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Crime Rates in a Pandemic: the Largest Criminological
Experiment in History
Ben Stickle
1
&Marcus Felson
2
Received: 29 May 2020 /Accepted: 11 June 2020/
#Southern Criminal Justice Association 2020
Abstract
The COVID-19 pandemic of 2020 has impacted the world in ways not seen in
generations. Initial evidence suggests one of the effects is crime rates, which appear
to have fallen drastically in many communities around the world. We argue that the
principal reason for the change is the government ordered stay-at-home orders, which
impacted the routine activities of entire populations. Because these orders impacted
countries, states, and communities at different times and in different ways, a naturally
occurring, quasi-randomized control experiment has unfolded, allowing the testing of
criminological theories as never before. Using new and traditional data sources made
available as a result of the pandemic criminologists are equipped to study crime in
society as never before. We encourage researchers to study specific types of crime, in a
temporal fashion (following the stay-at-home orders), and placed-based. The results
will reveal not only why, where, when, and to what extent crime changed, but also how
to influence future crime reduction.
Keywords COVID-19 .Crime rates .Coronavirus response .Routine activities .Rational
choice .Crime
The COVID-19 pandemic of 2020 is unquestionably one of the most significant world-
wide events in recent history, impacting culture, government operations, crime, eco-
nomics, politics, and social interactions for the foreseeable future. One unique aspect of
this crisis is the governmental response of issuing legal stay-at-home orders to attempt
to slow the spread of the virus. While these orders varied, both in degree and timing,
between countries and states, they generally began with strong encouragement for
persons to isolate themselves voluntarily. As the magnitude of the crisis grew,
American Journal of Criminal Justice
https://doi.org/10.1007/s12103-020-09546-0
*Ben Stickle
Ben.stickle@mtsu.edu
1
Department of Criminal Justice Administration, Middle Tennessee State University, Murfreesboro,
TN 37132, USA
2
Texas State University, San Marcos, TX, USA
governments began legally mandating persons to stay-at-home to reduce the transmis-
sion rate of the virus. There were, of course, exceptions; workers who were deemed
‘essential,’such as those in the fields of medicine, finance, public safety, food
production, transportation, and in other miscellaneous industries did not have to abide
by these orders to the degree to which the general public did.
Nevertheless, practically overnight, the entire country ceased or significantly reduced
day-to-day travels, eliminating commutes from home to work, as well as leisure activities,
shopping trips, social gatherings, the ability to dine out, and more. One poll in late March
found that 90% of Americans, including essential workers, were ‘staying at home as much
as possible’(Washington Post-ABC, 2020). The ‘stay-at-home’mandates brought about
the most wide-reaching, significant, and sudden alteration of the lives of billions of people
in human history. Across the United States and around the world, a positive byproduct
(Fattah, 2020) of these unprecedented events is a dramatic drop in crime rates.
Initial Crime Data
Several researchers have made initial examinations into how crime rates have fluctu-
ated in the advent of COVID-19. The results have been mixed, to say the least,
especially when comparing broad categories of crime across different cities and with
different methods and periods of study. However, these initial academic studies are
intrinsically valuable and deserve to be mentioned here.
One of the earliest studies with perhaps the most striking results was by Shayegh and
Malpede (2020), which identified an overall drop in crime in San Francisco of 43% and
Oakland of about 50% following city issuance of some of the most restrictive and early
stay-at-home orders in the US, beginning March 16th,2020 and the two weeks after.
Surprisingly, significant results are also clearly seen when examining specific crimes
against retailers in crime in Los Angeles. Pietrawska, Aurand, and Palmer (2020a)
found a 64% increase in retail burglary, while city-wide burglary rates weredown 10%.
Similarly, Pietrawska, Aurand, and Palmer (2020b) identified a five-week change in
crimes occurring at restaurants in Chicago, a 74% reduction, while city-wide crime
declined 35%. Continuing their study of crime rates in the pandemic outside of a retail
focus, Pietrawska, Aurand, and Palmer (2020c) compared crimes against persons and
crimes against property in four cities for ten weeks, finding sharp variations from week
to week and within different crime types.
