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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.3" xml:lang="en" article-type="research-article"><?properties open_access?><processing-meta base-tagset="archiving" mathml-version="3.0" table-model="xhtml" tagset-family="jats"><restricted-by>pmc</restricted-by></processing-meta><front><journal-meta><journal-id journal-id-type="nlm-ta">Police Q</journal-id><journal-id journal-id-type="iso-abbrev">Police Q</journal-id><journal-id journal-id-type="hwp">sppqx</journal-id><journal-id journal-id-type="publisher-id">PQX</journal-id><journal-title-group><journal-title>Police Quarterly</journal-title></journal-title-group><issn pub-type="ppub">1098-6111</issn><issn pub-type="epub">1552-745X</issn><publisher><publisher-name>SAGE Publications</publisher-name><publisher-loc>Sage CA: Los Angeles, CA</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmc">9806200</article-id><article-id pub-id-type="publisher-id">10.1177_10986111221148217</article-id><article-id pub-id-type="doi">10.1177/10986111221148217</article-id><article-categories><subj-group subj-group-type="heading"><subject>Original Article</subject></subj-group></article-categories><title-group><article-title>The Supply and Demand Shifts in Policing at the Start of the
Pandemic: A National Multi-Wave Survey of the Impacts of COVID-19 on American
Law Enforcement</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid" authenticated="false">https://orcid.org/0000-0002-1919-3987</contrib-id><name><surname>Lum</surname><given-names>Cynthia</given-names></name><xref rid="aff1-10986111221148217" ref-type="aff">1</xref><xref rid="corresp1-10986111221148217" ref-type="corresp"/></contrib><contrib contrib-type="author"><name><surname>Maupin</surname><given-names>Carl</given-names></name><xref rid="aff2-10986111221148217" ref-type="aff">2</xref></contrib><contrib contrib-type="author"><name><surname>Stoltz</surname><given-names>Megan</given-names></name><xref rid="aff2-10986111221148217" ref-type="aff">2</xref></contrib><aff id="aff1-10986111221148217"><label>1</label>Department of Criminology, Law and
Society, <institution-wrap><institution-id institution-id-type="Ringgold">3298</institution-id><institution content-type="university">George Mason University</institution></institution-wrap>, Fairfax, VA, USA</aff><aff id="aff2-10986111221148217"><label>2</label>Professional Development and
Engagement, <institution-wrap><institution-id institution-id-type="Ringgold">122560</institution-id><institution content-type="university">International Association of Chiefs
of Police</institution></institution-wrap>, Alexandria, VA, USA</aff></contrib-group><author-notes><corresp id="corresp1-10986111221148217">Cynthia Lum, Department of Criminology, Law
and Society, George Mason University, 4400 University Drive, MS 6D12, Fairfax,
VA 22030, USA. Email: <email>clum@gmu.edu</email></corresp></author-notes><pub-date pub-type="epub"><day>27</day><month>12</month><year>2022</year></pub-date><pub-date pub-type="pmc-release"><day>27</day><month>12</month><year>2022</year></pub-date><!--PMC Release delay is 0 months and 0 days and was based on the <pub-date pub-type="epub"/>.--><elocation-id>10986111221148217</elocation-id><permissions><copyright-statement>&#x000a9; The Author(s) 2022</copyright-statement><copyright-year>2022</copyright-year><copyright-holder content-type="sage">SAGE Publications</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><license-p>This article is made available via the PMC Open Access Subset for
unrestricted re-use and analyses in any form or by any means with
acknowledgement of the original source. These permissions are granted for
the duration of the COVID-19 pandemic or until permissions are revoked in
writing. Upon expiration of these permissions, PMC is granted a perpetual
license to make this article available via PMC and Europe PMC, consistent
with existing copyright protections.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="1148217.pdf"/><abstract><p>We report the results of the only multi-wave survey of a large and geographically
diverse sample of police agencies across the United States to understand the
immediate impacts of the COVID-19 pandemic on law enforcement. Findings indicate
an unprecedented and sustained shift in both the supply of and demand for police
services during that time. While overall calls for service (demand) tended to
decline in most agencies, some experienced increases in specific categories of
calls. During the early months of COVID, agencies also reduced their in-person
response to calls for service, arrests, proactive policing, and community
policing activities (supply). These findings indicate a substantial change in
the public safety landscape during that time, which was experienced by agencies
of all sizes and from all types of jurisdictions. We explore how public health
pandemics can lead to substantial, immediate, and potentially sustained changes
to police deployment and police-community interactions that may impact public
safety goals.</p></abstract><kwd-group><kwd>COVID</kwd><kwd>police</kwd><kwd>calls for service</kwd><kwd>proactive policing</kwd><kwd>community policing</kwd></kwd-group><custom-meta-group><custom-meta><meta-name>edited-state</meta-name><meta-value>corrected-proof</meta-value></custom-meta><custom-meta><meta-name>typesetter</meta-name><meta-value>ts10</meta-value></custom-meta></custom-meta-group></article-meta></front><body><p>The year 2020 will historically be remembered as when the highly contagious SARS-CoV-2
virus, commonly known as COVID-19, infected the world. As early as January 17, 2020, the
first reported case of COVID-19 in the United States emerged in the state of Washington.
By the end of January 2020, the U.S. Secretary of Health and Human Services declared the
virus a public health emergency. The number and spread of cases began accelerating at
the end of February 2020. On March 11, 2020, COVID-19 was officially declared a pandemic
by the World Health Organization. On March 13, then-U.S. President Trump declared a
national emergency, and by the end of March 2020, deaths from COVID-19 had begun
exponentially increasing. By early April, at least 42 states had implemented
stay-at-home orders and social distancing guidance.<sup><xref rid="fn1-10986111221148217" ref-type="fn">1</xref></sup> In over two-and-a-half years in the
United States alone, there have been over 97.6 million reported cases of COVID-19
infections and over 1 million deaths.<sup><xref rid="fn2-10986111221148217" ref-type="fn">2</xref></sup> In 2021 (as in 2020), COVID-19 was
the third leading cause of death in the United States, behind heart disease and
cancer.<sup><xref rid="fn3-10986111221148217" ref-type="fn">3</xref></sup> As a
pandemic and public health crisis, COVID-19 has been one of the worst infectious
diseases modern humanity has known.</p><p>COVID-19 and its related public health emergency declarations led to dramatic changes in
everyday life. With reductions and shutdowns in public and private activities, people
began sheltering indoors, working and schooling from home, and venturing outside
sparingly for supplies. Transportation and tourism came to a standstill with satellite
images showing entire highways and cities void of vehicle and pedestrian traffic.
Hospitals, nursing homes, and morgues bore the brunt of the immediate impact of COVID,
overwhelmed by sickness and death. Throughout 2020, there would be dramatic shifts in
employment and commerce, from lost jobs to how people labored. The pandemic and its
response further exacerbated pre-existing social inequalities that have been well
documented, including racial and ethnic disparities (see the systematic review by <xref rid="bibr34-10986111221148217" ref-type="bibr">Mude et al., 2021</xref>),<sup><xref rid="fn4-10986111221148217" ref-type="fn">4</xref></sup> gender inequality
(<xref rid="bibr11-10986111221148217" ref-type="bibr">Fisher &#x00026; Ryan,
2021</xref>; <xref rid="bibr32-10986111221148217" ref-type="bibr">Mooi-Reci &#x00026;
Risman, 2021</xref>), and relatedly, socio-economic disparities (<xref rid="bibr8-10986111221148217" ref-type="bibr">Clouston et al., 2021</xref>; <xref rid="bibr12-10986111221148217" ref-type="bibr">Fiske et al., 2022</xref>).
Deteriorating mental health, drug use, alcoholism, and overdoses were additional
consequences of these social changes (<xref rid="bibr9-10986111221148217" ref-type="bibr">Czeisler et al., 2021</xref>; <xref rid="bibr23-10986111221148217" ref-type="bibr">Linas et al., 2021</xref>; <xref rid="bibr28-10986111221148217" ref-type="bibr">Martellucci et al., 2021</xref>).</p><p>The significant impact of the pandemic on every aspect of life included unprecedented
changes in the criminal justice system (for an overview, see <xref rid="bibr37-10986111221148217" ref-type="bibr">National Commission on COVID-19 and Criminal Justice,
2020</xref>). For example, because of social distancing guidance and the risk of
contagion and death, court services attempted to reduce person-to-person contact and
postponed hearings and trials,<sup><xref rid="fn5-10986111221148217" ref-type="fn">5</xref></sup> leading to extensive downstream backlog impacts and changes in
court operations (<xref rid="bibr3-10986111221148217" ref-type="bibr">Baldwin et al.,
2020</xref>; <xref rid="bibr7-10986111221148217" ref-type="bibr">Chan, 2021</xref>;
<xref rid="bibr15-10986111221148217" ref-type="bibr">Godfrey et al., 2022</xref>;
<xref rid="bibr19-10986111221148217" ref-type="bibr">Jurva, 2021</xref>; <xref rid="bibr49-10986111221148217" ref-type="bibr">Witte &#x00026; Berman, 2021</xref>).
