocers fired from their last job is only marginally statistically
significantly dierent from the number of complaints for ocers who have never been fired,
at the . level. For ocers fired further back in their employment history, the dierence is
statistically significant at the . level.
the wandering officer
hiring and training requirements than ocers who have never been fired.
250
This
suggests that, as we expected, wandering ocers are not moving to agencies
with more stringent disciplinary processes than other agencies.
Third, wandering ocers may take jobs at agencies where, due to dierences
in culture, leadership, or beat assignment, they are exposed to conditions con-
ducive to further misconduct. Although we did not find any evidence that wan-
dering ocers move to agencies in higher-crime jurisdictions, many move to
smaller agencies with relatively fewer resources. For this reason, we think this
third hypothesis warrants further investigation.
We test the hypothesis in two ways.
251
First, we assess whether observable
agency-level variables reduce the predictive power of ocers’ professional-his-
tory variables. Table presents a series of linear probability models where the
dependent variable is whether an ocer was fired within three years.
252
Because
some of our agency-level variables begin in , we restrict our analysis to em-
ployment stints that began between and . Model contains only the
professional-history variables and shows that, during that period, ocers who
were fired from their last job and ocers fired further back in their employment
history were subsequently fired . and . percentage points more oen, re-
spectively, than veterans who had never been fired, the comparison group.
In Model , we add a number of agency-level variables (as of the date the
ocer’s employment began): total number of ocers employed by the agency,
agency expenditures per full-time ocer, violent crimes per , residents,
250. Our hiring and training scores begin in . We are also missing a substantial number of
observations within our sample. As a result, we lack hiring-requirement and training-require-
ment data for and of observations from to , respectively. Most of these
missing cases— and of the missing hiring and training observations—concern moves
from or to agencies that are neither municipal police departments nor sheris’ oces. Ocers
who have never been fired, who separated from their previous job voluntarily, and who land
a new job move from agencies that, on average, have hiring and training scores of . and .
to agencies with scores of . and .—almost no change at all. Similarly, ocers who were
fired earlier in their employment history but voluntarily separated from their last job and
found new work moved from agencies with hiring and training scores of . and . to agen-
cies with scores of . and .. In contrast, ocers who were fired from their last job and
obtain new work tend to move to agencies with lower hiring and training scores; indeed, they
move from agencies with average scores of . and . to agencies with scores of . and .
(p < .).
251. As in the rest of our analyses of professional history and firings, we exclude job stints in which
an ocer is employed by an agency that had fired him in his immediately preceding job.
252. As before, we report the results of linear probability models (or linear regressions) because
the magnitudes of their coecients are easy to interpret. The results are substantively similar,
however, when we use logistic regression, which better fits the binary structure of the depend-
ent variable.
the yale law journal 129:1676 2020
1752
property crimes per 100,000 residents, unemployment, proportion of the pop-
ulation that is black, and proportion of the population that is Hispanic. Adding
these variables decreases the coecient both for ocers who were fired from
their last job and for ocers who were fired further back in their employment
history by 10% to 15%. Because we are missing data for at least one of the agency-
level variables in roughly 17% of employment stints from 1997 to 2013, it is once
again possible that missing data—rather than the introduction of the agency-
level variables—explain the change in results from Model 1 to Model 2. We assess
this possibility in Model 3 by replicating Model 1 while dropping any observa-
tions for which we are missing some agency-level data. The coecients both for
ocers fired from their last job and for ocers fired from an earlier job are
roughly similar to those in Model 1, suggesting that the addition of the agency-
level variables is responsible for much of the modest decrease in the coecient
from Model 1 to 2. Taken together, these models suggest that the agency-level
variables may reduce the predictive power of the professional-history variables
but only very slightly.
Second, we test whether unobservable agency-level characteristics reduce the
predictive power of the professional-history variables. More specifically, we in-
troduce agency fixed eects to assess whether the heightened rates at which wan-
dering ocers are fired stem from their moving to agencies with higher rates of
firing and complaints in general. As Model 4 in Table 11 shows, adding these
fixed eects has little impact on the size of the coecients for either type of wan-
dering ocer. This provides further evidence that this third hypothesis has lim-
ited explanatory power.
the wandering officer
TABLE 11.
regression models on firing within three years with agency characteristics,
1997-2013
253
Model 1 Model 2 Model 3 Model 4
Intercept
0.039**
[0.002]
0.052**
[0.006]
0.041**
[0.002]
Fired from Last Job
0.066**
[0.009]
0.055**
[0.009]
0.060**
[0.009]
0.056**
[0.009]
Fired from Earlier Job
0.029**
[0.008]
0.026**
[0.009]
0.029**
[0.009]
0.024**
[0.009]
Rookie
0.015**
[0.003]
0.018**
[0.002]
0.015**
[0.003]
0.016**
[0.002]
Number of Officers
(In Hundreds)
-0.002**
[0.000]
0.002
[0.002]
Expenditure Per Officer
(In Thousands)
0
[0.000]
0
[0.000]
Violent-Crime Rate
(Per 100 Residents)
0.005**
[0.001]
0.002
[0.003]
Property-Crime Rate
(Per 100 Residents)
-0.003†
[0.002]
0
[0.002]
County-Level
Unemployment Rate
0.024
[0.059]
-0.063
[0.058]
Proportion Black
-0.008
[0.014]
-0.053
[0.061]
Proportion Hispanic
-0.015
[0.010]
-0.042
[0.060]
Agency Fixed Effects No No No Yes
n 59,861 49,687 49,687 49,687
The fourth and final hypothesis is the most straightforward: holding all else
constant, wandering ocers may simply behave worse than ocers who have
253. Asterisks (*) denote estimates that are statistically significant at the . level; two asterisks
(**) denote estimates statistically significant at the . level. While our statistical signifi-
cance threshold is ., we note estimates statistically significant at the . level with daggers
(†). Cluster-robust standard errors clustered at the person- and agency-level are in brackets.
the yale law journal :
never been fired. On top of that, they tend to be hired by smaller agencies with
fewer resources, which may be unable to provide them with the policies, train-
ing, and supervision that could help them stay in line. Because the other hypoth-
eses largely fail, and because this explanation is consistent with anecdotal ac-
counts, we suspect that this is, in the end, the most plausible explanation of the
higher rates of firing and complaints against wandering ocers.
v. predicting which wandering officers get fired again
Before we turn to potential legal reforms for the problems just identified, we
examine one final empirical question: whether certain agencies, or certain wan-
dering ocers, pose greater or lesser risks than others. More specifically, should
certain agencies be especially wary of hiring wandering ocers? Do agencies’
hiring or training requirements aect the risk that wandering ocers pose? Are
there observable characteristics that mark some wandering ocers as more or
less risky than others?
To examine these questions, we fit a series of linear probability models on a
data set containing an observation for every time, from to , an agency
hired a wandering ocer.
254
The dependent variable indicates whether the of-
ficer was fired within three years. We include a range of independent variables
measuring characteristics of the ocer, as well as the law-enforcement agencies
he moved from and to, in order to estimate whether any of these characteristics
is associated with a lower probability that the ocer’s separation was involun-
tary. As in the earlier analyses of firings, we exclude job stints in which an ocer
is employed by an agency that had fired the ocer in his immediately preceding
job.
Table presents the results.
255
Model includes one independent variable:
whether the ocer was fired from his most recent job. It shows that such ocers
are . percentage points more likely to be fired than other wandering ocers.
Model adds various ocer-level independent variables, including the number
of jobs worked, gender, age (at start of employment), and a dummy for prior
complaints. The only statistically significant variable is whether the ocer was
fired from his most recent job.
254. We report the results of linear probability models (or linear regressions) because the magni-
tudes of their coecients are easy to interpret. The results are substantively similar, however,
when we use logistic regression, which better fits the binary structure of the dependent vari-
able.
255. We report cluster-robust standard errors clustered at the agency and ocer level.
the wandering officer
In Model , we add a variable reflecting the ocer’s educational attainment
to see whether education is associated with the likelihood that a wandering of-
ficer is fired. Because education data is missing for many ocers, we lose a sig-
nificant proportion of our observations. We find no statistically significant evi-
dence that ocer education makes a dierence here, but we caution that our
estimates are noisy, at least partially due to the decreased sample size.