Another early study by Ashby (2020a) of eight large US cities during the first few
weeks of the crisis (January to March 23rd—before some states and areas implemented
stay-at-home orders) found disparate impacts by crime type and location. For example,
burglary declined in Austin, Los Angeles, Memphis, and Scan Francisco, but not in
Louisville or Boston. Conversely, serious assaults in public declined in Austin, Los
Angeles, and Louisville, but not other cities.
Felson,Jiang,andXu(2020) examined burglary in Detroit during three periods,
representing data before stay-at-home orders were in place and two periods under
orders (March 10th to March 23rd and March 24th to March 31st). Their findings
indicated an overall 32% decline in burglary, with the most substantial change in the
third period. However, the decline was more significant in block groups of higher
residential parcels than in mix-use land areas.
American Journal of Criminal Justice
Campedelli et al. (2020) analyzed crime in Los Angeles in two time periods (the first
ending March 16th and the second ending March 28th) using Bayesian structural time-
series models to estimate what crime would have been if the COVID-19 pandemic had
not occurred. Comparing the actual crime data against the estimated ‘sans-pandemic’
data, the first model found an overall crime reduction of 5.6% during the pandemic.
Likewise, the second model (ending March 28th) showed a 15% reduction. Specifi-
cally, researchers found that overall crime rates significantly decreased, particularly
when referencing robbery (−24%), shoplifting (−14%), theft (−21%), and battery
(−11%). However, burglary, domestic violence, stolen vehicles, and homicide
remained statically unchanged.
While not explicitly measuring crime rates, studies of calls for police service
can function as an indirect measure of crime in a given area. Early studies of calls
for service during the pandemic present mixed results. Lum, Maupin, and Stoltz
(2020) found that 57% of 1000 agencies surveyed in the United States and Canada
reported a reduction in calls for service in March of 2020. Ashby (2020b), on the
other hand, found no discernible difference in forecasted calls for service in 10
large US cities between the first identified cases of COVID-19 in the US through-
out early March. However, Ashby found that once stay-at-home orders were
implemented, calls for service did decline, although not evenly across call types
or cities. In another study of police calls for service, Mohler et al. (2020)
examined calls in Los Angeles and Indianapolis between January and mid-April;
they concluded there was some impact on police calls for service but not across all
crime types or places.
Internationally, Swedish researchers Gerell, Kardell, and Kindgren (2020)examined
crime during the five weeks after government restrictions on activities began, observing
an 8.8% total drop in reported crime despite the country’s somewhat lax response
(when compared to other countries’policies on restricting the public’smovement).
Specifically, the researchers found residential burglary fell by 23%, commercial bur-
glary declined 12.7%, and instances of pick-pocketing were reduced by a staggering
61% —however, there was little change in robberies or narcotics crime. In Australia,
Payne and Morgan (2020) studied crime in March, finding assaults, sexual violations,
and domestic violence were not significantly different from what was predicted under
‘normal’conditions at the lower end of the confidence interval. They cautioned against
early conclusions based on this data as the government orders came only a few weeks
into the study.
These initial reports indicate that crime rates have indeed changed, but un-
equally across different categories, types, places, and timeframes. Among crime
researchers, the featured question of this pandemic will be, “Why have crime rates
fallen so dramatically?”The corollary is, “What can be learned from this experi-
ence to leverage crime reduction in the future?”The data and opportunities before
every criminologist will provide near-endless research opportunities at levels
never before possible, and every effort should be made to capture data and
promote the study of crime. This research note aims to identify and encourage
these lines of inquiry, to urge researchers to dive deeply into the data made
available from the pandemic, and to provide the impetus for not only discerning
why crime fell but also for how to pragmatically utilize this knowledge after the
world emerges from seclusion.
American Journal of Criminal Justice
Crime in Lock-Down: Theoretical Implications
During the few hours before a legal stay-at-home order was implemented, and through-
out the first few weeks that followed, it is essential to note what likely did ‘not’change.