Correctional systems also adjusted, attempting to restrict intake and hasten release to
reduce the incarcerated population and alleviate the spread of COVID-19 in confinement
(<xref rid="bibr6-10986111221148217" ref-type="bibr">Carson et al., 2022</xref>;
<xref rid="bibr16-10986111221148217" ref-type="bibr">Hawks et al., 2020</xref>;
<xref rid="bibr27-10986111221148217" ref-type="bibr">Marcum, 2020</xref>).<sup><xref rid="fn6-10986111221148217" ref-type="fn">6</xref></sup> Parole and probation
supervision and treatment services were also affected, with officers and providers
unable to meet with their clients (<xref rid="bibr45-10986111221148217" ref-type="bibr">Schwalbe &#x00026; Koetzle, 2021</xref>; <xref rid="bibr48-10986111221148217" ref-type="bibr">Viglione et al., 2020</xref>).</p><p>Some of the most immediate impacts of the pandemic were on police officers and first
responders. Unlike schools and other public or private services, law enforcement could
not shut down or transition to remote work. Police still needed to respond to public
safety concerns and COVID medical emergencies. At the same time, police leaders
immediately realized the risk of COVID to their workforces. These concerns were
reflected in the immediate mobilization of the large national policing organizations to
provide COVID-related information. For example, the Police Executive Research Forum
(PERF) began a &#x0201c;daily report&#x0201d; on March 17, 2020, in which readers could see real-time
comments by police executives about some of their current challenges and
activities.<sup><xref rid="fn7-10986111221148217" ref-type="fn">7</xref></sup>
On March 19, the National Policing Institute (formerly the National Police Foundation)
released a briefing for law enforcement in collaboration with the Center for Disease
Control (CDC). The International Association of Chiefs of Police (IACP) also began
several efforts to monitor the developing situation, consult with expert practitioners,
and inform and advise police agencies on emerging issues.<sup><xref rid="fn8-10986111221148217" ref-type="fn">8</xref></sup> The current study&#x02019;s surveys were part
of that effort.</p><p>Given these significant changes that COVID imposed on life and criminal justice, what
would be the immediate impacts of the pandemic on law enforcement? To research this
question in real-time, the authors and their respective organizations partnered and
mobilized quickly once COVID was declared a pandemic in March 2020 to track police
agencies&#x02019; reactions to COVID. The initial purpose of the surveys was to capture the
pandemic&#x02019;s impact on law enforcement as it was unfolding to provide police agencies with
real-time assessments and fact sheets<sup><xref rid="fn9-10986111221148217" ref-type="fn">9</xref></sup> to inform their practices.</p><sec id="sec1-10986111221148217"><title>The Impact of Covid-19 on Law Enforcement Agencies</title><p>During COVID researchers have conducted empirical surveys assessing police officer
perceptions and agency reactions to the pandemic. While these have been on limited
samples or occurred after the beginning months of COVID or the murder of George
Floyd,<sup><xref rid="fn10-10986111221148217" ref-type="fn">10</xref></sup>
they offer several insights. Early studies, for example, focused on the impact of
the pandemic on officer perceptions rather than agency operations. <xref rid="bibr13-10986111221148217" ref-type="bibr">Frenkel et al. (2021)</xref>
surveyed officers from six agencies in five European countries between March and
June to understand officer stress, strain, emotional regulation, and preparedness
for the pandemic. They found that officers tolerated stress fairly well (conditioned
by other factors), although the risk of officers getting COVID and poor agency
communication exacerbated officer stress. Between July and September 2020, <xref rid="bibr21-10986111221148217" ref-type="bibr">Kyprianides et al. (2021)</xref>
surveyed 325 officers in the United Kingdom to ask about their policing experiences
during COVID-19. They found that positive organizational support was associated with
use of force restraint, procedural justice policing, and better officer health.
However, <xref rid="bibr21-10986111221148217" ref-type="bibr">Kyprianides et al.
(2021)</xref> also discovered that greater officer self-confidence was
associated with poorer health and more support for police use of force during the
pandemic. Additionally, in March 2021, <xref rid="bibr30-10986111221148217" ref-type="bibr">Mask&#x000e1;ly et al. (2022)</xref> asked 167 officers
and non-executives from seven U.S. agencies about operational and organizational
changes due to COVID-19. Their results show substantial heterogeneity in how
officers viewed organizational policies about the pandemic, both within the same
organization and across different organizations. Overall, officers experienced the
pandemic differently, and this experience was likely conditioned by characteristics
of their agency and their own health concerns.</p><p>At the organizational level, although there have been anecdotal accounts of the
impacts of COVID-19 on individual agencies (see, e.g., <xref rid="bibr18-10986111221148217" ref-type="bibr">Jennings &#x00026; Perez, 2020</xref>),<sup><xref rid="fn11-10986111221148217" ref-type="fn">11</xref></sup> systematic
surveys across agencies were scarce during the first year of the pandemic. We found
only three, and none captured changes at the beginning of the pandemic. For example,
informed by our surveys, <xref rid="bibr29-10986111221148217" ref-type="bibr">Mask&#x000e1;ly et al. (2021)</xref> queried police executives using international
contacts and received responses from people from 27 countries (they did not report
how many individuals responded or from what agencies). The authors noted their
survey was sent in &#x0201c;the summer of 2020&#x0201d; (p. 271). As with their officer survey
mentioned above, their findings were highly heterogeneous, indicating that changes
in organizational operations varied across responses. However, they found some
consistent changes, including decreases in in-person training and roll calls,
restrictions to public access to police agencies, and increases in remote work. It
was unclear from the survey how the worldwide protests for police reform during the
time of their survey confounded these findings.</p><p><xref rid="bibr33-10986111221148217" ref-type="bibr">Mrozla (2021)</xref>, also
building off of our surveys, queried approximately 2500 rural agencies in the United
States (serving populations of 10,000 or less) between May and September 2020,
focusing on how rural police agencies responded to the pandemic. Using email
addresses obtained from agency websites, they received responses from 312 rural
agencies. Mrozla found that agency size may be positively correlated with having
policies related to pandemics. They also noted the specific challenges of personal
protection equipment (PPE) provisions for rural agencies, officer shortages, and the
need for risk management and preparation for these agencies. Mrozla importantly
finds that contrary to some beliefs, rural agencies may not be immune to pandemics
and should also prepare for them.</p><p>A third survey was reported in <italic toggle="yes">Security Magazine</italic> by <xref rid="bibr10-10986111221148217" ref-type="bibr">Ekici and Alexander
(2021)</xref>. They surveyed agencies post-Floyd starting in June 2020 in Illinois,
Missouri, and Ohio (they did not report their survey methodology). They received
responses from 73 agencies in Illinois, 30 in Missouri, and 97 in Ohio. <xref rid="bibr10-10986111221148217" ref-type="bibr">Ekici and Alexander (2021)</xref>
noted significant reductions in these agencies in enforcement, arrest, traffic and
pedestrian stops, police training (including academy shut-downs), and community
access to police officers.</p><p>We believe that our surveys remain the only effort to document&#x02014;in real time&#x02014;the
immediate (March-May 2020) impacts of COVID-19 at the start of the pandemic and
before Floyd&#x02019;s death, for a large and diverse sample of law enforcement agencies
across the U.S. Capturing the immediate impacts of COVID-19 on a wide range of
agencies, especially during the first two months of the pandemic, most accurately
showcases the initial challenges that U.S. law enforcement agencies face in a
rapidly evolving public health crisis with regard to their daily public safety
deployment. This understanding is crucial to better planning, preparedness, and
response in the future.</p></sec><sec id="sec2-10986111221148217"><title>Survey Implementation and Data Collection</title><p>The challenges of surveying a representative sample of the over 18,000 U.S. law
enforcement agencies have been well-documented in the national policing survey
research (see, e.g., challenges of the Bureau of Justice Statistics&#x02019; Law Enforcement
Management and Administrative Statistics (LEMAS) survey).<sup><xref rid="fn12-10986111221148217" ref-type="fn">12</xref></sup> Further, surveying agencies
quickly and during a crisis certainly dims prospects of success. Thus, at the start
of the pandemic in March 2020, the first author partnered with the International
Association of Chiefs of Police (IACP) leadership to create and implement the
surveys. The IACP is the world&#x02019;s largest professional organization for police
leaders, whose tens of thousands of members from around the world include over 6400
chief executives from approximately 5800 agencies in the United States and Canada
(with the vast majority from the U.S.). In addition, the chief executive members of
IACP are from a wide array of urban, suburban, and rural agencies dispersed across
the United States. Using the IACP membership agencies was, therefore, the quickest
way to obtain the most immediate estimate of the initial impact of COVID-19 on a
large sample of diverse law enforcement agencies in North America.</p><p>The IACP agreed to implement two waves of surveys, one from March 25 &#x02013; April 3, 2020,
and the second from May 12 &#x02013; 25, 2020. The surveys were officially sent by IACP to
all of its U.S. and Canadian chief executives by email, and a Qualtrics link was
provided for agencies to fill out the survey online. Two additional reminders were
sent within the period each survey was open. As more than one individual who
received the survey request might have served in an executive leadership role in the
same agency, recipients were given explicit instructions that only one survey was to
be filled out for each agency by the chief executive with direct knowledge of
operational adjustments due to COVID-19. Recipients were also frequently reminded
across each survey of the &#x0201c;as of&#x0201d; date for questions, which was two days before each
survey&#x02019;s release. Specifically, for Wave 1 (released on March 25, 2020), respondents
were asked to provide answers &#x0201c;as of March 23, 2020.&#x0201d; For Wave 2 (released on May
12, 2020), respondents were asked to respond with answers &#x0201c;as of May 10, 2020.&#x0201d; To
ensure that the murder of George Floyd and subsequent protests did not impact agency
responses, we did not include any surveys received on or after May 25, 2020, the
last day of the second survey implementation. Only two responses were removed
because of this reason.</p><p>As the survey was implemented at the start of the pandemic, it was difficult to
determine precisely the relevant questions to ask. Because of this, the March and
May surveys were not identical, although many questions were similar. For example,
in the first survey, we focused on changes to agency operations, agency
preparedness, changes to the civilian workforce, and the impacts of stay-at-home
orders. In the second wave, we continued to ask about these changes but also
inquired about trends of specific categories of calls for service and budgetary
concerns.<sup><xref rid="fn13-10986111221148217" ref-type="fn">13</xref></sup></p><p>For the first wave, 989 agency representatives completed and returned surveys after
two reminders (reflecting a 17% agency-level response rate). For this analysis, we
removed the 11 Canadian agency responses given the different country context of
those surveys (although they were included in the initial fact sheets distributed to
the agencies). For the second wave, 1141 surveys were completed and returned after
two reminders (reflecting a 20% agency-level response rate). Ten of these were from
Canadian agencies, which, again, were removed for this analysis. While checks across
the data (I.P. addresses, location, state, number of officers in the agency) did not
indicate multiple responses from potentially the same agency, the anonymous and
voluntary nature of the survey made this impossible for us to determine with 100%
certainty (for example, in both Wave 1 and 2 surveys, approximately 7% of
respondents did not provide the state where their agency was located). However,
given our checks, we believe the responses represent unique and individual law
enforcement agencies in the United States but note this possible source of error. We
also do not know if the same agencies answered both surveys and can only estimate
impacts based on a cross-section of the two samples taken from the same
population.</p><p><xref rid="table1-10986111221148217" ref-type="table">Table 1</xref> shows the
proportion of agency responses across various characteristics, including the number
of sworn officers, civilian personnel, population served, and U.S. region.