Finally, in Model , we examine whether any agency characteristics are cor-
related with whether a wandering ocer will be fired again. We add several char-
acteristics of the hiring agency for the year the ocer was hired, including the
proportion of the agency’s jurisdiction that is black, the proportion that is His-
panic, the violent and property crime rates per , residents, county-level
unemployment rates, and agency expenditures per ocer. We also test whether
the agency’s hiring or training requirements are correlated with whether the
agency will fire a wandering ocer it hires by adding two composite scores, de-
scribed in Section III.B, that measure the stringency of the hiring agency’s re-
quirements. The idea is that stricter hiring requirements might direct agencies
toward the least risky wandering ocers and that rigorous training require-
ments might keep wandering ocers on the right path. Finally, the model also
contains two variables capturing the number of full-time ocers employed by
the agencies the ocer moved to and from divided by (to ease interpretation
of the results). Because some of these agency-level variables are available only
starting in , we exclude all employment stints that began before that year.
We also lose some additional observations due to missingness on some of the
agency-level variables.
In this model, the variable indicating whether an ocer was fired from his
most recent job (rather than a job further back in his employment history) de-
creases in size and is no longer statistically significant. More important, the only
agency-level variables that are statistically significant relate to agency size; they
show that ocers who move from and to agencies that are smaller tend to be
fired less frequently. Otherwise, we find little evidence of any statistical associa-
tion between the agency variables and firing. When we examine firing rates
based on wandering ocers’ history of firings for misconduct, specifically, we
find a similar pattern of results.
256
Because Model is restricted to hirings from
to , however, the number of observations is substantially reduced,
which may partly explain the null results.
256. See Table A.
the yale law journal :
TABLE 12.
predicting whether wandering officers are fired again, 1988–2013
257
Model 1 Model 2 Model 3 Model 4
Intercept
0.074**
[0.007]
0.103**
[0.023]
0.075†
[0.042]
0.194**
[0.049]
Fired from
Last Job
0.037**
[0.010]
0.035**
[0.013]
0.043*
[0.020]
0.025
[0.020]
Job Number
2
0.009
[0.017]
-0.002
[0.030]
-0.008
[0.029]
3
0.005
[0.017]
-0.016
[0.029]
-0.003
[0.030]
4+
0.015
[0.019]
-0.002
[0.032]
-0.024
[0.030]
Age
(At Start)
-0.001
[0.001]
0
[0.001]
0
[0.001]
Male
-0.028
[0.018]
-0.031
[0.025]
-0.091**
[0.031]
Any Past
Complaints
0.006
[0.014]
-0.007
[0.019]
-0.011
[0.018]
Education
Associate’s
0.005
[0.028]
Bachelor’s
0.014
[0.027]
Master’s/
Doctorate
-0.011
[0.032]
257. An asterisk (*) denotes an estimate that is statistically significant at the . level; two aster-
isks (**) denote an estimate that is statistically significant at the . level. While our thresh-
old of statistical significance throughout the paper is ., we also note estimates that are
statistically significant at the . level with a dagger (†). Cluster-robust standard errors clus-
tered at the person-level are reported in brackets.
the wandering officer
Hiring
Agency
Proportion Black
-0.025
[0.050]
Proportion
Hispanic
-0.062
[0.041]
Violent-Crime
Rate
(Per 100,000)
0
[0.000]
Property-Crime
Rate
(Per 100,000)
0
[0.000]
County-Level
Unemployment
Rate
0.031
[0.313]
Expenditures Per
Officer (2008)
0
[0.000]
Hiring Re-
quirement Score
0.003
[0.005]
Training Re-
quirement Score
-0.007
[0.006]
No. of Officers
-0.003*
[0.001]
Separating
Agency
No. of Officers
-0.003**
[0.001]
n
3,533 3,533 1,390 1,451
Taken together, what should we make of these results? Assuming a law-en-
forcement agency will hire a wandering ocer, are there ways to reduce the
risks? Our models provide little optimism on this front. Most of the models sug-
gest that, among wandering ocers who are hired again, those who have sepa-
rated voluntarily from at least one job since their last firing are less likely to be
fired again. The models also suggest that ocers coming from or going to
smaller agencies may be slightly less likely to be fired. Beyond that, however, the
regression models provide little consistent evidence about how to predict which
wandering ocers will be fired again.
the yale law journal :
vi. mechanisms and reforms
We have found that an average of over one thousand wandering ocers were
working in full-time Florida law-enforcement jobs in any given year between
and . Many of these ocers found work by migrating to smaller, less
desirable agencies—measured by resources per ocer—than the agencies that
fired them. They were then fired and subjected to complaints alleging “moral
character violations” at higher rates than ocers who had never been fired be-
fore, even when we restrict the latter group to ocers who were hired around
the same time and place by similarly desirable agencies. Although we cannot be
sure why wandering ocers experienced these adverse outcomes, aer a review
of several hypotheses, we think the most straightforward explanation is also the
most plausible one: wandering ocers simply behave worse than ocers who
have never been fired. These results appear generally consistent with the perti-
nent labor economics literature and, in particular, with the closest study on
point, which concerns the labor mobility and behavior of financial advisers who
commit misconduct.
258
If wandering ocers are so risky, one might reasonably ask, why do police
chiefs keep hiring them? Our data do not permit us to isolate a single mecha-
nism, and we doubt that there is only one cause in any event. In this final Part,
we consider five plausible explanations along with the policy implications of
each. In Section A, we examine the possibility that agencies may not know they
are hiring wandering ocers. In Section B, we consider whether agencies may
not appreciate the risks that wandering ocers pose. In Section C, drawing upon
our empirical analysis in Section IV.B., we discuss whether wandering ocers
are simply the best available candidates—that is, if they are hired when no
stronger candidate is available. In Section D, we explore the benefits that might
make wandering ocers attractive notwithstanding their risks. Finally, in Sec-
tion E, we discuss the possibility that agencies are not concerned about the risks
of hiring wandering ocers because, generally speaking, they do not bear the
costs of any resulting harms.
A. Poor Information
In some instances, an agency may hire a wandering ocer simply because it
does not know about the ocer’s past. Indeed, agencies do not always complete
adequate background checks, possibly due to unprofessional organizational cul-
ture or resource constraints. The Cleveland Police Department, for instance,
“never reviewed [the] personnel file” for Tim Loehmann, who shot and killed
258. See supra notes - and accompanying text.
the wandering officer
Tamir Rice.
259
In addition, some candidates may deliberately conceal their pro-
fessional history. Much of the small academic literature on wandering ocers
emphasizes this explanation for the ability of wandering ocers to find work.
260
The favored solution here is to build a robust national decertification data-
base. Such a database would record state agency decisions to decertify ocers
and make them available for local agencies in other states to see. A small group
of academics has been pushing this idea for decades and, in , the President’s
Task Force on st Century Policing lent its imprimatur.
261
To be sure, a national
database does already exist—the NDI described in Part I. But its coverage is poor.
Some states “have decided it’s a drain on resources to contribute to the index
[and] the state-by-state discrepancies significantly limit the database’s eective-
ness.”
262
As one reference point, Georgia decertified ocers in but sub-
mitted nothing to the database.
263
Our data and findings highlight both the importance and limits of a national
decertification database as a tool to stop wandering ocers. On the one hand,
our finding that wandering ocers are more likely than other ocers to be fired,
including for misconduct, and more likely to be subject to serious misconduct
complaints, underscores the importance of some kind of national—and manda-
tory—tracking system. Such a tool could help agencies avoid hiring wandering
ocers who saunter in from other states.
On the other hand, as the Loehmann example demonstrates—along with all
of our own findings—the focus on interstate movement seems to skip a step.
Wandering ocers frequently move around within a single state. Even before
we get to any national database, background checks should be standardized and
259. Dewan & Oppel, supra note ; see CIVIL RIGHTS DIV., U.S. DEP’T OF JUSTICE & U.S. ATTORNEY’S
OFFICE FOR THE N. DIST. OF OHIO, INVESTIGATION OF THE CLEVELAND DIVISION OF POLICE
() (detailing systemic and structural deficiencies in the department). On the importance
of organizational culture more generally, see Armacost, supra note .