As people around the world returned from frantic and stress-filled trips to stock up on
food and other essentials and closed the door to their residence behind them, their
biological and physiological conditions changed very little, nor did the labels attributed
to them by society, friends, or family. Poverty and inequality did not disappear or
increase immediately. It is unlikely that self-control dramatically increased either. There
were, however, things that did change; society became more disorganized, and social
influences and relationships were suddenly cut, diminished, or otherwise altered. Strain,
stress, and anomie likely increased significantly as many became fearful for the future
(both financially and physically) and estranged from family and friends whom they
could not visit physically. Further, punitive responses to crime (i.e., deterrence) were
slowed or ceased altogether as courts closed, police were encouraged to reduce contact
with the public, and thousands of prisoners were released early.
With crime declining at such a significant pace and many of the often-attributed
circumstances impacting crime staying consistent or in some cases increasing or
decreasing in a direction opposite of what many believe drives crime, many crimino-
logical theories appear to be struggling to explain the abrupt and sweeping change. We
believe the scope and nature of crime changes during the COVID-19 crisis will become
a proving ground for the many theories that attempt to explain the etiology of criminal
behavior. In the end, this naturally occurring experiment will advance our knowledge
of crime and human behavior as no other event has ever done during the era in which
criminological data were widely available.
As such, we argue that the single most salient aspect of the steep fall in crime rates
during the COVID-19 pandemic are the legal stay-at-home orders (i.e., lock-down,
shelter-in-place) implemented to slow the spread of the virus by promoting social
distancing. Stay-at-home orders were issued by most states and legally required residence
to stay within their homes except for authorized activities. Commonly, these activities
included seeking health care, purchasing food and other necessary supplies, banking, and
similar activities. The orders either outrightclosedorbyde-factoclosedbroadswathsof
the economy and impacted schools, private social gatherings, religious activities, travel,
and more. In short, these orders disrupted the daily activities of entire populations and was
the only variable that changed abruptly, just days before double-digit drops in crime
around the world. As such, we believe, the Environmental Criminology suite of perspec-
tives including; Rational Choice (Clarke & Felson, 1993) and Routine Activity (Cohen &
Felson, 1979) will emerge as frontrunners in understanding the crime changes during
COVID-19 and will provide insight how to influence crime in the future.
A Call to Examine Crime
Therefore, we offer a call for examining crime before, during, and after a government-
imposed stay-at-home order, that coincides with the COVID-19 pandemic. Specifical-
ly, we advocate for researchers to consider crime in the context of temporal shifts, in a
place-based context, to use emerging data sources, and to study crime with specificity.
American Journal of Criminal Justice
Crime Specificity
Criminologists tend to overgeneralize about crime while underestimating the enormous
specificity in offender decision making (LeClerc, & Wortley, R. (Eds.)., 2013). Even
within each crime type, the finer particulars of an offense should be studied to under-
stand how crime patterns change and shift. Specificity is even more critical when
researching crime in a pandemic as it allows for an understanding of nuanced changes,
such as opportunity structure, that would otherwise be missed. For example, the changes
in daily activities in the wake of the pandemic tend to decrease the population in non-
residential parts of the metropolis, while increasing the population in residential zones.
For example, the broad category of ‘theft’appears to be down across many cities in
the US (Ashby, 2020a). However, theft is likely not declining evenly across all
categories. Consider theft in a retail context. The retail sector has experienced an
85% decline in foot traffic after the stay-at-home orders were implemented (Jahshan,
2020); many stores are closed, and thus the opportunity for shoplifting and employee
theft are curtailed. Pietrawska et al. (2020a), for example, identified a 24% decline in
shoplifting in Los Angeles, compared to a city-wide decline of theft at only 5%.
However, theft may persist (and even see an increase) within stores that remain open
such as grocers, construction supplies, convenience stores, pharmacies, and other
‘essential’retailers. These thefts may be the result of a change in offender behavior
(i.e., shifting from targeting a specific store—now closed, to another that is open), due
to panic buying (i.e., purchasing limits on essential products may result in theft), or
impacted by reduced guardianship within the stores (e.g., short-staffed employees are
more focused on service than crime prevention).