Approximately three-quarters of agencies in the United States have fewer than 25
sworn officers, and only 5% have over 100 officers (<xref rid="bibr17-10986111221148217" ref-type="bibr">Hyland &#x00026; Davis, 2019</xref>). Thus, agencies
with 100 or more officers are overrepresented in both waves of this survey.
Nonetheless, we captured a substantial proportion of agencies with 25 officers or
less (41% in Wave 1; 32% in Wave 2). Additionally, at least half of the agencies
participating in each survey serve populations of 25,000 or less, showing that we
captured a large sample of (likely) rural agencies. In the second survey, a larger
proportion of agencies with more than 50 officers serving places with a population
of more than 25,000 answered the survey compared to smaller agencies with smaller
populations. Participating agencies represented every state and Bureau of Economic
Analysis region of the United States.<table-wrap position="float" id="table1-10986111221148217"><label>Table 1.</label><caption><p>U.S. Agencies that Responded to Each Survey.</p></caption><alternatives><graphic xlink:href="10.1177_10986111221148217-table1" specific-use="table1-10986111221148217" position="float"/><table frame="hsides" rules="groups"><thead valign="top"><tr><th align="left" rowspan="1" colspan="1"/><th align="left" rowspan="1" colspan="1">Wave 1 (<italic toggle="yes">n</italic> = 978)</th><th align="left" rowspan="1" colspan="1">Wave 2 (<italic toggle="yes">n</italic> = 1131)</th></tr></thead><tbody valign="top"><tr><td align="left" colspan="3" rowspan="1">Sworn officers</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Less than 25</td><td align="char" char="." rowspan="1" colspan="1">41.3%</td><td align="char" char="." rowspan="1" colspan="1">32.1%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;25-49</td><td align="char" char="." rowspan="1" colspan="1">24.1%</td><td align="char" char="." rowspan="1" colspan="1">23.5%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;50-99</td><td align="char" char="." rowspan="1" colspan="1">14.6%</td><td align="char" char="." rowspan="1" colspan="1">18.1%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;100-499</td><td align="char" char="." rowspan="1" colspan="1">11.3%</td><td align="char" char="." rowspan="1" colspan="1">14.9%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;500-1000</td><td align="char" char="." rowspan="1" colspan="1">1.4%</td><td align="char" char="." rowspan="1" colspan="1">2.9%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;1000 or more</td><td align="char" char="." rowspan="1" colspan="1">1.1%</td><td align="char" char="." rowspan="1" colspan="1">3.4%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Missing responses</td><td align="char" char="." rowspan="1" colspan="1">6.0%</td><td align="char" char="." rowspan="1" colspan="1">5.0%</td></tr><tr><td align="left" colspan="3" rowspan="1">Civilian employees</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Less than 10</td><td align="char" char="." rowspan="1" colspan="1">56.0%</td><td align="char" char="." rowspan="1" colspan="1">46.9%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;10-19</td><td align="char" char="." rowspan="1" colspan="1">14.3%</td><td align="char" char="." rowspan="1" colspan="1">16.9%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;20-29</td><td align="char" char="." rowspan="1" colspan="1">7.9%</td><td align="char" char="." rowspan="1" colspan="1">9.0%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;30-49</td><td align="char" char="." rowspan="1" colspan="1">5.3%</td><td align="char" char="." rowspan="1" colspan="1">7.3%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;50-99</td><td align="char" char="." rowspan="1" colspan="1">4.8%</td><td align="char" char="." rowspan="1" colspan="1">6.4%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;100 or more</td><td align="char" char="." rowspan="1" colspan="1">5.1%</td><td align="char" char="." rowspan="1" colspan="1">8.6%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Missing responses</td><td align="char" char="." rowspan="1" colspan="1">6.3%</td><td align="char" char="." rowspan="1" colspan="1">5.0%</td></tr><tr><td align="left" colspan="3" rowspan="1">Population served</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Less than 25,000</td><td align="char" char="." rowspan="1" colspan="1">60.1%</td><td align="char" char="." rowspan="1" colspan="1">50.9%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;25,000 - 49,999</td><td align="char" char="." rowspan="1" colspan="1">14.6%</td><td align="char" char="." rowspan="1" colspan="1">17.0%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;50,000 - 99,999</td><td align="char" char="." rowspan="1" colspan="1">8.9%</td><td align="char" char="." rowspan="1" colspan="1">11.6%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;100,000 - 249,999</td><td align="char" char="." rowspan="1" colspan="1">5.1%</td><td align="char" char="." rowspan="1" colspan="1">6.5%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;250,000 - 499,999</td><td align="char" char="." rowspan="1" colspan="1">1.4%</td><td align="char" char="." rowspan="1" colspan="1">2.2%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;500,000 - 999,999</td><td align="char" char="." rowspan="1" colspan="1">1.2%</td><td align="char" char="." rowspan="1" colspan="1">2.7%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;1 million or more</td><td align="char" char="." rowspan="1" colspan="1">2.5%</td><td align="char" char="." rowspan="1" colspan="1">4.0%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Missing responses</td><td align="char" char="." rowspan="1" colspan="1">6.1%</td><td align="char" char="." rowspan="1" colspan="1">5.2%</td></tr><tr><td align="left" colspan="3" rowspan="1">Bureau of economic analysis
regions<sup><xref rid="table-fn1-10986111221148217" ref-type="table-fn">a</xref></sup>
based on state provided</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Great lakes</td><td align="char" char="." rowspan="1" colspan="1">20.2%</td><td align="char" char="." rowspan="1" colspan="1">17.6%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Mideast</td><td align="char" char="." rowspan="1" colspan="1">18.5%</td><td align="char" char="." rowspan="1" colspan="1">17.4%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Southeast</td><td align="char" char="." rowspan="1" colspan="1">14.9%</td><td align="char" char="." rowspan="1" colspan="1">16.1%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;New england</td><td align="char" char="." rowspan="1" colspan="1">10.8%</td><td align="char" char="." rowspan="1" colspan="1">10.7%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Southwest</td><td align="char" char="." rowspan="1" colspan="1">8.7%</td><td align="char" char="." rowspan="1" colspan="1">8.0%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Far west</td><td align="char" char="." rowspan="1" colspan="1">8.0%</td><td align="char" char="." rowspan="1" colspan="1">10.5%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Plains</td><td align="char" char="." rowspan="1" colspan="1">7.7%</td><td align="char" char="." rowspan="1" colspan="1">7.9%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Rocky mountain</td><td align="char" char="." rowspan="1" colspan="1">3.8%</td><td align="char" char="." rowspan="1" colspan="1">4.9%</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Missing responses</td><td align="char" char="." rowspan="1" colspan="1">7.4%</td><td align="char" char="." rowspan="1" colspan="1">6.9%</td></tr></tbody></table></alternatives><table-wrap-foot><fn id="table-fn1-10986111221148217"><p><sup>a</sup>See <ext-link xlink:href="https://apps.bea.gov/regional/docs/msalist.cfm?mlist=2" ext-link-type="uri">https://apps.bea.gov/regional/docs/msalist.cfm?mlist=2</ext-link>.</p></fn></table-wrap-foot></table-wrap></p></sec><sec id="sec3-10986111221148217"><title>Immediate Impacts of Covid-19 on U.S. Police Agencies</title><p>Given space limitations, we do not present all of the findings from both surveys
here. However, we present several main results, focusing on how the pandemic
significantly changed the <italic toggle="yes">demand</italic> and <italic toggle="yes">supply</italic> for
police services. By &#x0201c;demand,&#x0201d; we mean the public&#x02019;s expectation of safety and police
legitimacy, often manifested through the public&#x02019;s calls for police service but also
from other community requests to the police. By &#x0201c;supply,&#x0201d; we mean the resources and
activities the police use to meet that demand for public safety. These can include
police response to calls for service, enforcement activities, proactivity, and
community engagement.</p><sec id="sec4-10986111221148217"><title>Changes in Requests for Police Service</title><p>It would be near impossible to obtain and analyze calls for service data across
the thousand agencies in our samples to understand their calls for service
trends. So instead, we asked agencies if they had experienced changes in their
overall volume of calls for service received (demand). At the end of March,
during the first survey, 57% percent of responding agencies reported
experiencing significant declines (10&#x02013;50%) in their calls for service, with an
additional 14% of respondents noting that calls dramatically decreased by more
than 50%. We then asked a more specific question in our second survey,
requesting that agencies estimate increases or decreases (5&#x02013;20%, greater than
20%) or no change for different call types, comparing their calls for service
volumes during the previous month of April 2020 with those in April 2019. <xref rid="table2-10986111221148217" ref-type="table">Table 2</xref> shows the
specific breakdown of the estimates provided in the second survey. Cell
proportions are bolded when a quarter or more of responding agencies reported
increases, no change, or decreases.<table-wrap position="float" id="table2-10986111221148217"><label>Table 2.</label><caption><p>Percent of Responding U.S. Agencies in Wave 2 (<italic toggle="yes">N</italic> =