260. See, e.g., Atherley & Hickman, supra note , at - (describing impediments to the flow
of information necessary to vet candidates thoroughly); Bell, supra note , at (explaining
that “resource constraints make it more dicult” for agencies “to discriminate between good
and bad ocers”); Goldman & Puro, supra note , at (“[T]he second department may
be unaware of the previous misconduct, either because the first department would not dis-
close the ocer’s previous misconduct, or because the second department does not conduct a
thorough background check.”).
261. See, e.g., Atherley & Hickman, supra note , at -; Goldman & Puro, supra note , at -
; Goldman & Puro, supra note , at -; see also P
RESIDENT’S TASK FORCE ON ST CEN-
TURY
POLICING, supra note , at .
262. Fisher, supra note .
263. Id.
the yale law journal :
mandatory (as they are in some states, including Florida). There is some sug-
gestion, mentioned earlier, that background checks are taxing on very small or
resource-strapped departments. If so, states or the federal government should
consider subsidizing or otherwise assisting local agencies in conducting the nec-
essary investigation of ocer candidates.
In addition, even if the database includes every decertification decision na-
tionwide, it is useful only if states regularly decertify problem ocers. But they
do not.
264
Recall that five states plus the District of Columbia, employing a sig-
nificant share of all law-enforcement ocers nationally, have no decertification
option at all. Another twenty states require a criminal conviction before an ocer
can be decertified. And according to one study, in , over half of all police
decertifications reported to the NDI came from Florida and Georgia, while Lou-
isiana, Mississippi, and Wyoming did not decertify a single ocer
265
—in fact,
Louisiana has not decertified anyone in at least a decade.
266
Even in states that
do decertify a relatively high number of ocers, moreover, decertification is still
rare. Only a small subset of misconduct will render an ocer eligible for decer-
tification.
267
It is worth pointing out that, to our knowledge, none of the employ-
ment stints in our study—including those of wandering ocers—was held by an
ocer who had been decertified in Florida before the stint began.
268
The federal government—through an exercise of Congress’s spending
power—could encourage the states to decertify ocers under specified condi-
tions. Short of that, the most productive reform might be to expand the substan-
tive coverage of the national database. Rather than merely cover ocer decertifi-
cations, the database ought to record at least all misconduct-related terminations,
and probably all involuntary terminations or all separations of any kind. This
would resemble the National Practitioner Databank (NPDB) that tracks medical
264. See, e.g., Puro et al., supra note , at (“The decertification authority is available [in most
states] but may not be implemented in many states owing to factors such as a lack of political
will by the state legislature, insucient stang at the POST, or lack of participation by local
police chiefs or sheris.”).
265. Fisher, supra note .
266. Kelly et al., supra note .
267. See Fisher, supra note (“I think some people are under the misimpression that if a cop gets
fired for anything really bad, they’re going to get decertified, and that is not the case. . . . It’s
a very narrow range of behavior that will cause them to lose their certification.” (quoting Sue
Rahr, former police chief and member of the President’s Task Force on st Century Polic-
ing)).
268. It is possible that some had been decertified and later recertified. It is also possible that some
had been decertified in another state before moving to Florida.
the wandering officer
malpractice and fraud.
269
In fact, the NPDB goes further, requiring reporting of
not only employment separations but also malpractice payments and certain dis-
ciplinary actions.
270
In the s, Congress actually considered two bills that
sought to establish a federal clearinghouse of law-enforcement “employment
termination data.”
271
Both died in committee.
272
Some states have recently taken
steps in this direction for their in-state databases. A Illinois law, for exam-
ple, requires local agencies to notify the state POST board of any “final determi-
nation of willful violation of department or agency policy, ocial misconduct,
or violation of law” in connection with an ocer’s termination or resignation.
273
If other states enact similar reforms, an eective national database will be that
much closer to reality.
B. Unawareness of Risk
Some agencies may know they are hiring wandering ocers but may not
know that wandering ocers are, in general, risky hires.
274
Aer all, plenty of
wandering ocers do not reoend,
275
and plenty of ocers who have not been
fired before commit misconduct. In fact, police administrators sometimes make
the plausible argument that wandering ocers should be more conscientious
than others because they have been reprimanded before—they know, in other
269. National Practitioner Data Bank, U.S. DEP’T HEALTH & HUM. SERVS.,
https://www.npdb.hrsa.gov/index.jsp [https://perma.cc/GCA-WM]; see Goldman &
Puro, supra note , at .
270. See Ilene N. Moore et al., Rethinking Peer Review: Detecting and Addressing Medical Malpractice
Claims Risk, V
AND. L. REV. , ().
271. Law Enforcement and Correctional Ocers Employment Registration Act of , H.R.
, th Cong. () (); Law Enforcement and Correctional Ocers Employment
Registration Act of , S. , th Cong. () ().
272. Fisher, supra note .
273. Illinois Police Training Act, ILL. COMP. STAT. / .(a) (). But see Toner & Rutecki,
supra note (reporting in that only twenty-six ocers’ names had been submitted and
that ten of the twenty-six had gone on to find additional police work).
274. See, e.g., Cormier & Doig, supra note (describing a city manager who knowingly hired of-
ficers who had been fired for perjury, excessive force, and making false statements—but not
decertified—reasoning that, “[i]f the [certifying] commission says they’re good to go, they’re
good to go”); cf. Egan et al., supra note , at (hypothesizing that financial advisers with
records of misconduct may find work because unsophisticated customers do not know “how
to interpret” their records).
275. See, e.g., Shockey-Eckles, supra note , at - (relating the story of a wandering ocer
who “salvaged his career,” was named ocer of the year, and was promoted to lieutenant in
his new agency).
the yale law journal :
words, that they are not invincible.
276
Very few agencies, we suspect, hire enough
wandering ocers to notice that, in the aggregate, the pattern of their behavior
actually seems to cut the other way.
Police administrators are now on notice. Even when well intentioned—as a
second chance for a hard-working cop—hiring a wandering ocer is risky busi-
ness. Wandering ocers, we have shown, are fired and subjected to moral-char-
acter complaints more oen than other ocers. Notably, they are riskier, by our
measures, than even ocers hired as rookies. Our findings are consistent with
prior studies, using dierent data and methods, that examined police miscon-
duct without focusing on wandering ocers specifically. White and Kane, for
example, found that New York City ocers with “red flags” early in their careers
were at greatest risk of dismissal later on.
277
Machine-learning researchers who
developed an early intervention system to predict adverse events for Charlotte-
Mecklenburg ocers found that “[t]he most predictive features of the model
were those relevant to the prior [internal aairs] history of the ocer.”
278
And
Kyle Rozema and Max Schanzenbach found that Chicago ocers who receive
more civilian complaints are more likely to be sued later for civil-rights viola-
tions.
279
In light of this evidence, law-enforcement agencies should be wary of know-
ingly hiring wandering ocers and, when they do make such hires, should be
realistic about the risk they assume. Agencies should consider enhanced moni-
toring and support of wandering ocers as a potential way to manage this risk.
They might also promulgate recidivist penalties for ocers—common in the
substantive criminal law—designed to deter misconduct by this high-risk pop-
ulation. (Some may have already.) And if given the choice between two wander-
ing-ocer candidates, one of whom was just fired and one of whom was fired
earlier in his career, our evidence suggests that agencies ought to opt for the lat-
ter, all else equal.
C. Inadequate Alternatives
Many have assumed that hiring a wandering ocer is an obvious manage-
ment blunder. It certainly appears so aer the fact, when the public learns that
276. See Cormier & Doig, supra note .
277. Michael D. White & Robert J. Kane, Pathways to Career-Ending Police Misconduct: An Exami-
nation of Patterns, Timing, and Organizational Responses to Ocer Malfeasance in the NYPD,
C
RIM. JUST. & BEHAV. , ().
278. Carton et al., supra note , at .
279. Rozema & Schanzenbach, supra note , at -.
the wandering officer
an ocer who hurt someone had previously been fired by another agency. But
these cases alone, tragic as they are, actually tell us little about the wisdom of the
decision from an ex ante perspective. Even if agencies suspect that, on average,
wandering ocers carry certain risks, they might still be making optimal hiring
decisions under the circumstances. The wandering ocers who are hired might
be less risky than the available alternative candidates.