One of the most exciting illustrations of crime specificity has to do with pocket-
picking the covert removal of a wallet from a pocket or purse in a crowded venue. This
crime thrives on a crowd, perhaps more than any other form. As noted earlier, Swedish
researchers (Gerell et al., 2020) found that pocket-picking decreased by 61% in
Stockholm during the COVID-affected period when crowd-reduction was especially
emphasized. These findings underscore the importance of linking specific changes in
routines to specific types of crime.
Theft may also be moving outside of the physical retail structure and developing in
areas where officially reported came data is not readily available. For example, before
COVID-19 package theft (e.g., packages delivered outside a residence and stolen
before the owner can retrieve them) was a growing concern, and few, if any, police
agencies kept data on the problem (Stickle, Hicks, Stickle, & Hutchinson, 2020).
However, with entire populations confined to their homes, shopping has shifted
virtually, and delivery of products has risen 74% (ACI, 2020). As a result, the
opportunity for theft of packages left unattended at a residence may be increasing
(Stickle, 2020a). While more person may be home, daily routine activities have also
been interrupted, which impact guardianship. As a result, packages left unattended for
extended periods or forgotten altogether (Stickle, 2020b).
These are just a few examples of why examining specific crime types and situations is
vital to criminology. It allows the researcher to identify nuanced changes that are important
when developing future prevention techniques and to test theoretical tools. There are, no
doubt, many factors that are impacting pandemic crime rates, and only by examining them
with specificity can researchers achieve an enhanced understanding of crime.
American Journal of Criminal Justice
Temporal Shift
Temporal understanding of crime is essential because the time of day, day of the week,
months, seasons, and other time-related factors are commonly known to impact crime;
in other words, crime is not evenly distributed across place or time (Brantingham &
Brantingham, 1995). However, stay-at-home orders that have people living, working,
eating, and finding entertainment at home as weekdays merge into weekends may cause
time distinctions to blur when speaking of crime. The change in the population’sroutine
behavior, even at home, is already being seen in online browsing habits and television
use; behavior has shifted to higher viewing rates on Mondays than on the traditional
Saturday (Comcast, 2020). To address these unusual, pandemic-generated changes in
routine activities, criminologists need to examine crime rates in a different temporal
perspective and consider the context of COVID-19 stay-at-home orders. However, there
must be more specificity than a pre and post examination of crime trends, and measure-
ments at the state and even community level are needed to ensure accuracy.
We propose the following seven important periods for identification and comparison
of crime rate changes related to the crisis (Table 1).
These measures must be tailored to individual communities or states to coincide with
routine activity trends and government orders. Period 1 should be of sufficient time to
establish some base levels of crime rates. Period 2 is where the beginning of voluntary
behavior changes is likely to be observable, somewhere around mid-February, and
extending until the government ordered quarantines for the general population. During
this time, as concern swept across the nation, many people chose to alter their lifestyles;
schools closed, and other modifications in society likely began to impact crime slowly. For
example, an early study of police calls for service by Mohler et al. (2020) found routine
activities began to change 8 to 10 days before stay-at-home orders were enacted in Los
Angeles, California, and Indianapolis, Indiana, as well as other cities and other nations.
Periods 3 and 4 are contingent onthe length of the government-ordered closures. For
example, if a state was under stay-at-home orders for 4 weeks, we recommend
examining an early period (period 3) as well as a late period (period 4) of two weeks.
Dividing the length of stay-at-home orders by half (or more if the order is longer than
six weeks) will capture the changes in routine activity as the stay-at-home orders
continue. Capturing this data in two or more periods is crucial as the longer the order
continues, the more likely people will begin to violate the order, and crime rates may
Table 1 Summary of the changes in routine activities by period, February to May, 2020
Period approximation Comments
1. Before Mid-February Little change in routine activities
2. Mid-February to Mid-March Soft changes in routine activities appear. Important markers include
closure of schools and suggested stay-at-home order
3. Mid-March to Mid-May Earlier legally imposed stay-at-home orders
4. Mid-May to End of May Later legally imposed stay-at-home orders
5. Ill-defined End of stay-at-home orders phased re-opening begins
6. Ill-defined Phased re-opening continues
American Journal of Criminal Justice
begin to change. For example, early reports in Sweden saw a slight decline in
vandalism (−4%), followed by a sharp increase after five weeks into the restrictions.