1131) Experiencing an Increase or Decrease in Certain Types of
Events in April 2020 Compared to April 2019.</p></caption><alternatives><graphic xlink:href="10.1177_10986111221148217-table2" specific-use="table2-10986111221148217" position="float"/><table frame="hsides" rules="groups"><thead valign="top"><tr><th align="left" rowspan="1" colspan="1"/><th align="left" rowspan="1" colspan="1">Increase &#x0003e;20%</th><th align="left" rowspan="1" colspan="1">Increase 5&#x02013;20%</th><th align="left" rowspan="1" colspan="1">Stayed About Same, %</th><th align="left" rowspan="1" colspan="1">Decrease 5&#x02013;20%</th><th align="left" rowspan="1" colspan="1">Decrease&#x0003e;20%</th></tr></thead><tbody valign="top"><tr><td align="left" rowspan="1" colspan="1">Overall calls for service</td><td align="char" char="." rowspan="1" colspan="1">1.8%</td><td align="char" char="." rowspan="1" colspan="1">6.3%</td><td align="char" char="." rowspan="1" colspan="1">15.6%</td><td align="char" char="." rowspan="1" colspan="1"><bold>41.9%</bold></td><td align="char" char="." rowspan="1" colspan="1"><bold>33.9%</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Domestic incidents (violent and
non-violent)</td><td align="char" char="." rowspan="1" colspan="1">8.5%</td><td align="char" char="." rowspan="1" colspan="1"><bold>34.3%</bold></td><td align="char" char="." rowspan="1" colspan="1"><bold>37.5%</bold></td><td align="char" char="." rowspan="1" colspan="1">12.5%</td><td align="char" char="." rowspan="1" colspan="1">7.1%</td></tr><tr><td align="left" rowspan="1" colspan="1">Violent crimes, generally</td><td align="char" char="." rowspan="1" colspan="1">2.7%</td><td align="char" char="." rowspan="1" colspan="1">10.5%</td><td align="char" char="." rowspan="1" colspan="1"><bold>40.7%</bold></td><td align="char" char="." rowspan="1" colspan="1"><bold>28.7%</bold></td><td align="char" char="." rowspan="1" colspan="1">16.5%</td></tr><tr><td align="left" rowspan="1" colspan="1">Commercial burglaries</td><td align="char" char="." rowspan="1" colspan="1">3.5%</td><td align="char" char="." rowspan="1" colspan="1">11.8%</td><td align="char" char="." rowspan="1" colspan="1"><bold>45.3%</bold></td><td align="char" char="." rowspan="1" colspan="1">23.7%</td><td align="char" char="." rowspan="1" colspan="1">14.4%</td></tr><tr><td align="left" rowspan="1" colspan="1">Traffic crashes and fatalities</td><td align="char" char="." rowspan="1" colspan="1">1.1%</td><td align="char" char="." rowspan="1" colspan="1">5.0%</td><td align="char" char="." rowspan="1" colspan="1"><bold>24.9%</bold></td><td align="char" char="." rowspan="1" colspan="1"><bold>38.3%</bold></td><td align="char" char="." rowspan="1" colspan="1"><bold>29.6%</bold></td></tr><tr><td align="left" rowspan="1" colspan="1">Calls related to mental distress</td><td align="char" char="." rowspan="1" colspan="1">9.3%</td><td align="char" char="." rowspan="1" colspan="1"><bold>37.6%</bold></td><td align="char" char="." rowspan="1" colspan="1"><bold>40.6%</bold></td><td align="char" char="." rowspan="1" colspan="1">8.0%</td><td align="char" char="." rowspan="1" colspan="1">3.8%</td></tr></tbody></table></alternatives><table-wrap-foot><fn><p>Less than 1% of responses were missing for each type of call for
service.</p></fn></table-wrap-foot></table-wrap></p><p>As <xref rid="table2-10986111221148217" ref-type="table">Table 2</xref>
indicates, by May 2020, over three-quarters of responding agencies had
experienced reductions in demands for their services from community members,
with one-third of respondents reporting that this decrease was substantial (more
than 20%). Regarding specific categories of calls for service asked in the
second survey, agencies were most likely to see decreases (when comparing April
2019 with April 2020) in traffic crashes and fatalities, and for some, violent
crimes and commercial burglaries (although a significant minority of agencies
reported stable trends in these crime types). Some agencies experienced
increases in specific categories, most notably domestic violence and mental
health calls, which we will return to shortly. The overall relationships between
the reported trends experienced between each category of calls were positive and
significant, as shown in <xref rid="table3-10986111221148217" ref-type="table">Table 3</xref>, which reports Kendall&#x02019;s tau-b for each of these
relationships. Generally, when an agency experienced a decline in overall calls
for service, this decline was often felt across nearly all types of calls. No
significant relationships between the experienced trends of all calls or
specific categories of calls and population size or number of sworn officers
were found,<sup><xref rid="fn14-10986111221148217" ref-type="fn">14</xref></sup>
indicating that the decline in overall requests for police services was a shared
experience across various sizes of police agencies and populations.<table-wrap position="float" id="table3-10986111221148217"><label>Table 3.</label><caption><p>Relationships between Trends Experienced for each Calls for Service
Category Compared to all other Calls for Service Category (Kendall&#x02019;s
tau-b Displayed for Ordinal by Ordinal Relationships).</p></caption><alternatives><graphic xlink:href="10.1177_10986111221148217-table3" specific-use="table3-10986111221148217" position="float"/><table frame="hsides" rules="groups"><thead valign="top"><tr><th align="left" rowspan="1" colspan="1"/><th align="left" rowspan="1" colspan="1">Overall Calls</th><th align="left" rowspan="1" colspan="1">Domestic related</th><th align="left" rowspan="1" colspan="1">Violence</th><th align="left" rowspan="1" colspan="1">Comm. burglary</th><th align="left" rowspan="1" colspan="1">Traffic crashes</th><th align="left" rowspan="1" colspan="1">Mental distress</th></tr></thead><tbody valign="top"><tr><td align="left" rowspan="1" colspan="1">Overall calls</td><td align="char" char="." rowspan="1" colspan="1">1.000</td><td align="char" char="." rowspan="1" colspan="1">0.230</td><td align="char" char="." rowspan="1" colspan="1">0.339</td><td align="char" char="." rowspan="1" colspan="1">0.285</td><td align="char" char="." rowspan="1" colspan="1">0.317</td><td align="char" char="." rowspan="1" colspan="1">0.131</td></tr><tr><td align="left" rowspan="1" colspan="1">Domestic related</td><td align="left" rowspan="1" colspan="1"/><td align="char" char="." rowspan="1" colspan="1">1.000</td><td align="char" char="." rowspan="1" colspan="1">0.307</td><td align="char" char="." rowspan="1" colspan="1">0.240</td><td align="char" char="." rowspan="1" colspan="1">0.140</td><td align="char" char="." rowspan="1" colspan="1">0.405</td></tr><tr><td align="left" rowspan="1" colspan="1">Violence</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="char" char="." rowspan="1" colspan="1">1.000</td><td align="char" char="." rowspan="1" colspan="1">0.464</td><td align="char" char="." rowspan="1" colspan="1">0.281</td><td align="char" char="." rowspan="1" colspan="1">0.200</td></tr><tr><td align="left" rowspan="1" colspan="1">Comm. Burglary</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="char" char="." rowspan="1" colspan="1">1.000</td><td align="char" char="." rowspan="1" colspan="1">0.232</td><td align="char" char="." rowspan="1" colspan="1">0.172</td></tr><tr><td align="left" rowspan="1" colspan="1">Traffic crashes</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="char" char="." rowspan="1" colspan="1">1.000</td><td align="char" char="." rowspan="1" colspan="1">0.115</td></tr><tr><td align="left" rowspan="1" colspan="1">Mental distress</td><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="left" rowspan="1" colspan="1"/><td align="char" char="." rowspan="1" colspan="1">1.000</td></tr></tbody></table></alternatives><table-wrap-foot><fn><p>All tau-b statistics shown have approximate significance of
<italic toggle="yes">p</italic> &#x0003c; .01.</p></fn></table-wrap-foot></table-wrap></p><p>There are some interesting caveats to these findings. As <xref rid="table2-10986111221148217" ref-type="table">Table 2</xref> shows, 43% of responding
agencies experienced <italic toggle="yes">increases</italic> in domestic incidents, and 47%
of responding agencies experienced <italic toggle="yes">increases</italic> in calls related
to people in mental distress. <xref rid="table3-10986111221148217" ref-type="table">Table 3</xref> also indicates a stronger
relationship between agencies reporting increases in domestic-related calls for
service and mental distress calls. Similarly, stronger relationships were found
between those reporting increases in violence and commercial burglaries. Several
ordered logistic regression models run separately for each call category against
all other call categories, population size, and the number of sworn officers
confirmed these results and continued to confirm that jurisdiction or agency
size was not a factor in these trends. In total, these findings suggest that
while the overall demand for public safety declined in the first two months of
COVID, some agencies did experience increases in certain categories (and
groupings of categories) of calls.</p></sec><sec id="sec5-10986111221148217"><title>Changes to Agencies&#x02019; Response to Calls for Service</title><p>Not only did the overall volume of calls for police service decline at the start
of the pandemic, but police agencies also decided&#x02014;often by policy&#x02014;to reduce
their in-person response (supply) to some calls for service. As <xref rid="table4-10986111221148217" ref-type="table">Table 4</xref> shows, a
substantial proportion of agencies in the first and second surveys (43% and 45%,
respectively) reported that they were no longer responding in person to more
than 20% of calls for service that they would have normally responded to in
person before the pandemic.<sup><xref rid="fn15-10986111221148217" ref-type="fn">15</xref></sup> In addition, by March 23, 91% of agency respondents had
already provided their patrol officers with formal criteria and guidance as to
when officers were required (or not) to respond to calls for service in person.