280
That wandering ocers
tend to migrate to agencies with fewer resources is evidence consistent with this
account. Certainly, if law-enforcement agencies were epistemically sophisti-
cated, profit-driven private entities that internalized the costs of bad hiring de-
cisions, this story would be compelling. As we discuss in Section E below, how-
ever, there are reasons to think most agencies are not like this.
Fortunately, we were able to do more than speculate in this way. We were
able, as detailed in Part IV above, to identify, for each wandering ocer, a plau-
sible candidate cohort—a group of ocers who were hired around the same time
by nearby, and similarly desirable, agencies. This group roughly approximates
the type of ocers who may have been candidates for the job the wandering
ocer ultimately obtained. As we showed, although this cohort is riskier than
the general population of ocers who have never been fired, the wandering of-
ficers are riskier still. It would be preferable, of course, to compare the wandering
ocers to the actual ocers who competed for the same jobs, but that infor-
mation is unavailable. In its absence, our cohort analysis provides some evidence
that agencies are not always making the optimal hiring decision.
Nevertheless, it remains possible that a shallow applicant pool does explain
some wandering-ocer hiring. If so, and assuming additional hires are actually
necessary, the solution may be to improve the pool of candidates by raising com-
pensation or improving outreach and recruitment.
281
This, of course, is likely far
easier said than done. Still, to the extent that ocials allocating budgets have not
280. See, e.g., Atherley & Hickman, supra note , at (explaining that small agencies sometimes
consciously hire wandering ocers when “financial resources are limited and lateral ocers
are simply scarce”); Goldman & Puro, supra note , at (“Although it might seem unusual
for a police department to hire an ocer with a past record of misconduct, the second depart-
ment is usually located in a poor community that cannot aord to pay high salaries to its
police.”); Cohen, supra note (“[I]f it’s not an attractive department, you’d have . . . very
few applicants, and it’s a matter of getting the best of the worst.” (quoting Vincent del Cas-
tillo)); Kyle Hopkins, The Village Where Every Cop Has Been Convicted of Domestic Violence,
P
ROPUBLICA (July , , : AM), https://www.propublica.org/article/stebbins-alaska
-cops-criminal-records-domestic-violence [https://perma.cc/TC-YFY] (describing small
Alaskan communities in which all applicants for law-enforcement jobs have criminal records).
281. Cf., e.g., Johnson, supra note (“L[i]ke the mayor, the chief is concerned about the town’s
ability to draw candidates to small-town policing ‘when you can make more at McDonalds.’”
(quoting Roger Dowell, Police Chief, Damascus, Va.)).
the yale law journal :
perceived a problem, our findings might encourage them to reconsider. Locali-
ties that are forced to hire wandering ocers might appeal to intergovernmental
sources of funding, such as the state, for assistance in subsidizing salary en-
hancements.
Another way to improve the candidate pool may be to reduce barriers to en-
try into the profession. This could include minimizing financial barriers, such as
the cost of the requisite training or education. Another possibility—although not
one without risks—is to relax the stringency of hiring requirements. Because re-
search finds that more educated ocers, for example, tend to commit less mis-
conduct, many assume that agencies should raise their education require-
ments.
282
But higher education requirements may screen out some otherwise-
excellent candidates—candidates who might be a superior choice to a better-ed-
ucated wandering ocer. More research is warranted on the eects of hiring re-
quirements on the makeup and performance of the police forces they yield.
D. Countervailing Benefits
Some agencies may understand that wandering ocers are risky—riskier,
even, than alternative candidates—but hire them because of the benefits, both
cultural and financial, they’re perceived to bring. The chief, for example, may be
looking for a “cowboy” ocer to work the toughest beat—someone who’s savvy
and unafraid to do what’s necessary to “clean up the streets.”
283
Some agency
leaders may even believe they are doing a service to the profession by helping a
cast-out comrade find his way. Unlike a new recruit, a wandering ocer has
earned his spot in policing’s “band of brothers”;
284
that he has been fired may
signal only that he was unfairly maligned or fell victim to “politics.”
285
The
chance to right this perceived wrong may generate for some chiefs a “warm
282. See, e.g., KANE & WHITE, supra note , at .
283. See, e.g., Toner & Rutecki, supra note (“Certain ocers are more active than other oc-
ers. . . . You have ocers that are out there simply looking for the bad elements. They are
looking for the criminals. They are looking for the drugs. They are looking for the guns out
there, which is what they should be doing.” (quoting Robert Collins Jr., Police Chief, Dolton,
Ill.)).
284. See WILLIAM SHAKESPEARE, KING HENRY V act , sc. (MIT ed. ) (“We few, we happy
few, we band of brothers; / For he to-day that sheds his blood with me / Shall be my
brother . . . .”).
285. Kelly et al., supra note (“Former New Orleans police superintendent Ronal Serpas said that
sheris and other chiefs oen justify rehiring ocers by dismissing their problems as ‘politi-
cal.’”).
the wandering officer
glow” that actually makes a wandering ocer more, not less, attractive than a
nonwandering alternative.
286
Alternatively, agencies might hire wandering ocers who are riskier than the
marginal hires if the costs of doing so are lower. Because collective-bargaining
agreements in many law-enforcement agencies across the country, and in Florida
specifically, establish scheduled pay raises based primarily on years of service,
police administrators typically have little discretion over salaries.
287
It is there-
fore unlikely that an administrator would hire a wandering ocer simply be-
cause that ocer is willing to accept a lower salary than a similarly experienced
marginal candidate who has never been fired. But wandering ocers might be
cheaper than rookies. In many jurisdictions, hiring agencies must pay to send
new recruits to the police academy and fund their salaries during training as
286. See, e.g., Cormier & Doig, supra note (“‘This stu is supposed to follow you forever? . . .
Of course I’m going to give somebody a second chance.’” (quoting Roberto Fulgueira, Police
Chief of the Sweetwater Police Department)); Schaefer & Kaufman, supra note (describing
police chief who considers his agency “a place where cops can earn a second chance”); cf. Maya
Lau & Matt Stiles, L.A. County Sheri Alex Villanueva Reinstates Four More Fired Deputies,
L.A.
TIMES (Apr. , ), https://www.latimes.com/local/lanow/la-me-sheri-more
-reinstatements--story.html [https://perma.cc/LC-MNV] (describing a sheri
who “argued that previous sheris were too harsh in punishing deputies and he wants to be
fairer” and who campaigned “on a promise to correct the wrongs of the past, including . . .
addressing the cases of deputies who’d been unfairly disciplined through a ‘truth and recon-
ciliation’ panel”). On the concept of “warm glow,” see James Andreoni, Giving with Impure
Altruism: Applications to Charity and Ricardian Equivalence, J.
POL. ECON. ().
287. See, e.g., Seth W. Stoughton, The Incidental Regulation of Policing, MINN. L. REV. ,
() (describing the New York Police Department’s wage schedule, in which “pay de-
pend[s] on the length of employment, not ocer performance”); Agreement Between the City
of Boca Raton and Fraternal Order of Police Lodge #35, C
ITY OF BOCA RATON (), http://
bocaraton.granicus.com/DocumentViewer.php?file=bocaraton_da
ebbe.pdf [https://perma.cc/PRH-QDBP]; Agreement Between the City of St. Peters-
burg and Sun Coast Police Benevolent Association for Police Ocers and Technicians, C
ITY OF ST.
PETERSBURG , (), https://www.stpete.org/city_departments/human_resources
/docs/PBAContract.pdf [https://perma.cc/NCN-PG]; City of Orlando Collective Bar-
gaining Agreement with Fraternal Order of Police, Orlando Lodge #25, C
ITY OF ORLANDO
- (), https://orlando.novusagenda.com/AgendaPublic/AttachmentViewer.ashx
?AttachmentID=&ItemID= [https://perma.cc/SP-KWX].
the yale law journal :
well.
288
Training also creates delay; wandering ocers join the agency ready to
deploy.
289
While not easy, it may be possible to diminish the influence of these per-
ceived benefits on agencies’ hiring decisions. With respect to the cultural factors,
scholars and even some police leaders are increasingly challenging the notion
that high crime—even violent crime—demands a militaristic response.
290
The
idea is that Guardian Ocers, not Warrior Cops, may best accomplish the aspi-
rational aim of law enforcement: “protecting civilians from unnecessary indig-
nity and harm.”