There is also likely some relationship between non-compliance and crime as Nivette
et al. (2020) found non-compliance with stay-at-home orders was associated with
delinquent behavior. While early reports have not identified the same trends in the
US, news reports during the month of May (Koetsier, 2020) indicated that a large
number of persons were emerging from homes before an official end to the stay-at-
home orders. A rise in crime may be detected because it is possible that the longer the
orders continue, the less effective they become.
Lastly, periods 5 and 6 are difficult to define as the situation is still unfolding at the
time of this publication, as a complete rescinded stay-at-home order has not occurred to
date. Moreover, it is also critical to consider that many individuals who live in an area
where the stay-at-home orders have been partially revoked may still choose not to
return to their daily lives (see a news report by Schaul et al., 2020). This is why it will
be important to capture data starting at the point of a rescinded stay-at-home order and
by measuring crime rates every few weeks after that for an extended period. These
periods may coincide with the phased re-opening plan followed by many governments
(see CDC, 2020) or within a timeframe for several weeks each, which may result in the
need to add continued periods of crime data.
Criminologists do not have to rely on the assumption that people follow stay-at-
home orders. For the first time, Mobility Trend Reports are being offered free (includ-
ing in CSV format) by both Google (2020)andApple(2020). These reports offer
aggregated movement data based on anonymized cell phone location history at the
national, state, and county levels. The data includes daily reports and includes inferred
locations (i.e., retail, grocery, parks, transit, residential, workplace). With this data, it is
possible to compare societal behavior within these recommended periods and gain a
more accurate picture of where people were and importantly when they were there.
Combined with the ability to measure compliance with movement restrictions, crimi-
nologists have the data to examine the routine activities of whole populations at a level
never before possible while overlaying crime rates for both a temporal a place-based
evaluation.
Place-Based
Studying crime based at a place is another critical part of understanding not only crime
trends but also methods to disrupt crime (Eck & Weisburd, 2015). Under the current
circumstances with people’s daily routine disrupted, this is even more important as
people shift to more time within the home, the opportunities and places for offenders
and victims to meet become limited. As a result, there is likely far less crime as people;
both victims and offenders are not together in a place for the crime to occur.
To illustrate, consider workplace violence and crime. With a significant number of
persons at home, rather than work, there is a reduced opportunity for offenders to
assault co-workers. Similarly, there is less opportunity for a victim to have a phone
stolen from the breakroom. It is important to remember that during the COVID-19
crisis, variables commonly related to many other criminological theories (i.e., poverty,
stress, self-control) have not changed to such a degree to explain the sharp reduction in
crime. Instead, the opportunity to be connected to a victim in time and place appears to
American Journal of Criminal Justice
be the most significant variable that has led to a marked reduction in the workplace and
other place-based crimes.
However, in some regards, this place-based shift may result in increased crime rates in
other areas (Roberts, 2020). For example, while digital, the internet can be classified as a
‘place’or medium for victimization to occur (Machimbarrena et al., 2018). Under the
COVID-19 stay-at-home orders, people are spending significantly more time online. By
late March, for example, cable internet usage, as reported by The Internet and Television
Association (2020), surged more than 30% and continued to grow until mid-April, which
appears to coincide with many of the stay-at-home orders. The increased time using the
internet likely leads to more opportunities for cybercrimes to occur as the victim’s virtual
presence has shifted dramatically (e.g., away from place-based crime at work or school
and to place-based crime online). Additionally,offendersmayhavealsobeenimpactedby
the COVID-19 stay-at-home orders and have increased time to identify victims.