This increased to 95% in Wave 2. Combined with the reduction in calls for
service, this reflects a substantial (and historic) decline in day-to-day
interactions that police officers had with people during the early stages of the
pandemic and a remarkable shift in how police officers typically respond to
dispatched calls for service. While there have been times when agencies have
chosen not to respond temporarily to certain calls for service in person (e.g.,
during the terrorist attacks of September 11, 2001, or during natural
disasters), this policy decision across multiple agencies in the U.S., sustained
for at least the three months measured by this survey, is unique in modern law
enforcement history.<table-wrap position="float" id="table4-10986111221148217"><label>Table 4.</label><caption><p>Estimated Proportion of Calls that Officers were no Longer Handling
in Person in the first 2&#x000a0;months of the Pandemic.</p></caption><alternatives><graphic xlink:href="10.1177_10986111221148217-table4" specific-use="table4-10986111221148217" position="float"/><table frame="hsides" rules="groups"><thead valign="top"><tr><th align="left" rowspan="1" colspan="1"/><th align="left" rowspan="1" colspan="1">Wave 1 (&#x0201c;as of Mar 23&#x0201d;)</th><th align="left" rowspan="1" colspan="1">Wave 2 (&#x0201c;as of May 10&#x0201d;)</th></tr></thead><tbody valign="top"><tr><td align="left" rowspan="1" colspan="1">10% or less</td><td align="char" char="." rowspan="1" colspan="1">27.0%</td><td align="char" char="." rowspan="1" colspan="1">29.2%</td></tr><tr><td align="left" rowspan="1" colspan="1">11%&#x02013;20%</td><td align="char" char="." rowspan="1" colspan="1">20.1%</td><td align="char" char="." rowspan="1" colspan="1">25.5%</td></tr><tr><td align="left" rowspan="1" colspan="1">21%&#x02013;30%</td><td align="char" char="." rowspan="1" colspan="1">14.7%</td><td align="char" char="." rowspan="1" colspan="1">22.3%</td></tr><tr><td align="left" rowspan="1" colspan="1">More than 30%</td><td align="char" char="." rowspan="1" colspan="1">28.2%</td><td align="char" char="." rowspan="1" colspan="1">22.2%</td></tr><tr><td align="left" rowspan="1" colspan="1">Did not answer</td><td align="char" char="." rowspan="1" colspan="1">9.9%</td><td align="char" char="." rowspan="1" colspan="1">0.8%</td></tr></tbody></table></alternatives></table-wrap></p><p>Perhaps this decision to respond remotely to calls was correlated with challenges
faced with provisions of personal protective equipment, officers infected with
COVID, or other agency characteristics. We regressed several factors on this
decision to reduce the supply of in-person response, including population size,
number of sworn officers, trends in calls for service, availability of personal
protective equipment (discussed later), the proportion of the sworn workforce
with COVID (measured only in the second survey), and even the proportion of the
civilian workforce working remotely. None of these models were well fitting and
violated several assumptions. However, ordinal by ordinal crosstabulations
revealed only a modest but statistically significant relationship
(<italic toggle="yes">p</italic> &#x0003c; .001) between this decision and the number of sworn
officers and population size (Kendall&#x02019;s tau-c = &#x02212;0.096 and &#x02212;0.106,
respectively). Generally, agencies with fewer than 100 officers or jurisdictions
with populations of 100,000 or fewer tended to be more likely to reduce their
in-person responses to calls for service. However, given the lack of clear
findings in the regression models, we caution readers about the robustness of
this finding.</p></sec><sec id="sec6-10986111221148217"><title>Reductions in Officers&#x02019; Use of Arrest</title><p>Agencies substantially reduced their use of arrests and enforcement at the
beginning of COVID (another reduction in the supply of policing), especially for
minor offenses. The reduction in the use of arrest was likely not only the
result of a decline in certain types of offenses, but also a policy decision
made by several agencies to prevent officers from contracting COVID and because
of decisions made by other parts of the justice system. For example, 65% of
responding agencies noted that by March 23, 2020, their jail or correctional
facilities that receive and process arrestees had already restricted the types
of arrestees they would intake (e.g., not receiving misdemeanants or those who
appeared sick). This proportion increased to 72.4% by May 10. By March 23, 77%
of responding law enforcement agencies had already provided their officers with
formal instructions to reduce their use of physical arrests for minor offenses
(and similarly, 73% by the second survey). Regression analysis shown in <xref rid="table5-10986111221148217" ref-type="table">Table 5</xref> confirmed
these findings when controlling for the number of sworn officers, jurisdiction
population, the proportion of sworn officers sick with COVID, or trends in calls
for service. Agencies in jurisdictions that restricted jail intake were 2.4
times more likely to restrict arrests for minor offenses. Additionally, agencies
that restricted proactive enforcement activity (see discussion below) were also
five times more likely to restrict arrests for minor offenses.<table-wrap position="float" id="table5-10986111221148217"><label>Table 5.</label><caption><p>Logistic Regression Analysis of Predictors of Agency Restricting
Arrest Activities.</p></caption><alternatives><graphic xlink:href="10.1177_10986111221148217-table5" specific-use="table5-10986111221148217" position="float"/><table frame="hsides" rules="groups"><thead valign="top"><tr><th align="left" rowspan="1" colspan="1"/><th align="left" rowspan="1" colspan="1">B</th><th align="left" rowspan="1" colspan="1">S.E.</th><th align="left" rowspan="1" colspan="1">Wald</th><th align="left" rowspan="1" colspan="1">df</th><th align="left" rowspan="1" colspan="1">Sig.</th><th align="left" rowspan="1" colspan="1">Exp (B)</th></tr></thead><tbody valign="top"><tr><td align="left" colspan="7" rowspan="1">Population (reference = under
25,000)</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;25,000-49,999</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.223</td><td align="char" char="." rowspan="1" colspan="1">0.294</td><td align="char" char="." rowspan="1" colspan="1">0.574</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.449</td><td align="char" char="." rowspan="1" colspan="1">0.800</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;50,000-99,999</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.455</td><td align="char" char="." rowspan="1" colspan="1">0.409</td><td align="char" char="." rowspan="1" colspan="1">1.237</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.266</td><td align="char" char="." rowspan="1" colspan="1">0.634</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;100,000-249,999</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.268</td><td align="char" char="." rowspan="1" colspan="1">0.519</td><td align="char" char="." rowspan="1" colspan="1">0.267</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.606</td><td align="char" char="." rowspan="1" colspan="1">0.765</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;250,000-499,999</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.147</td><td align="char" char="." rowspan="1" colspan="1">0.660</td><td align="char" char="." rowspan="1" colspan="1">0.049</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.824</td><td align="char" char="." rowspan="1" colspan="1">0.864</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;500,000-999,999</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.209</td><td align="char" char="." rowspan="1" colspan="1">0.688</td><td align="char" char="." rowspan="1" colspan="1">0.092</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.761</td><td align="char" char="." rowspan="1" colspan="1">0.812</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;1 million or more</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.216</td><td align="char" char="." rowspan="1" colspan="1">0.599</td><td align="char" char="." rowspan="1" colspan="1">0.130</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.718</td><td align="char" char="." rowspan="1" colspan="1">0.806</td></tr><tr><td align="left" colspan="7" rowspan="1">Number of sworn officers
(reference = less than 25)</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;25&#x02013;49</td><td align="char" char="." rowspan="1" colspan="1">0.081</td><td align="char" char="." rowspan="1" colspan="1">0.228</td><td align="char" char="." rowspan="1" colspan="1">0.127</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.721</td><td align="char" char="." rowspan="1" colspan="1">1.085</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;50-99</td><td align="char" char="." rowspan="1" colspan="1">0.382</td><td align="char" char="." rowspan="1" colspan="1">0.345</td><td align="char" char="." rowspan="1" colspan="1">1.225</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.268</td><td align="char" char="." rowspan="1" colspan="1">1.465</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;100-499</td><td align="char" char="." rowspan="1" colspan="1">0.213</td><td align="char" char="." rowspan="1" colspan="1">0.466</td><td align="char" char="." rowspan="1" colspan="1">0.208</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.648</td><td align="char" char="." rowspan="1" colspan="1">1.237</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;500 or more</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.389</td><td align="char" char="." rowspan="1" colspan="1">0.605</td><td align="char" char="." rowspan="1" colspan="1">0.413</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.520</td><td align="char" char="." rowspan="1" colspan="1">0.678</td></tr><tr><td align="left" colspan="7" rowspan="1">Proportion of sworn on sick
leave due to covid (reference = none or less than
1%)</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;1-5%</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.156</td><td align="char" char="." rowspan="1" colspan="1">0.182</td><td align="char" char="." rowspan="1" colspan="1">0.734</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.392</td><td align="char" char="." rowspan="1" colspan="1">0.855</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;6-10%</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.041</td><td align="char" char="." rowspan="1" colspan="1">0.340</td><td align="char" char="." rowspan="1" colspan="1">0.014</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.905</td><td align="char" char="." rowspan="1" colspan="1">0.960</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;More than 10%</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.020</td><td align="char" char="." rowspan="1" colspan="1">0.500</td><td align="char" char="." rowspan="1" colspan="1">0.002</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.968</td><td align="char" char="." rowspan="1" colspan="1">0.980</td></tr><tr><td align="left" colspan="7" rowspan="1">Overall calls for service
(reference = decrease)</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Stayed the same</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.032</td><td align="char" char="." rowspan="1" colspan="1">0.222</td><td align="char" char="." rowspan="1" colspan="1">0.021</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.884</td><td align="char" char="." rowspan="1" colspan="1">0.968</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Increased</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.092</td><td align="char" char="." rowspan="1" colspan="1">0.299</td><td align="char" char="." rowspan="1" colspan="1">0.095</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.757</td><td align="char" char="." rowspan="1" colspan="1">0.912</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Policy to limit proactive enforcement [no
= 0; yes = 1]</td><td align="char" char="." rowspan="1" colspan="1">1.814*</td><td align="char" char="." rowspan="1" colspan="1">0.167</td><td align="char" char="." rowspan="1" colspan="1">117.506</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.000</td><td align="char" char="." rowspan="1" colspan="1">6.135</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Jail restricted intake [no = 0; yes =
1]</td><td align="char" char="." rowspan="1" colspan="1">1.228*</td><td align="char" char="." rowspan="1" colspan="1">0.167</td><td align="char" char="." rowspan="1" colspan="1">53.818</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.000</td><td align="char" char="." rowspan="1" colspan="1">3.415</td></tr><tr><td align="left" rowspan="1" colspan="1">&#x02003;Constant</td><td align="char" char="." rowspan="1" colspan="1">&#x02212;0.504</td><td align="char" char="." rowspan="1" colspan="1">0.203</td><td align="char" char="." rowspan="1" colspan="1">6.156</td><td align="char" char="." rowspan="1" colspan="1">1</td><td align="char" char="." rowspan="1" colspan="1">0.013</td><td align="char" char="." rowspan="1" colspan="1">0.604</td></tr></tbody></table></alternatives><table-wrap-foot><fn><p>Nagelkerke R<sup>2</sup> = .283, Cox and Snell R<sup>2</sup> =
.193, -2 Log Likelihood = 990.208, no. of observations = 1,060,
Chi-Square = 227.592 (<italic toggle="yes">p</italic> &#x0003c; .001). * indicates
statistical significance (<italic toggle="yes">p</italic> &#x0003c; .001).</p></fn></table-wrap-foot></table-wrap></p></sec><sec id="sec7-10986111221148217"><title>Changes in Officer-Initiated Proactive and Community-Oriented
Activity</title><p>In addition to responding to calls for service and making arrests, police
officers commonly engage in proactive or self-initiated activities to prevent
and deter crime, disorder, and traffic crashes, or to improve police-community
relationships. <xref rid="bibr26-10986111221148217" ref-type="bibr">Lum et al.