291
“[R]ethinking the professional self-image of policing and
changing some of the core values that inform ocers’ actions and decisions” will
not be easy,
292
but instructive examples do exist.
293
By foregrounding the service
288. See, e.g., Goldman & Puro, supra note , at (explaining that, when they hire wandering
ocers, “[d]epartments need not pay for the costs of a training academy or the salary of the
trainee while he is in training”); Williams, supra note (“[S]maller departments and those
that lack sucient funding or are understaed are most likely to hire applicants with prob-
lematic pasts if they have completed state-mandated training, which allows departments to
avoid the cost of sending them to the police academy.”). In Florida, for example, local agencies
are authorized, though not legally required, to pay police-academy tuition. See F
LA. STAT.
. (). Most do: according to CJAP data, in both and , of the re-
sponding police departments and sheris’ oces reimbursed tuition costs. As a point of ref-
erence, tuition at one program in ran roughly , for Florida residents and ,
for out-of-state residents. Law Enforcement Ocer, G
A. STONE TECH. COLL., https://
gstc-ecsd-fl.schoolloop.com/pf/cms/view_page?d=x&group_id=&vdid
=igyerxdmb [https://perma.cc/VTZ-HWT]. Given that so many agencies pay these
costs, it may be extremely dicult for agencies that do not to hire new recruits.
289. See, e.g., Williams, supra note (explaining that wandering ocers “can start work almost
immediately”); Yoder, supra note (“Given the opportunity to hire a licensed ocer who can
start immediately and for whom the hiring agency doesn’t need to pay training costs, [a small
department] may decide to ignore their history.”).
290. See, e.g., PRESIDENT’S TASK FORCE ON ST CENTURY POLICING, supra note , at ; Seth W.
Stoughton, Principled Policing: Warrior Cops and Guardian Ocers, W
AKE FOREST L. REV.
, - (); Kate Mather, LAPD Urges Ocers to Be Community Guardians, Not War-
riors on Crime, L.A.
TIMES (Aug. , , : AM), https://www.latimes.com/local/crime
/la-me-warrior-guardians--story.html [https://perma.cc/ABW-FZ]; Nick
Morgan, From Warriors to Guardians, M
AIL TRIB. (Medford, Or.) (Mar. , ),
http://www.mailtribune.com/article//NEWS/ [https://perma.cc
/ZNF-Z].
291. Stoughton, supra note , at .
292. Id. at .
293. See, e.g., Sue Rahr & Stephen K. Rice, From Warriors to Guardians: Recommitting American
Police Culture to Democratic Ideals, N
EW PERSPECTIVES IN POLICING BULL., Apr. , at -,
https://www.ncjrs.gov/pdles/nij/.pdf [https://perma.cc/DXL-NV] (describ-
ing training curriculum reforms in Washington designed to cultivate a guardian mentality by
the wandering officer
aspects of the job, these same reforms might help temper the “band of brothers”
mentality that may lead chiefs to pity, or at least identify with, wandering oc-
ers. Litigation targeting legal rules that reinforce a self-protective, insular mind-
set might, over time, gradually erode that mentality as well.
294
The perceived financial benefits—that wandering ocers are cheaper to hire
than new recruits—may be more straightforward to neutralize. If agencies are
hiring ocers with troubled histories to avoid the startup costs of educating and
training new recruits, then they should not be asked to pay these costs. If the
locality itself cannot allot more money to the police department’s budget, state
and federal authorities may need to intervene. The idea is not a fanciful one—
the federal government, for example, already supports local policing with tens
of billions of dollars. Rachel Harmon has argued that much of this money goes
to programs that are ineective or that may do more harm than good.
295
If that
is right, the suggestion here is simply to repurpose some of these funds to pay
for training rather than tanks.
296
To be clear, we do not mean to suggest that
agencies are correct to think that a wandering ocer is cheaper than a rookie in
the long run. Even if a wandering ocer is less costly upfront, what he gives
with one hand he may take away with the other, later, in the form of attrition
and potential civil liability. All told, the best approach may be to ensure that
agencies are internalizing these countervailing costs, a point to which we now
turn.
E. Cost Externalization
Finally, agencies may hire wandering ocers because they externalize, and
therefore discount, the costs of doing so—in other words, they know it’s risky
but they don’t care.
297
Agencies’ principal financial exposure comes from law-
suits against ocers for the harms their misconduct inflicts. Although these ac-
tions technically run against the defendant ocers as individuals, ocers are
emphasizing the nobility of policing, procedural justice, crisis intervention, tactical social in-
teraction, and respect).
294. See Aziz Z. Huq & Richard H. McAdams, Litigating the Blue Wall of Silence: How to Challenge
the Police Privilege to Delay Investigation, U.
CHI. LEGAL F. .
295. See Rachel A. Harmon, Federal Programs and the Real Costs of Policing, N.Y.U. L. REV. ,
, - ().
296. See id. at - (describing federal grant programs that finance the acquisition of militaristic
equipment).
297. See, e.g., Dill, supra note (“You have some agencies that take the approach, we need warm
bodies, so they will hire that [wandering] individual . . . .” (quoting Union County, South
Carolina Sheri David Taylor)).
the yale law journal :
virtually always indemnified by the employing locality.
298
Yet, even setting aside
the fact that many civilians wronged by the police will never sue, the locality
itself bears relatively limited exposure due to robust qualified immunity protec-
tions that immunize “all but the plainly incompetent or those who knowingly
violate the law.”
299
Moreover, even when the locality does incur liability on behalf of an ocer,
there are additional impediments to the generation of eective behavioral incen-
tives for those who run the agency. Roughly half of the agencies covered by one
recent study contribute nothing to the satisfaction of lawsuits brought against
them; central government funds are used to pay the bills.
300
Not all of the agen-
cies that do contribute, moreover, actually experience financial pressures; some,
for example, pay from funds that were earmarked for litigation costs alone.
301
All told, because of these complexities in the way localities finance liability costs,
the majority of agencies suer no financial consequences when liability costs in-
crease—they do not “feel the burn,” so to speak.
302
And though political pres-
sures may increase with liability, most agencies do not track or analyze infor-
mation about police litigation in a way that could facilitate learning and
improvement.
303
Municipalities are also exposed to direct liability for faulty hiring deci-
sions.
304
But the opening for plaintis is narrow, limited to situations in which
an ocer’s misconduct was a “plainly obvious consequence of the decision to
hire” the ocer.
305
That said, courts have upheld claims “where there is a close
connection between information a municipality did or should have learned about
an employee in the hiring process and the constitutional violation that ultimately
occurred.”
306
In one recent case, an ocer who had previously been fired from
298. See Joanna C. Schwartz, Police Indemnification, N.Y.U. L. REV. , ().
299. Malley v. Briggs, U.S. , (); see, e.g., Carbado, supra note , at -. But cf.
Joanna C. Schwartz, How Qualified Immunity Fails, Y
ALE L.J. , - () (finding that
qualified immunity rarely ends civil rights cases, although allowing that it may discourage
people from ever filing suit).
300. Schwartz, supra note , at .
301. Id. at .
302. See id. at .
303. See generally Joanna C. Schwartz, Myths and Mechanics of Deterrence: The Role of Lawsuits in
Law Enforcement Decisionmaking, UCLA
L. REV. () (finding that ocials do not
typically learn from civil-rights lawsuits to make informed decisions about how to modify
agency policy).
304. Bd. of the Cty. Comm’rs v. Brown, U.S. ().
305. Id. at .
306. AVERY ET AL., supra note , at ; see id. at n. (collecting cases).
the wandering officer
four separate law-enforcement agencies shot and killed the former mayor of a
small town in South Carolina. Shortly aer the incident, the town’s police chief
noted that, despite the ocer’s rocky past, he had “proven himself” at his current
job: “He’s done a good job, so I guess he got a second chance.”
307
The decedent’s
family sued, alleging, among other theories, negligent hiring by the town. A jury
awarded the family almost one hundred million dollars, including sixty million
in punitive damages against the town.
308
Given our evidence about the risks wandering ocers pose, the jury in a case
like this—or a court deciding a dispositive motion—is justified in deeming an
agency’s decision to hire a wandering ocer (or failure to conduct an adequate
background investigation) to be probative of fault. The more serious the ocer’s
past misconduct, the more probative the evidence is. Nevertheless, wandering-
ocer status remains but one risk factor among many and rarely should be dis-
positive.