Shifting back to a physical place and crimes, it is also important to evaluate land
usage and population density when considering crime trends. There are emerging
trends in the new COVID-19 crime data suggesting crime differences in certain places
(Ashby, 2020a). For example, public places such as stores, restaurants, and entertain-
ment areas are experiencing sharp decreases in some types of crime (Pietrawska et al.,
2020a), while crime in the home may be remaining consistent (Campbdelli, 2020;
Payne & Morgan, 2020; Shayegh & Malpede, 2020, and mix-land use may see
relatively stable or slightly increasing crime rates (Felson et al., 2020). Here again,
routine activities and rational choice perspectives may explain much of the crime in
these places. For instance, entertainment businesses and districts, along with dine-in
restaurants, were generally closed during the orders. Thus, with fewer offenders
routinely in these places and fewer victims present, crime will naturally decline.
However, a reasoning offender (Cornish & Clarke, 2014) may choose to target areas
with fewer people (i.e., guardians) such as closed malls, business parks, and other
places that may see an increase in property crimes. Additionally, mixed land usage,
especially in population-dense areas, may allow an offender to travel in areas unnoticed
easily and, therefore, present opportunities for crime (Felson et al., 2020).
Place, whether virtual or physical, is a crucial factor in crime. The COVID-19 crisis
has re-shaped the places that persons routinely visit, increasing some—home and
online, while decreasing others—work, retail, school, and entertainment. Highlighting
the role that place has played in crime rates during the pandemic should influence how
criminologists study crime in a post-pandemic world and lead to further crime reduc-
tion through place-based prevention techniques.
Data-Driven
We have listed some initial findings on crime in the COVID-19 era and also described
the need to study crime specifically, temporally, and place-based. Next, we will discuss
data for measuring crime. One problem in criminology, as in other social science fields,
is there are too many variables, too little variation, or an inability to control for specific
variables. However, in the current pandemic, these problems decrease dramatically, and
criminologists should take advantage of the favorable conditions and abundant data.
First, as described in the introduction, few variables changed during the first several
weeks of the pandemic. The most substantial change has been the stay-at-home orders,
American Journal of Criminal Justice
which impacted the routine activities of entire populations. With so few variables
changed, it should be easier to identify and measure significant and substantial changes
in crime. Second, the variation in crime rates has been drastic. On the order of 10%, 20%,
and even sometimes 60% transformation of crime patterns. These significant measurable
changes allow researchers to see ‘past’other variables that have little impact and focus on
the significant variables impacting crime. Third, with entire populations affected by the
pandemic, there is little need for controlling traditional variables such as age, gender,
education, social status, and more. The impacted population is closer to the entire
population rather than a ‘sample population,’which means it is possible to move beyond
inferential statistics and measure the actual change in the whole population.
Another challenge for criminologists is crime data. We encourage the use of four
broad categories of data, including official police reports, victim and other self-report
surveys, private or anecdotal data, and public data. Police data is an essential source
during the pandemic. However, with many agencies experiencing workforce-related
issues during the pandemic and purposely reducing the person-to-person contact to
reduce the risk of virus spread, the official police data may underreport crime more than
usual. Further, with more persons staying inside and not venturing out to school and
work, other crimes, such as intimate partner violence and abuse of children, may not be
captured through traditional reporting means. Therefore, it will be important that victim
and self-report surveys continue to be used to help capture data that official reports do
not (see Krohn, Thornberry, Gibson, & Baldwin, 2010).
Other sources of direct crime data and ancillary sources are often overlooked.
Ancillary sources of data can take the form of calls to abuse hotlines, reports on
consumer spending, internet traffic, police call for service, hospital mandatory
reporting on specific injuries, and the Bureau of Labor Statics (2020)dataoninjuries
resulting from violence at the workplace. Additionally, sources from private companies
also provide insight into crime not always reported through official channels. For
example, many retail organizations release data on crime within their stores, credit
card companies release fraud statistics, and insurance organizations publish claims
related to crime victimization. These sources may be particularly important as many
areas where crime is occurring during the COVID-19 crisis are within private spaces,
and obtaining non-police data is essential to understanding the crime shift. Lastly, other
publicly available resources should be included in the analysis as well. Specifically,
Mobility Trend Reports by Apple and Google, which provide detailed information on
population location daily that the county level. This data set, never before publicly
provided, should be used to overlay with other data (see Mohler et al., 2020).