(2020)</xref> have found that most of this supply-side proactivity consists
of traffic (and sometimes pedestrian) stops and generalized patrol. Thus, we
asked agencies whether they had adopted a formal policy or directive to limit
officers&#x02019; self-initiated proactive enforcement behaviors (e.g., traffic and
pedestrian stops) and also community engagement activities due to COVID.</p><p>By March 23, 62% of responding agencies had already adopted formal policies
asking officers to reduce or limit proactive enforcement behaviors, which, as
<xref rid="table5-10986111221148217" ref-type="table">Table 5</xref>
indicated, was strongly associated with a reduction in the use of arrest for
minor offenses. However, by May 10, this proportion declined to 54%, indicating
that a small group of agencies may have resumed proactive enforcement efforts
(or at least did not formally restrict them). Larger agencies (those with 500 or
more sworn officers) were somewhat less likely<sup><xref rid="fn16-10986111221148217" ref-type="fn">16</xref></sup> to formally ask officers to
reduce their proactive enforcement activities (although these agencies make up
the smallest number of responses). (Interestingly, a recent study by <xref rid="bibr39-10986111221148217" ref-type="bibr">Nielson et al. (2022)</xref>
found that self-initiated proactivity for patrol <italic toggle="yes">increased</italic>
after COVID began in Houston, Texas).</p><p><xref rid="fig1-10986111221148217" ref-type="fig">Figure 1</xref> shows that this
decline of agencies restricting proactive enforcement between March and May
occurred in every agency size level, except for agencies with 500 or more
officers (who made up the smallest number of responses, as shown in <xref rid="table1-10986111221148217" ref-type="table">Table 1</xref>). We can only
speculate about reasons for the decline in the proportion of agencies
restricting proactive enforcement between March and May. Many jurisdictions
still maintained stay-at-home orders, although some public activity was resumed
by May. In addition, we now know from reports by the National Highway
Transportation Safety Administration (<xref rid="bibr46-10986111221148217" ref-type="bibr">Stewart, 2022</xref>) that traffic fatalities
<italic toggle="yes">increased</italic> from 2019 to 2020, particularly speeding-related,
alcohol-impaired, and seat-belt non-use fatalities. Agencies may have realized
this problem early on and adjusted their initial reduction in proactive traffic
enforcement, given increases in reckless driving behaviors during the first two
months of the pandemic.<fig position="float" fig-type="figure" id="fig1-10986111221148217"><label>Figure 1.</label><caption><p>Percentage of Responding Agencies (Categorized by Number of Sworn
Officers) Who had Formal Policies to Restrict Proactive Enforcement
for Each Survey Wave by Size of Agency.</p></caption><graphic xlink:href="10.1177_10986111221148217-fig1" position="float"/></fig></p><p>We also asked agencies whether they had adopted a formal policy or directive to
limit community-oriented policing activities of officers (for example, community
meetings, problem-solving activities, etc.) due to COVID. It appears that U.S.
police agencies were even more likely to restrict community-oriented activities
than proactive enforcement activities. By March 23, 2020, 73% of agencies
responded that they had officially and formally reduced or limited
community-oriented policing activities. By May 10, this proportion had declined
to 64%, but it remained higher than proactive enforcement activities across all
sizes of agencies, as shown in <xref rid="fig2-10986111221148217" ref-type="fig">Figure 2</xref>. While smaller agencies were less likely to restrict
community policing activities than their larger counterparts, the proportion of
agencies restricting community engagement was still substantial.<sup><xref rid="fn17-10986111221148217" ref-type="fn">17</xref></sup><fig position="float" fig-type="figure" id="fig2-10986111221148217"><label>Figure 2.</label><caption><p>Percentage of Responding Agencies (Categorized by Number of Sworn
Officers) Who had Formal Policies to Restrict Community Policing
Activities for Each Survey Wave by Size of Agency.</p></caption><graphic xlink:href="10.1177_10986111221148217-fig2" position="float"/></fig></p><p>Other forms of community engagement were also low during the first two months of
the pandemic. For example, by May 10, 2020, only 19% of responding agencies had
led an official press conference addressing law enforcement activities related
to COVID-19. More generally, 66% of responding agencies had responded that they
had not significantly changed their use of social media to communicate with the
public (although 27% did report increasing their use of social media because of
COVID). Again, these trends were not correlated to jurisdiction or population
sizes.</p></sec><sec id="sec8-10986111221148217"><title>Protecting Officers from Contracting COVID</title><p>Initial efforts to restrict in-person contact and other supply-side policing
efforts at the beginning of the pandemic were likely due to agencies being
concerned about their workforce contracting COVID-19. Even by the first survey,
43% of agency heads responded that all of their officers had already received
formal guidance and information from either the CDC or their state and local
health agencies about the infectious nature of COVID-19. This proportion had
increased to 83% by May 10. Given this, how prepared were U.S. law enforcement
agencies in protecting their workforce from contracting COVID, and how might
this have impacted their activities?</p><p>The provision of personal protection equipment (PPE) for front-line officers and
emergency responders had been a significant concern at the start of the
pandemic, with immediate supply shortages reported by the media. Police agencies
often have PPE supplies in stock, but the exponential increase in the need for
these supplies due to COVID placed incredible pressure on some agencies for
large amounts of these supplies. However, as already mentioned, the provision of
PPE was also not found to be related to the reduction in the supply of police
services. In March, 90% of agencies responding to the survey said that officers
had PPE that they could use (primarily face masks and gloves) in their
possession. At the same time, 15% of agency respondents rated their ability to
provide PPE to their officers as &#x0201c;excellent&#x0201d;; 38% as &#x0201c;good&#x0201d;; 28% as &#x0201c;fair&#x0201d;; 13%
of agencies indicated this ability to be &#x0201c;poor&#x0201d; or &#x0201c;very poor.&#x0201d; At that time,
about 57% of responding agencies had tasked their first-line supervisors with
regularly inspecting, monitoring, and supervising PPE use, and 53% of agencies
were &#x0201c;confident&#x0201d; in maintaining these supplies.</p><p>By the second survey wave (as of May 10), a better picture of the availability of
PPE emerged (and we also asked more specific questions). Seventy-six percent of
responding agencies stated that they had enough PPE to sustain employees for at
least 30 days (an additional 17% had enough PPE for at least the next two
weeks). Only 5% of agencies did not have enough PPE to last one more week or did
not have any PPE. The majority of agencies by the second survey received their
PPE supplies locally or from internal supplies (62%), while another 13% received
them through donations or private companies/individuals. Only 2% of agencies by
May 2020 were relying on the federal government to provide them with PPE. By
May, almost three-quarters of agencies had supervisors regularly inspecting PPE
and had confidence that they could sustain PPE supplies. Compared to March,
agencies in May were more confident about managing officers exposed to COVID-19.
On a scale of 1 to 5, with 1 being &#x0201c;very poor&#x0201d; and 5 being &#x0201c;excellent,&#x0201d; agencies
had rated themselves 3.7 in Wave 1, but by Wave 2 this average rating increased
to 4.2. Overall, it seems that within the first few months of the pandemic,
police agencies had quickly adapted to manage the provision and inspection of
PPE and the risk of COVID infections among their staff.</p><p>It is unclear from our survey how successful agencies were in the first two
months of keeping their officers from contracting COVID based on the
availability of PPE and their efforts to reduce in-person contact with the
public. Recent reports from the <xref rid="bibr38-10986111221148217" ref-type="bibr">National Law Enforcement Memorial and Museum
(2022)</xref> indicate that COVID-19 deaths were the leading cause of
officer fatalities in 2020 and 2021. The Officer Down Memorial Page
(ODMP)<sup><xref rid="fn18-10986111221148217" ref-type="fn">18</xref></sup> reports its earliest COVID-19 related line-of-duty death
was on March 24, 2020. Between our March and May surveys, 27% of the
<italic toggle="yes">total</italic> deaths reported by ODMP in 2020 (a total of 274) were
COVID-related. We only asked about sick leave in Wave 2, and the findings are
hard to interpret, as contracting COVID on duty may not have been dealt with
through normal sick-leave processes. For example, approximately 60% of agencies
did <italic toggle="yes">not</italic> report noticeable officer sick leave due to COVID,
although 28% reported 1&#x02013;5% of sworn officers out on sick leave due to COVID-19
infections or quarantining. However, 38% of agencies also reported 1&#x02013;5% of sworn
officers out on sick leave for other reasons, not due to COVID.</p></sec></sec><sec id="sec9-10986111221148217"><title>The Changing Supply and Demand of Policing: Are we at a New Equilibrium
Point?</title><p>In this study, we report on the findings of the only systematic agency surveys
conducted across a large and diverse sample of U.S. law enforcement agencies during
the first two months of the COVID-19 pandemic and before the murder of George Floyd.