309
Increased judicial willingness to entertain negligent-hiring suits concerning
wandering ocers might, on the margins, aect the frequency with which wan-
dering ocers are hired. Yet the impediments just discussed—to translating fi-
nancial liability into constructive behavioral incentives—are every bit as power-
ful here as in the case of individual ocer liability. To the extent these
impediments contribute to the hiring of wandering ocers, the appropriate re-
sponse would be to improve the system’s mechanisms of accountability so that
agencies internalize the costs of their hiring decisions. But this is hardly a novel
suggestion; indeed, it is clear by now there is no straightforward solution. Po-
lice-liability insurers might pitch in by converting the “large but improbable po-
tential liabilities” of negligent hiring into more salient premium dollars.
310
By
raising premiums for agencies that hire wandering ocers, for example, insurers
would force local governments to pay for the incremental increase in risk that
307. Harve Jacobs, Cop Accused of Shooting Ex-Mayor Had Previous Encounter, Chief Says, WCSC
(June , , : AM), http://www.livenews.com/story//sled-investigate-ocer
-involved-shooting-in-cottageville [https://perma.cc/UYD-VLHL].
308. See Heath Hamacher, A Matter of Force: $97.5M Jury Award Trains a Spotlight on the Issue of Law
Enforcement Hiring, S.C.
LAW. WKLY. (Oct. , ), https://sclawyersweekly.com/news
////a-matter-of-force--m-jury-award-trains-a-spotlight-on-the-issue-of-law
-enforcement-hiring [https://perma.cc/RZG-U]. The parties later settled for mil-
lion. South Carolina Mayor’s Death Settlement Reduced to $10M, I
NS. J. (Mar. , ), https://
www.insurancejournal.com/news/southeast////.htm [https://perma.cc
/QVN-MD].
309. See supra Part II (reviewing research on other ocer-level correlates of misconduct).
310. John Rappaport, How Private Insurers Regulate Public Police, HARV. L. REV. ,
().
the yale law journal :
wandering ocers present regardless whether that risk ultimately material-
ized.
311
Here, too, however, according special treatment to wandering-ocer
status may make most sense if other ocer-level risk factors are also considered.
Of course, many costs of wrongful policing are nonpecuniary in nature. Po-
lice misconduct humiliates and degrades its subjects, creates racial disparities in
criminal-justice outcomes, causes negative health consequences, and breeds cyn-
icism toward the police—which, in turn, can “stymie or hinder public safety ef-
forts and, instead, keep crime rates higher in the same communities where fair
and just policing practices are most needed.”
312
Agencies externalize many of
these nonpecuniary costs as well.
313
Improving transparency ought to allow the
public to monitor the police more eectively and thus to exert pressure on polit-
ical actors to account for these neglected costs of policing.
314
In the meantime,
federal pattern-or-practice lawsuits might help to achieve organizational change
where financial penalties do not. The federal government could consider the hir-
ing of wandering ocers, for example, when determining whether and how to
target particular agencies under U.S.C. .
315
Barring successful general-accountability reforms, and if future research cor-
roborates our findings, states could consider the “nuclear option” that Connect-
icut invoked in . Connecticut law now prohibits any local agency from hiring
311. See id.; see also id. at (describing “feature rating” of insurance policies, the practice of
“charging more to riskier customers . . . based on the presence of traits correlated with riski-
ness”).
312. Marie Ouellet et al., Network Exposure and Excessive Use of Force: Investigating the Social Trans-
mission of Police Misconduct, C
RIMINOLOGY & PUB. POL’Y (); see Harmon, supra note
, at -. As Harmon points out, lawful policing can impose many of these costs as well.
Harmon, supra note , at .
313. See, e.g., Harmon, supra note , at (explaining that, “[f]or local governments to func-
tion as a check on the nonbudgetary costs of policing, the public must be able to monitor and
attribute responsibility for the harm the police do, and political actors must be able to influ-
ence police conduct,” and highlighting ways that federal policing programs “undermine these
preconditions for local accountability”).
314. See, e.g., Barry Friedman & Maria Ponomarenko, Democratic Policing, N.Y.U. L. REV. ,
() (“The people must be able to see what their agents are doing so they can evaluate
those actions and exercise control as necessary.”).
315. Some of the important sources on include Rachel A. Harmon, Promoting Civil Rights
Through Proactive Policing Reform, S
TAN. L. REV. (); Debra Livingston, Police Reform
and the Department of Justice: An Essay on Accountability, B
UFF. CRIM. L. REV. ();
Stephen Rushin, Federal Enforcement of Police Reform, F
ORDHAM L. REV. (); and
Samuel Walker, The New Paradigm of Police Accountability: The U.S. Justice Department Pattern
or Practice Suits in Context, S
T. LOUIS U. PUB. L. REV. (). For more information, see
also C
IVIL RIGHTS DIV., U.S. DEP’T OF JUSTICE, THE CIVIL RIGHTS DIVISION’S PATTERN AND
PRACTICE POLICE REFORM WORK: -PRESENT ().
the wandering officer
an ocer who was previously employed by another Connecticut agency and who
“() was dismissed for malfeasance or other serious misconduct calling into
question such person’s fitness to serve as a police ocer; or () resigned or re-
tired from such ocer’s position while under investigation for such malfeasance
or other serious misconduct.”
316
We are not prepared, on the basis of our evi-
dence alone, to oer a full-throated defense of the nuclear option, but it is cer-
tainly a plausible backstop, and it could be useful to study Connecticut’s experi-
ence to develop a better sense of the costs and benefits of going down this road.
conclusion
Not all those who wander are lost, but in policing, many are. In any given
year over the last three decades, an average of roughly , full-time law-en-
forcement ocers in Florida walk the streets having been fired in the past, and
almost having been fired for misconduct, not counting the many who were
fired and reinstated in arbitration. These ocers, we have shown, are subse-
quently fired and subjected to “moral character” complaints at elevated rates rel-
ative to both ocers hired as rookies and veterans with clean professional histo-
ries. And we likely underestimate the prevalence of the phenomenon nationwide.
We have, moreover, only a partial understanding of the extent of the problem
wandering ocers pose. Beyond their own misbehavior, wandering ocers may
undermine eorts to improve police culture, as they carry their baggage to new
locales. Worse yet, wandering ocers may “infect” other ocers upon arrival,
317
causing misconduct to metastasize to the farthest reaches of the law-enforcement
community. Future research should investigate these possibilities.
316. CONN. GEN. STAT. -c(a) ().
317. Cf. Ouellet et al., supra note (suggesting that peers influence whether an individual ocer
will engage in misconduct); Edika G. Quispe-Torreblanca & Neil Stewart, Causal Peer Eects
in Police Misconduct, N
ATURE HUM. BEHAV. () (estimating peer eects in police mis-
conduct); Daria Roithmayr, The Dynamics of Excessive Force, U.