Moving beyond the data to the methods, the circumstances of the COVID-19 crisis
has led to a naturally occurring quasi-random control trial. Because each state-issued
stay-at-home order at different times, under different circumstances, and rescinded
them at different dates, it is possible to compare crime across many population groups.
For example, Kentucky issued an order on March 26th and entered phased re-opening
on May 11th (47 days) while neighboring state Tennessee waited seven more days,
issuing a stay-at-home order on March 31st, and began a phased re-opening on April
27th, fourteen days ahead of its neighbor. These states, which share many demographic
similarities, are ideal for comparison.
In addition to the unequal start and stop dates for state-wide lock-downs, the activities
limited by the orders varied as well; for instance, some states kept parks open while others
American Journal of Criminal Justice
closed them. Similarly, some states outlawed gatherings of 10 or more, while other states
established different criteria. The response to alcohol also creates a valuable point in data
analysis. Examples abound of states that relaxed laws on alcohol sales, such as Kentucky,
which allowed for the first-time home delivery of alcohol and service of alcohol with food
take-out orders during the crisis (Minton, 2020). On the other end of the spectrum, some
states deemed alcohol ‘non-essential’but changed course after public backlash. For
example, Pennsylvania initially closed liquor stores and created a cascade of persons
traveling outside the state seeking alcohol (Thomas, 2020). Conditions such as these either
between states or even within states are plentiful and provide essential data points that
allow for an excellent comparison of crime and related factors.
The Largest Criminological Experiment in History
There is little doubt that the COVID-19 crisis will impact history on a scale not seen
since WWII. Provisional insights indicate that a substantial drop in crime is occurring
around the world and within the US. However, these reports also indicate the changes
are not even across time, place, or crime type. Therefore, we encourage criminologists
to study this crisis through the use of new and existing sources of crime data, with a
specificity of crime types, in a temporal fashion, and placed based.
Moreover, the leading feature of these crime changes will be that the government
ordered stay-at-home mandates, which impacted the routine activities of entire populations.
The variation in these orders by state and community regarding when the orders were
implemented and rescinded and what restrictions were in place has provided a naturally
occurring, quasi-randomized control experiment. For example, researchers can compare
states and communities that released prisoners early, increased or reduced alcohol avail-
ability, began lock-downs early, crime in public places as opposed to residential and mixed
land use, and operationalize many variables that were previously intangible or inarticulable.
The findings emerging from the COVID-19 crisis will impact criminological theo-
ries for the next several decades. We encourage researchers to embark on in-depth
explorations of the data made available from the pandemic and to search for not only
why, where, when, and to what extent crime fell, but also how to use this knowledge
for practical applications after the world returns to ‘normal’and concludes this exper-
iment in crime reduction and extraordinary test of human determination and resiliency.
Funding Information No funding was received for this research.
Compliance with Ethical Standards
Conflicts of Interest The authors report no conflicts of interest.
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Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Ben Stickle is an Associate Professor of Criminal Justice Administration at Middle Tennessee State Univer-
sity. He holds a Ph.D. in Justice Administration from the University of Louisville. Ben has nearly twenty years
of law enforcement and private security experience. He has published several scholarly journal articles, book
chapters, and a book. Ben’s research interests include policing, crime prevention, and property crime (metal
theft & package theft).
Marcus Felson originated the routine activity approach to crime rate analysis. He is anexpert in how to think
about crime in very tangible terms and how to reduce it using such thinking. His books include Crime and
Nature (Sage, 2006) as well as Crime and Everyday Life, now in its sixth edition. His work is increasingly
applied to crime reduction and to explain diverse crime problems, including gangs, drug sales, robberies,
burglaries, fights, organized crime, and cybercrime.
American Journal of Criminal Justice