The ability to carry out these two national surveys as the pandemic broke speaks to
the partnerships that can be established between researchers and practitioners for a
common goal. Given that the surveys were implemented before George Floyd&#x02019;s murder,
they are also the only agency-level surveys with responses that are not confounded
by that event and its aftermath. As with other national policing surveys, our
surveys somewhat oversample larger agencies (although a substantial proportion of
the sample are small agencies) and are samples of convenience (using the IACP
membership list). Additionally, given the limitations detailed in our methods
section, we could not conduct a longitudinal analysis given the cross-sectional data
collected. Despite these shortcomings, our surveys show that COVID&#x02019;s impacts on
policing were dramatic in the first two months of the pandemic, with some lingering
effects. Not only were the impacts of COVID-19 on police agency operations
substantial, but these impacts were shared across agencies large, small, urban,
suburban, and rural.</p><p>The surveys reveal that COVID-19 significantly altered both the supply and demand for
police services in the first three months of the pandemic. Concerning demand, we
find the overall volume of calls for service initially declined, confirming what
others have generally found (see, e.g., <xref rid="bibr1-10986111221148217" ref-type="bibr">Abrams, 2021</xref>; <xref rid="bibr2-10986111221148217" ref-type="bibr">Ashby, 2020</xref>; <xref rid="bibr5-10986111221148217" ref-type="bibr">Campedelli et al., 2020</xref>; <xref rid="bibr22-10986111221148217" ref-type="bibr">Langton et al., 2021</xref>;
<xref rid="bibr24-10986111221148217" ref-type="bibr">Lopez &#x00026; Rosenfeld,
2021</xref>; <xref rid="bibr41-10986111221148217" ref-type="bibr">Piquero et
al., 2021</xref>). The halting of public life dramatically altered the
opportunities, routines, situations, and interpersonal exchanges associated with
crime, disorder, conflicts, and traffic accidents. These changes naturally impacted
the volume, frequency, and types of calls for emergency services through 911 and
other non-emergency public safety numbers. At the same time, studies have revealed
that the short and long-term effects of COVID-19 on specific types of calls for
service and crime are complex. Certain requests, such as those for traffic issues or
minor disorders, decreased early in the pandemic, likely due to changing routines
and the ceasing of public life. Some declines in calls for service may have also
occurred due to a decline in official reporting but may not reflect actual trends,
such as domestic violence (<xref rid="bibr40-10986111221148217" ref-type="bibr">Nix
&#x00026; Richards, 2021</xref>; <xref rid="bibr41-10986111221148217" ref-type="bibr">Piquero et al., 2021</xref>) or child abuse
(<xref rid="bibr47-10986111221148217" ref-type="bibr">U.S. Department of Health
&#x00026; Human Services, 2022</xref>). In our surveys, agencies that reported
increases in calls related to domestic violence also often reported increases in
calls for mental distress. Others have found that firearms and other violence have
increased in some cities and at certain times (<xref rid="bibr4-10986111221148217" ref-type="bibr">Beard et al., 2021</xref>; <xref rid="bibr20-10986111221148217" ref-type="bibr">Kim &#x00026; Phillips, 2021</xref>; <xref rid="bibr24-10986111221148217" ref-type="bibr">Lopez &#x00026; Rosenfeld,
2021</xref>). Our survey indicated that agencies that experienced increased
violence initially in the pandemic may have also experienced increased commercial
burglaries.</p><p>The overall decline and heterogeneity of trends across call categories suggest that
public safety demands may have transformed significantly and quickly, reflecting a
new portfolio of community public safety needs for the police. Typical patterns of
resource allocation and prioritization that police had been accustomed to changed
almost overnight. Critical questions for researchers to explore about these shifts
are whether and why certain calls went up or down and whether law enforcement
agencies were prepared for these shifts. For example, were agencies prepared to
handle the changing needs and shifts in tactics required to respond to (and prevent)
increases in specific types of events? When agencies did shift to remote response
for certain calls, were they for call types that increased or decreased? What were
the consequences of those adjustments for public safety?</p><p>At the same time as changing demands, the police adopted policies to modify the
supply of police services, likely to protect their officers from contracting COVID
and anticipating staffing shortages. A significant majority of the responding
agencies from both surveys restricted officers from responding to certain calls for
service in person, reduced arrest and proactive enforcement, and stopped community
engagement activities. Some of these adjustments may have continued longer than
expected. As previously mentioned, <xref rid="bibr29-10986111221148217" ref-type="bibr">Mask&#x000e1;ly et al. (2021)</xref> found similar
results in a smaller international sample of agencies during the summer of 2020.
<xref rid="bibr10-10986111221148217" ref-type="bibr">Ekici and Alexander
(2021)</xref> noted these same trends again in the fall of 2020. Our survey also
indicated that these reductions in supply were not met by a substantial increase in
other forms of communication with the public. If thinking of this as a supply and
demand relationship (see <xref rid="fig3-10986111221148217" ref-type="fig">Figure
3</xref>), the shift in both the supply (S<sub>1</sub> to S<sub>2</sub>) and
demand (D<sub>1</sub> to D<sub>2</sub>) for police services may have significantly
moved the point of equilibrium (E<sub>1</sub> to E<sub>2</sub>) of police-community
interactions. The magnitude and impact of this change depend not only on the
elasticity (or slope) of each curve but also the extent of the shifts of both.<fig position="float" fig-type="figure" id="fig3-10986111221148217"><label>Figure 3.</label><caption><p>Shifting Supply and Demand of Policing during the First Few Months of the
COVID-19 Pandemic.</p></caption><graphic xlink:href="10.1177_10986111221148217-fig3" position="float"/></fig></p><p>Police leaders, communities, and researchers should be concerned about the short- and
long-term impacts of this equilibrium shift on police legitimacy and public safety.
Without tracking these adjustments carefully, agencies may adopt remote response or
reduce some enforcement blindly, potentially with negative consequences. For
example, when the police move to remote response to some calls for service, can they
address community requests as effectively and efficiently, and what are the benefits
and costs of doing so? Unfortunately, we do not have rigorous research comparing
face-to-face versus virtual call responses on various outcomes (e.g., customer
service, resolution of the problem, apprehension of the suspect, prevention of the
problem in the future, the legitimacy of the system). Therefore, we do not know if
this patrol deployment approach is beneficial for the individual who calls the
police or for public safety more generally. Additionally, <italic toggle="yes">which</italic>
calls are being handled remotely? For some categories of calls, remote response may
have little impact on victim services or public safety. But for other categories of
calls, remote response may not help to calm victim fears, resolve particular
disputes, or adequately relay information to callers or deterrence and prevention
signaling to offenders.</p><p>And what about the increased demand for police services for specific types of
incidents? While family violence and child abuse were always serious public concerns
before COVID, our surveys, combined with the studies from <xref rid="bibr41-10986111221148217" ref-type="bibr">Piquero et al.&#x02019;s (2021)</xref> systematic review,
show that having people stay at home for extended periods may have increased these
problems in our society. Getting people back out of their homes will not undo the
abuse that may have already occurred during the stay-at-home periods. Formal and
informal mechanisms for reporting family violence also shifted during COVID (<xref rid="bibr43-10986111221148217" ref-type="bibr">Richards et al., 2021</xref>).
For example, school policies and laws have long facilitated child abuse reporting by
third parties (e.g., teachers and counselors), but virtual education altered this
dynamic. The adjustment to virtual schooling may have caused an artificial decline
in demand (requests for service) in some jurisdictions when needs may have actually
increased. Delays in court and detention processes may have further exacerbated
these changes during the early months of the pandemic, which may worsen domestic
violence cases and situations. Some jurisdictions also noticed increases in certain
types of violence (<xref rid="bibr24-10986111221148217" ref-type="bibr">Lopez and
Rosenfeld 2021)</xref>. These specific findings indicate that pandemics may
present police leaders with more complicated concerns than just whether certain
crimes increase or decrease. The causes and landscape of offenses and public safety
problems may transform in ways that require dynamic adjustments to the quality of
police deployment, not simply the quantity. Agencies can prepare by developing
better tracking and analytic mechanisms for understanding not only the frequency but
also the nature of public safety demands, so they can see shifts in the landscape of
those demands more clearly to make more strategic adjustments to their supply of
policing.</p><p>We also note that agencies may be slower to return to proactivity and community
engagement, even though demand (or crime) may return to previous levels relatively
quickly. We know from a large body of research that when done well, proactive and
community-oriented policing can reduce crime, disorder, and accidents, and improve
community satisfaction and legitimacy towards the police (see assessments by <xref rid="bibr35-10986111221148217" ref-type="bibr">NAS, 2018</xref>; National
Research Council <xref rid="bibr36-10986111221148217" ref-type="bibr">[NRC],
2004</xref>). At the same time, we also know that these types of deployments and
reforms have been slow to penetrate the vice-like grip that traditional, reactive,
and arrest-oriented approaches have on U.S. policing (<xref rid="bibr50-10986111221148217" ref-type="bibr">Lum &#x00026; Koper, 2017</xref>). Several gains were
made in the 21st century in improving agency approaches to align with this
evidence-base. However, the pandemic reduced these activities significantly and
quickly. Subsequent protests of the police after Floyd&#x02019;s murder and calls to defund
or divert police responsibilities to others may have continued to slow the return of
these activities. However, once public routines (and calls for service) bounce back
(which they did later in 2020), such proactive police activities might be important
to preventing and deterring crimes and traffic accidents. More specifically, certain
crimes that increased during the first year of the pandemic may be particularly
elastic to the supply of police services, and therefore depend on police enforcement
to control.</p><p>Returning to <xref rid="fig3-10986111221148217" ref-type="fig">Figure 3</xref>, we
also do not know the long-term effects of the change in the police-community
equilibrium point on police legitimacy. Many agencies continue to deploy
alternatives to in-person call response to this day and for several reasons
unrelated to the fear of officers contracting COVID (resource-saving, officer
preference, or as a response to calls for defunding or diversion, to name a few).
Several agencies have still not resumed pre-pandemic levels of community-oriented
activities in person. Over the long term, what impacts will these shifts have on
police legitimacy and police-community relationships? Again, we only speculate, and
the direction of effects is uncertain. Maybe those service calls now being handled
remotely or pre-COVID community engagement activities had little connectivity with
police legitimacy and police-community relationships, which may depend more on
visible police activities, sentinel events, police use of force, or how the agency
responds to serious crimes. On the other hand, previous research has indicated that
community members are often concerned about minor issues in their neighborhoods.