CHI. LEGAL F. (argu-
ing that use of excessive force should be thought of as contagious); Thibaut Horel et
al., The Contagiousness of Police Violence (unpublished manuscript), https://www.law
.uchicago.edu/files/-/chicago_contagiousness_of_violence.pdf [https://perma.cc
/FX-XJ] (studying whether police shootings are “contagious”).
the yale law journal 129:1676 2020
1772
appendix
FIGURE A1.
proportion of separations in which officer obtains subsequent employment
within three years, by professional history of firing for misconduct, 1988-
2013
the wandering officer
TABLE A1.
frequency distribution of raw separation codes, 1988-2016
Separation Code
Frequency
Proportion
Fired
Fired for
Misconduct
Administrative - Unfavorable
(Historical Use Only)
928 0.009 Yes Yes
Misconduct
(Historical Use Only)
584 0.006 Yes Yes
No Cause for Decertification
(Historical Use Only)
90 0.001 Yes Yes
Resigned - Would Not Rehire
(Historical Use Only)
7 0 Yes Yes
Resigned/Retired in Lieu of Separation for
Violating Agency/Training Center Policy
270 0.003 Yes Yes
Resigned/Retired in Lieu of Separation for
Violating Moral Character Standards
216 0.002 Yes Yes
Resigned/Retired While Being Investigated
for Violating Moral Character Standards
1,027 0.01 Yes Yes
Resigned/Retired While Being Investigated
for Violating Agency Policy
1,039 0.01 Yes Yes
Terminated for Violating Agency Policy
(No Moral Character Violation)
1,143 0.011 Yes Yes
Terminated for Violating Ch. 943.13(4),
FS or Moral Character Standards
1,048 0.01 Yes Yes
Under Investigation
(Historical Use Only)
304 0.003 Yes Yes
Failure to Complete Basic
Recruit Training
476 0.005 Yes
Failure to Complete Elder
Abuse Training
114 0.001 Yes
Failure to Meet Mandatory
Retraining Requirement
438 0.004 Yes
Failure to Pass State
Certification Examination
70 0.001 Yes
Failure to Perform Assigned
Tasks Satisfactorily
283 0.003 Yes
Failure to Qualify with Firearm 225 0.002 Yes
the yale law journal :
Failure to Satisfactorily Complete Agency
Field-Training Program
1,083 0.011 Yes
Involuntary Separation
(Historical Use Only)
2 0 Yes
Other - Excessive Absence, Fail Report for
Duty, Sleep on Duty, Etc.
141 0.001 Yes
Staff Termination
(Historical Use Only)
106 0.001 Yes
Administrative Separation
(Not Involving Misconduct)
1,087 0.011
Budgetary Constraints 86 0.001
Deceased 707 0.007
Extended Leave of Absence 17 0
Extended Leave of Absence or Suspension
(Historical Use Only)
135 0.001
Leave of Absence
(Historical Use Only)
10 0
Military Leave of Absence 138 0.001
Not Separated 39,344 0.39
Processed Fingerprints Not Received
Within One Year
364 0.004
Resigned/Retired
(Historical Use Only)
19 0
Retired
(Not Involving Misconduct)
7,845 0.078
Special Elected or Appointed Position 28 0
Suspension 13 0
Temporary Employment Authorization
(Period Exceeded)
48 0
Transfer Within Agency
(No Break in Service)
5,658 0.056
Voluntary Separation
(Not Involving Misconduct)
35,675 0.354
the wandering officer
TABLE A2.
descriptive statistics of cjap hiring requirements, 1997-2016
Mean SD Min Max n
Age
18 0.01 0.12 0 1 6,539
19 0.64 0.48 0 1 6,539
20 0.02 0.13 0 1 6,539
21 0.33 0.47 0 1 6,539
22
0 0.07 0 1 6,539
Education
High School/
GED
0.9 0.3 0 1 6,581
Associate’s/
Some College
0.09 0.29 0 1 6,581
Bachelor’s 0.01 0.08 0 1 6,581
Criminal-Justice
Experience
0.07 0.26 0 1 6,575
Tobacco
Requirement
0.26 0.44 0 1 6,575
Driving Test
0.79 0.41 0 1 5,252
Interview
0.92 0.27 0 1 6,585
Physical-Ability
Test
0.49 0.5 0 1 6,570
Polygraph Test
0.5 0.5 0 1 6,564
Psychological
Test
0.75 0.43 0 1 6,562
Selection Exam
0.54 0.5 0 1 6,575
Swimming Test
0.09 0.29 0 1 6,254
Vision Test
0.61 0.49 0 1 6,570
Voice Test
0.23 0.42 0 1 5,906
Probation Period
(In Months)
11.5 2.72 0 24 5,811
Composite
Requirement Score
4.61 1.96 0 9 6,469
the yale law journal :
TABLE A3.
descriptive statistics of cjap training variables, 1997-2016
Mean SD Min Max n
Chemical
Agents
Not Required 0.43 0.49 0 1 6,207
Every 24-48 Months 0.21 0.41 0 1 6,207
Every 6-12 Months 0.36 0.48 0 1 6,207
Self-
Defense
Not Required 0.39 0.49 0 1 6,530
Every 24-48 Months 0.17 0.38 0 1 6,530
Every 6-12 Months 0.44 0.50 0 1 6,530
Driving
Not Required 0.59 0.49 0 1 6,521
Every 24-48 Months 0.20 0.40 0 1 6,521
Every 6-12 Months 0.20 0.40 0 1 6,521
Firearm
Not Required 0.01 0.09 0 1 6,550
Every 12-48 Months 0.89 0.31 0 1 6,550
Every 6 Months 0.10 0.30 0 1 6,550
In-Service
Not Required 0.85 0.36 0 1 5,192
Every 24-48 Months 0.02 0.14 0 1 5,192
Every 6-12 Months 0.13 0.34 0 1 5,192
Medical
Not Required 0.31 0.46 0 1 6,527
Every 24-48 Months 0.49 0.50 0 1 6,527
Every 6-12 Months 0.21 0.40 0 1 6,527
FTO
11.96 5.91 0 52 6,486
Continued
0.68 0.47 0 1 5,887
Composite
Training Score
2.91 1.44 0 6 6,094
the wandering officer
TABLE A4.
median time to new employment by demographic groups and professional
history, 1988-2013
Panel A: Fired Panel B: Fired for Misconduct
Never
Fired
Fired,
Last Job
Fired,
Earlier
Job
Never
Fired
Fired,
Last Job
Fired,
Earlier
Job
Race
White
19
(19,280)
318
(1,175)
57
(1,024)
21
(19,977)
427
(800)
70
(702)
Black
14
(2,049)
401
(210)
9
(198)
17
(2,224)
457
(129)
6
(104)
Hispanic
11
(2,519)
470
(212)
14
(154)
12
(2,649)
553
(137)
21
(99)
Gender
Male
15
(21,451)
335
(1,441)
27
(1,288)
17
(22,327)
442
(986)
46
(867)
Female
61
(2,750)
406
(180)
55
(98)
75
(2,892)
692
(92)
36
(44)
Education
High School
18
(937)
509
(55)
158
(36)
26
(977)
664
(30)
269
(21)
Associate’s
16
(3,401)
322
(247)
42
(179)
18
(3,538)
498
(162)
70
(127)
Bachelor’s
12
(6,982)
327
(347)
11
(270)
13
(7,236)
404
(210)
15
(153)
Master’s
18
(1,436)
376
(58)
75
(69)
19
(1,491)
642
(33)
90
(39)
All
17
(24,205)
350
(1,621)
28
(1,386)
18
(25,223)
450
(1,078)
46
(911)
TABLE A5.
subsequent firing by professional history, 1996-2013
Fired Fired for Misconduct
n Ever 3 Years Ever 3 Years
Panel A: Firings
Never 21,693 7.6% 4.0% 5.3% 2.3%
Fired, last job 1,200 16.8% 10.8% 11.3% 6.7%
Fired, earlier job 1,143 13.9% 7.0% 10.1% 5.0%
Panel B: Firings
for Misconduct
Never 22,603 7.8% 4.1% 5.4% 2.4%
Fired, last job 761 18.1% 11.3% 12.6% 7.2%
Fired, earlier job 672 14.4% 7.4% 10.1% 5.4%
Panel C: Rookie
39,007 9.4% 5.4% 6.0% 2.5%
the yale law journal :
TABLE A6.
subsequent firing by professional history with one-, three-, and five-year
time windows, 1988-2013
318
Fired Fired for Misconduct
n 1-Year 3-Year 5-Year 1-Year 3-Year 5-Year
Panel A:
Firings
Never 29,888 2.7% 4.5% 6.0% 1.5% 3.0% 4.3%
Fired,
last job
1,969 7.3% 11.2% 13.7% 4.8% 7.9% 9.8%
Fired,
earlier job
1,631 4.6% 7.5% 9.9% 3.2% 5.6% 7.5%
Panel B:
Firings
for Mis-
conduct
Never 31,182 2.8% 4.7% 6.1% 1.6% 3.1% 4.4%
Fired,
last job
1,297 7.7% 12.2% 14.8% 5.4% 9.0% 11.2%
Fired,
earlier job
1,009 5.2% 8.1% 10.7% 3.6% 6.2% 8.1%
Panel C:
Rookie
54,476 3.8% 5.8% 7.3% 1.6% 3.2% 4.5%
TABLE A7.