These calls for service of more minor issues may also serve as opportunities for
problem-solving or community engagement to build collective efficacy. Reducing
police response to seemingly minor community issues may have bigger impacts than
some might expect, a hypothesis raised by <xref rid="bibr31-10986111221148217" ref-type="bibr">Mazerolle et al. (2002)</xref> in their studies
of &#x0201c;311&#x0201d; alternative telephone lines. <xref rid="bibr14-10986111221148217" ref-type="bibr">Gill et al.&#x02019;s (2014)</xref> systematic review
also found that while community policing may not necessarily reduce crime, these
activities can improve police legitimacy and community satisfaction with the
police.</p><p>Additionally, Floyd&#x02019;s murder and the subsequent worldwide protests resulted in
several challenges to policing and exposed the fragile relationship between police
and communities. However, fundamental changes in police-community relationships were
occurring well before Floyd. Although only a hypothesis, the downward shift in the
equilibrium of police-community relationships caused by COVID may have accelerated
the downward spiral of police-community relationships post-Floyd that agencies were
already experiencing pre-pandemic. The reduction in face-to-face response to calls
for service during the COVID pandemic may have also inadvertently provided
<italic toggle="yes">more</italic> justification for defunding arguments, although such an
argument would be complicated (<xref rid="bibr25-10986111221148217" ref-type="bibr">Lum et al., 2021</xref>).</p><p>It is fair to say that people are tired of discussions about the pandemic and want to
move on. Thankfully, mortality rates have slowed, and vaccines and other treatments
have been developed to try and mitigate the pandemic&#x02019;s impacts on our everyday
lives. At the same time, it will be important for law enforcement agencies to
conduct agency-specific after-action assessments of the short and long-term impacts
of COVID on crime and disorder, community sentiment and police legitimacy, and
agency operations. In particular, understanding the lingering effects of COVID on
both police organizations and their communities, and how to restore police-community
interaction equilibrium points if slippage occurred during the pandemic, may be
important goals, especially during a period of police crisis and reform.</p></sec></body><back><fn-group><fn fn-type="other"><p><bold>Author&#x02019;s Note:</bold> The findings and recommendations presented within this report are from the
authors and do not necessarily reflect the official positions or opinions of the
IACP.</p></fn><fn fn-type="COI-statement"><p>The author(s) declared no potential conflicts of interest with respect to the
research, authorship, and/or publication of this article.</p></fn><fn fn-type="financial-disclosure"><p><bold>Funding:</bold> The author(s) received no financial support for the research, authorship, and/or
publication of this article.</p></fn></fn-group><bio id="d64e2834"><title>Author Biographies</title><p><bold>Dr. Cynthia Lum</bold> is a Professor of Criminology, Law and Society at George
Mason University and the Director of its Center for Evidence-Based Crime Policy. She
studies policing, technology, evidence-based crime policy, translational
criminology, and crime prevention.</p></bio><bio id="d64e2839"><p><bold>Dr. Megan Stoltz</bold> is a program manager at the International Association
of Chiefs of Police (IACP). She leads projects translating research into
evidence-based guidance for police practitioners.</p></bio><bio id="d64e2843"><p><bold>Carl Maupin</bold> is an assistant director at the International Association of
Chiefs of Police (IACP). He oversees evidence-based technical assistance projects
for police agencies around the world.</p></bio><sec id="sec13-10986111221148217"><title>ORCID iD</title><p>Cynthia Lum <ext-link xlink:href="https://orcid.org/0000-0002-1919-3987" ext-link-type="uri">https://orcid.org/0000-0002-1919-3987</ext-link></p></sec><notes><title>Notes</title><fn-group><fn fn-type="other" id="fn1-10986111221148217"><label>1.</label><p>See <ext-link xlink:href="https://www.kff.org/coronavirus-policy-watch/stay-at-home-orders-to-fight-covid19/" ext-link-type="uri">https://www.kff.org/coronavirus-policy-watch/stay-at-home-orders-to-fight-covid19/</ext-link>.
See also <ext-link xlink:href="https://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html" ext-link-type="uri">https://www.nytimes.com/interactive/2020/us/states-reopen-map-coronavirus.html</ext-link>.</p></fn><fn fn-type="other" id="fn2-10986111221148217"><label>2.</label><p>See <ext-link xlink:href="https://covid.cdc.gov/covid-data-tracker/#datatracker-homeforup-to-dateinformation" ext-link-type="uri">https://covid.cdc.gov/covid-data-tracker/#datatracker-homeforup-to-dateinformation</ext-link>.</p></fn><fn fn-type="other" id="fn3-10986111221148217"><label>3.</label><p>See <ext-link xlink:href="https://www.cdc.gov/mmwr/volumes/71/wr/mm7117e1.htm" ext-link-type="uri">https://www.cdc.gov/mmwr/volumes/71/wr/mm7117e1.htm</ext-link>.</p></fn><fn fn-type="other" id="fn4-10986111221148217"><label>4.</label><p>See also the CDC&#x02019;s tracking of racial inequalities in COVID infection here:
<ext-link xlink:href="https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-race-ethnicity.html" ext-link-type="uri">https://www.cdc.gov/coronavirus/2019-ncov/covid-data/investigations-discovery/hospitalization-death-by-race-ethnicity.html</ext-link>.</p></fn><fn fn-type="other" id="fn5-10986111221148217"><label>5.</label><p>These restrictions are too lengthy to review here. However, there is
extensive documentation on restrictions in U.S. federal (<ext-link xlink:href="https://www.uscourts.gov/about-federal-courts/court-website-links/court-orders-and-updates-during-covid19-pandemic)andstate" ext-link-type="uri">https://www.uscourts.gov/about-federal-courts/court-website-links/court-orders-and-updates-during-covid19-pandemic)andstate</ext-link>
and local courts (for one example in Virginia, see <ext-link xlink:href="https://www.vacourts.gov/news/items/covid_19.pdf" ext-link-type="uri">https://www.vacourts.gov/news/items/covid_19.pdf</ext-link>).</p></fn><fn fn-type="other" id="fn6-10986111221148217"><label>6.</label><p>Again, these orders are documented differently across states. To see an
example from New York, go to <ext-link xlink:href="https://doccs.ny.gov/doccs-covid-19-report" ext-link-type="uri">https://doccs.ny.gov/doccs-covid-19-report</ext-link>.</p></fn><fn fn-type="other" id="fn7-10986111221148217"><label>7.</label><p>See <ext-link xlink:href="https://www.policeforum.org/covid-19-response#daily" ext-link-type="uri">https://www.policeforum.org/covid-19-response#daily</ext-link>.</p></fn><fn fn-type="other" id="fn8-10986111221148217"><label>8.</label><p>See <ext-link xlink:href="https://www.theiacp.org/resources/document/law-enforcement-information-on-covid-19" ext-link-type="uri">https://www.theiacp.org/resources/document/law-enforcement-information-on-covid-19</ext-link>.
See also International Association of Chiefs of Police and Office of
Community Oriented Policing Services. 2022 (forthcoming). COVID-19 Law
Enforcement Impact and Response: Collaborative Reform Initiative Technical
Assistance Center (CRI-TAC): Washington, DC: Office of Community Oriented
Policing Services.</p></fn><fn fn-type="other" id="fn9-10986111221148217"><label>9.</label><p>Wave 1 Fact Sheet:
https://www.theiacp.org/sites/default/files/IACP-GMU%20Survey.pdf. Wave 2
Fact Sheet: <ext-link xlink:href="https://www.theiacp.org/sites/default/files/IACP_Covid_Impact_Wave2.pdf." ext-link-type="uri">https://www.theiacp.org/sites/default/files/IACP_Covid_Impact_Wave2.pdf.</ext-link></p></fn><fn fn-type="other" id="fn10-10986111221148217"><label>10.</label><p>On May 25, 2020, George Floyd, a 46-year-old Black male was murdered by a
white police officer in Minneapolis, Minnesota. The officer killed Floyd by
kneeling on Floyd&#x02019;s neck for over 9&#x000a0;minutes, despite the fact that Floyd was
handcuffed and lying face-down in the street. The officers involved were
charged (and some later convicted) of homicide. Floyd&#x02019;s death was a turning
point in policing and led to worldwide protests against police use of force
and extensive discussions and proposed legislation on police reform. Cities
that experienced protests often went from few people interacting in public
to large groups protesting.</p></fn><fn fn-type="other" id="fn11-10986111221148217"><label>11.</label><p>See also PERF&#x02019;s daily report collection at <ext-link xlink:href="https://www.policeforum.org/covid-19-response" ext-link-type="uri">https://www.policeforum.org/covid-19-response#agency</ext-link>.</p></fn><fn fn-type="other" id="fn12-10986111221148217"><label>12.</label><p>For more information about this data collection, see <ext-link xlink:href="https://bjs.ojp.gov/data-collection/law-enforcement-management-and-administrative-statistics-lemas" ext-link-type="uri">https://bjs.ojp.gov/data-collection/law-enforcement-management-and-administrative-statistics-lemas</ext-link>.
The first author has conducted several random-sample agency level surveys
using LEMAS information about agencies, which often take months (as they
often are best implemented with paper mail surveys), and result in low
response rates if multiple telephone followups are not conducted.</p></fn><fn fn-type="other" id="fn13-10986111221148217"><label>13.</label><p>The survey instruments are available upon request.</p></fn><fn fn-type="other" id="fn14-10986111221148217"><label>14.</label><p>Both ordinal by ordinal crosstabulations and ordinal regression were run on
this data, given that both the number of sworn officers and population size
were collected in categories. Unfortunately, given the anonymous nature of
these surveys, we only had these two agency and jurisdiction characteristics
for each respondent. Thus, we did not have the specific jurisdiction name
and therefore could not factor into our analysis rate of COVID infection,
demographic, socioeconomic, or crime-related factors.</p></fn><fn fn-type="other" id="fn15-10986111221148217"><label>15.</label><p>This question was worded slightly differently between the first and second
surveys. In the first survey, this question read, &#x0201c;Approximately what
proportion of calls for service had your agency stopped responding to or
changed its response to, based on the guidance issued?&#x0201d; This question was
conditioned on a respondent answering &#x0201c;yes&#x0201d; to the previous question about
providing specific and written guidance to officers on this (resulting in
9.9% missing answers on the second question from those who had answered
&#x0201c;no&#x0201d;). In the second wave, we asked the question more specifically and
without a conditional question: &#x0201c;Approximately what proportion of dispatched
calls for service that officers previously responded to in person, were
handled using a telephone, internet, or videoconferencing system?&#x0201d;</p></fn><fn fn-type="other" id="fn16-10986111221148217"><label>16.</label><p>We caution readers about this relationship as it is weak. In the first wave,
this ordinal by ordinal crosstabulation revealed a Kendall&#x02019;s tau-c of &#x02212;.070,
<italic toggle="yes">p</italic> = .040. In the second wave, tau-c = &#x02212;.058,
<italic toggle="yes">p</italic> = .088.</p></fn><fn fn-type="other" id="fn17-10986111221148217"><label>17.</label><p>In the first wave, the ordinal by ordinal crosstabulation between numbers of
categories of numbers of sworn officers in <xref rid="fig2-10986111221148217" ref-type="fig">Figure 2</xref> and decision to restrict
community oriented policing activities revealed a Kendall&#x02019;s tau-c of -.150,
<italic toggle="yes">p</italic> &#x0003c; .001. In the second wave, tau-c = &#x02212;.100,
<italic toggle="yes">p</italic> = .002.</p></fn><fn fn-type="other" id="fn18-10986111221148217"><label>18.</label><p>See <ext-link xlink:href="https://www.odmp.org/search/year?year=2020" ext-link-type="uri">https://www.odmp.org/search/year?year=2020</ext-link>.</p></fn></fn-group></notes><ref-list><title>References</title><ref id="bibr1-10986111221148217"><mixed-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Abrams</surname><given-names>D. S</given-names></name></person-group> (<year>2021</year>). <article-title>COVID and crime:
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