subsequent firing by professional history for matched comparators based
on timing, geography, and agency desirability, 1996-2013
Fired Fired for Misconduct
Ever 3 Years Ever 3 Years
Panel A: Firings
Never
10% 5.4% 7.1 3.3
Fired, last job
Wanderer 16.7% 10.3% 11.3% 6.2%
Comparator 9.03% 5.3% 6.2% 2.9%
Fired, earlier job
Wanderer 13.8% 6.7% 10.1% 5.0%
Comparator 9.8% 5.4% 6.4% 2.7%
Panel B: Firings
for Misconduct
Never
10.1% 5.5% 7.2% 3.3%
Fired, last job
Wanderer 17.5% 10.8% 12.2% 6.7%
Comparator 9% 5.3% 6.0% 2.9%
Fired, earlier job
Wanderer 14.5% 7.2% 10.2% 5.4%
Comparator 9.6% 5.3% 6% 2.6%
318. For the -year time window, we exclude all job stints beginning aer to ensure that we
have at least years of follow-up for each observation. The sample sizes are thus slightly
smaller for these estimates.
the wandering officer
TABLE A8.
number of complaints by professional history with one-, three-, and five-
year time windows, 1993-2013
n 1-Year 3-Year 5-Year
All Complaints
Panel A: Firings
Never 24,711 0.01 0.02 0.04
Fired, last job 1,394 0.02 0.07 0.09
Fired, earlier job 1,295 0.02 0.05 0.06
Panel B: Firings
for Misconduct
Never 25,686 0.01 0.02 0.04
Fired, last job 934 0.03 0.08 0.10
Fired, earlier job 780 0.02 0.05 0.07
Panel C: Rookie Panel C: Rookie 44,584 0.01 0.03 0.04
Violent and
Sexual Com
p
laints
Panel A: Firings
Never 24,711 0.00 0.01 0.01
Fired, last job 1,394 0.01 0.02 0.03
Fired, earlier job 1,295 0.01 0.01 0.02
Panel B: Firings
for Misconduct
Never 25,686 0.00 0.01 0.01
Fired, last job 934 0.01 0.03 0.03
Fired, earlier job 780 0.01 0.01 0.02
Panel C: Rookie Panel C: Rookie 44,584 0.00 0.01 0.01
Integrity Complaints
Panel A: Firings
Never 24,711 0.00 0.01 0.01
Fired, last job 1,394 0.01 0.03 0.03
Fired, earlier job 1,295 0.01 0.02 0.03
Panel B: Firings
for Misconduct
Never 25,686 0.00 0.01 0.01
Fired, last job 934 0.01 0.03 0.04
Fired, earlier job 780 0.01 0.02 0.03
Panel C: Rookie Panel C: Rookie 44,584 0.00 0.01 0.01
the yale law journal :
TABLE A9.
number of complaints by professional history for matched comparators
based on timing, geography, and agency desirability, 1996-2013
All
Violent/
Sexual
Integrity
Ever
3
Years
Ever
3
Years
Ever
3
Years
Panel A:
Firings
Never
0.08 0.02 0.02 0.01 0.03 0.01
Fired,
last job
Wanderer 0.13 0.06 0.04 0.02 0.05 0.02
Comparator 0.08 0.03 0.02 0.01 0.03 0.01
Fired,
earlier job
Wanderer 0.13 0.06 0.03 0.01 0.05 0.02
Comparator 0.07 0.03 0.01 0.01 0.03 0.01
Panel B:
Firings for
Misconduct
Never
0.08 0.02 0.02 0.01 0.03 0.01
Fired,
last job
Wanderer 0.16 0.07 0.04 0.02 0.06 0.03
Comparator 0.08 0.04 0.02 0.01 0.03 0.01
Fired,
earlier job
Wanderer 0.14 0.06 0.03 0.01 0.06 0.02
Comparator 0.07 0.02 0.01 0.01 0.03 0.01
TABLE A10.
predicting if wandering officers are fired for misconduct again, 1988-2013
Model 1 Model 2 Model 3 Model 4
Intercept
0.062**
[0.008]
0.060†
[0.030]
0.011
[0.056]
0.114*
[0.054]
Fired from
Last Job
0.029*
[0.011]
0.036*
[0.015]
0.063*
[0.026]
0.021
[0.022]
Job
Number
2
0
[0.023]
-0.021
[0.049]
-0.03
[0.037]
3
0.007
[0.023]
-0.009
[0.049]
-0.002
[0.038]
≥ 4
0.023
[0.026]
0.016
[0.053]
-0.012
[0.039]
Age
(At Start)
-0.001
[0.001]
0
[0.001]
-0.001
[0.001]
Male
0.007
[0.021]
-0.005
[0.034]
-0.031
[0.036]
Any Past
Complaints
-0.002
[0.013]
-0.011
[0.020]
-0.006
[0.017]
the wandering officer
Education
Associate’s
0.033
[0.024]
Bachelor’s
0.051*
[0.024]
Master’s/Doctorate
0.047
[0.034]
Hiring
Agency
Proportion Black
-0.086
[0.062]
Proportion Hispanic
-0.043
[0.039]
Violent-Crime Rate
(Per 100,000)
0
[0.000]
Property-Crime Rate
(Per 100,000)
0
[0.000]
County-Level
Unemployment Rate
0.129
[0.365]
Expenditures Per Officer
(2008)
0
[0.000]
Hiring Requirement
Score
-0.001
[0.004]
Training Requirement
Score
-0.001
[0.007]
Number of Officers
-0.002
[0.001]
Separating
Agency
Number of Officers
-0.001
[0.001]
n
2,272 2,272 848 882
TABLE A11.
number of fdle-initiated complaints by professional history, 1993-2013
n Ever 3 Years
Panel A: Firings
Never 24,711 0.02 0.01
Fired, last job 1,394 0.04 0.02
Fired, earlier job 1,295 0.05 0.02
Panel B: Firings
for Misconduct
Never 25,686 0.03 0.01
Fired, last job 934 0.05 0.02
Fired, earlier job 780 0.06 0.02
Panel C: Rookies
44,584 0.03 0.01
the yale law journal :
TABLE A12.
subsequent firings and number of complaints by professional history and
agency size, 1988-2013
319
Firings
Firings for
Misconduct
Complaints
Size
n Ever 3 Yr. Ever 3 Yr. n Ever 3 Yr.
≤15
Officers
Never 2,295 10% 6% 7% 4% 1,822 0.07 0.04
Fired, last job 413 21% 12% 15% 9% 314 0.12 0.09
Fired, earlier job 268 19% 10% 15% 9% 210 0.12 0.06
Rookie 2,001 12% 7% 9% 5% 1,554 0.07 0.03
>15
Officers
Never 27,593 9% 4% 7% 3% 22,889 0.07 0.02
Fired, last job 1,556 18% 11% 13% 8% 1,080 0.14 0.06
Fired, earlier job 1,363 14% 7% 10% 5% 1,085 0.12 0.04
Rookie 52,475 10% 6% 7% 3% 43,030 0.08 0.03
TABLE A13.
subsequent firings and number of complaints by professional history and
timing, 1993-2009
320
Firing
Firing for
Misconduct
Complaints
n
Years
1-3
Years
4-7
Years
1-3
Years
4-7
n
Years
1-3
Years
4-7
Panel A:
Firings
Never 26,346 4.6% 2.3% 3.2% 2.0% 21,169 0.024 0.024
Fired,
last job
1,811 11.2% 4% 8% 3.4% 1,236 0.070 0.038
Fired,
earlier job
1,487 7.5% 4% 5. 9% 2.8% 1,151 0.050 0.042
Panel B:
Firings
for Mis-
conduct
Never 27,516 4.8% 2.4% 3.3% 2.0% 22,020 0.025 0.024
Fired,
last job
1,198 12.1% 4.4% 9.1% 3.7% 835 0.079 0.044
Fired,
earlier job
930 8.3% 4% 6.6% 2.6% 701 0.056 0.054
Panel C:
Rookie
47,371 5.8% 2.4% 3.3% 2.1% 37,479 0.026 0.033
319. The firing data is based on employment stints beginning between and , and the
complaint data is based on stints beginning between and .
320. We exclude all employment stints that began aer to ensure that we have a full seven-
year window for each employment stint through which to observe firings and complaints.
