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Spatial analysis of outdoor indecent assault risk: a study using ambient population data

Spatiotemporal data on ambient populations have recently become widely available. Although previous studies have indicated a link between the spatial patterns of crime occurrence and ambient population distrib...

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Post-pandemic crime trends in England and Wales

This study of recorded crime trends in England & Wales spans three and a half years, that is, two covid pandemic years from March 2020  and 18 ‘post-pandemic’ months following cessation of covid restrictions...

Gender differences in online abuse: the case of Dutch politicians

Online abuse and threats towards politicians have become a significant concern in the Netherlands, like in many other countries across the world. This paper analyses gender differences in abuse received by Dut...

Online hate speech victimization: consequences for victims’ feelings of insecurity

This paper addresses the question whether and to what extent the experience of online hate speech affects victims’ sense of security. Studies on hate crime in general show that such crimes are associated with ...

Identity fraud victimization: a critical review of the literature of the past two decades

This study aims to provide an understanding of the nature, extent, and quality of the research evidence on identity fraud victimization in the US. Specifically, this article reviews, summarizes, and comments o...

Text mining domestic violence police narratives to identify behaviours linked to coercive control

Domestic and family violence (DFV) is a significant societal problem that predominantly affects women and children. One behaviour that has been linked to DFV perpetration is coercive control. While various def...

The spatial patterning of emergency demand for police services: a scoping review

This preregistered scoping review provides an account of studies which have examined the spatial patterning of emergency reactive police demand (ERPD) as measured by calls for service data. To date, the field ...

Operationalizing deployment time in police calls for service

Analyses of emergency calls for service data in the United States suggest that around 50% of dispatched police deployment time is spent on crime-related incidents. The remainder of time is spent in a social se...

Predictors of police response time: a scoping review

As rapid response has been a key policing strategy for police departments around the globe, so has police response time been a key performance indicator. This scoping review maps and assesses the variables tha...

Counterfeits on dark markets: a measurement between Jan-2014 and Sep-2015

Counterfeits harm consumers, governments, and intellectual property holders. They accounted for 3.3% of worldwide trades in 2016, having an estimated value of $509 billion in the same year. Estimations in the ...

An analysis of protesting activity and trauma through mathematical and statistical models

The effect that different police protest management methods have on protesters’ physical and mental trauma is still not well understood and is a matter of debate. In this paper, we take a two-pronged approach ...

Characteristics and associated factors of self-reported sexual aggression in the Belgian population aged 16–69

Sexual violence is a major public health, societal, and judicial problem worldwide. Studies investigating the characteristics of its perpetrators often rely on samples of convicted offenders, which are biased ...

Do police stations deter crime?

The introduction of community policing led to a significant increase in the number of police stations, particularly in urban settings. Police stations are largely assumed to have an impact on crime but there a...

Exploring the impact of measurement error in police recorded crime rates through sensitivity analysis

It is well known that police recorded crime data is susceptible to substantial measurement error. However, despite its limitations, police data is widely used in regression models exploring the causes and effe...

Measuring the impact of the state of emergency on crime trends in Japan: a panel data analysis

City-specific temporal analysis has been commonly used to investigate the impact of COVID-19-related behavioural regulation policies on crime. However, these previous studies fail to consider differences in th...

Domestic abuse in the Covid-19 pandemic: measures designed to overcome common limitations of trend measurement

Research on pandemic domestic abuse trends has produced inconsistent findings reflecting differences in definitions, data and method. This study analyses 43,488 domestic abuse crimes recorded by a UK police fo...

Circumstances, policing, and attrition of multiple compared to single perpetrator rape cases within the South African criminal justice system

Research into the circumstances of rape, and criminal justice system responses, is pivotal to informing prevention and improving the likelihood of justice for victims. In this paper, we explore the differences...

Overlapped Bayesian spatio-temporal models to detect crime spots and their possible risk factors based on the Opole Province, Poland, in the years 2015–2019

Geostatistical methods currently used in modern epidemiology were adopted in crime science using the example of the Opole province, Poland, in the years 2015–2019. In our research, we applied the Bayesian spat...

Why do people legitimize and cooperate with the police? Results of a randomized control trial on the effects of procedural justice in Quito, Ecuador

The present study employs a randomized control trial design to evaluate the impact of deterrence and procedural justice on perceptions of legitimacy and cooperation with law enforcement among individuals in Qu...

The value of criminal history and police intelligence in vetting and selection of police

Despite decades of research considering police misconduct, there is still little consensus on officer characteristics associated with misconduct, and best practice for detection and prevention. While current r...

Do increases in the price of fuel increase levels of fuel theft? Evidence from England and Wales

Fuel prices have increased sharply over the past year. In this study we test the hypothesis that increases in the price of fuel are associated with increases in motorists filling their fuel tank and driving of...

A field-experiment testing the impact of a warrant service prioritization strategy for police patrol officers

The objective of this experiment was to test the efficacy of providing prioritized warrant lists to patrol officers. A field experiment was carried out with the Greensboro (NC) Police Department. Warrant risk ...

Going dark? Analysing the impact of end-to-end encryption on the outcome of Dutch criminal court cases

Law enforcement agencies struggle with criminals using end-to-end encryption (E2EE). A recent policy paper states: “while encryption is vital and privacy and cyber security must be protected, that should not c...

Considering the lip print patterns of Ibo and Hausa Ethnic groups of Nigeria: checking the wave of ethnically driven terrorism

Lip print of an individual is distinct and could be a useful form of evidence to identify the ethnicity of a terrorist.

Towards cyber-biosecurity by design: an experimental approach to Internet-of-Medical-Things design and development

The introduction of the internet and the proliferation of internet-connected devices (IoT) enabled knowledge sharing, connectivity and global communications. At the same time, these technologies generated a cr...

Police practitioner views on the challenges of analysing and responding to knife crime

Knife crime remains a major concern in England and Wales. Problem-oriented and public health approaches to tackling knife crime have been widely advocated, but little is known about how these approaches are un...

Supporting crime script analyses of scams with natural language processing

In recent years, internet connectivity and the ubiquitous use of digital devices have afforded a landscape of expanding opportunity for the proliferation of scams involving attempts to deceive individuals into...

Weather and crime: a systematic review of the empirical literature

The weather-crime association has intrigued scholars for more than 150 years. While there is a long-standing history of scholarly interest in the weather-crime association, the last decade has evidenced a mark...

Unpacking the police patrol shift: observations and complications of “electronically” riding along with police

As frontline responders, patrol officers exist at the core of policing. Little remains known, however, about the specific and nuanced work of contemporary patrol officers and their shift characteristics. Drawi...

Spatial distribution and developmental trajectories of crime versus crime severity: do not abandon the count-based model just yet

A new body of research that focuses on crime harm scores rather than counts of crime incidents has emerged. Specifically in the context of spatial analysis of crime, focusing on crime harm suggests that harm i...

Analysis of the risk of theft from vehicle crime in Kyoto, Japan using environmental indicators of streetscapes

With the advent of spatial analysis, the importance of analyzing crime patterns based on location has become more apparent. Previous studies have advanced our understanding of the factors associated with crime...

A multilevel examination of the association between COVID-19 restrictions and residence-to-crime distance

Restrictions resulting from the COVID-19 pandemic interrupted people’s daily routine activities. Rooted in crime pattern and routine activity theories, this study tests whether the enactment of a Safer-at-Home...

Correction: Offline crime bounces back to pre-COVID levels, cyber stays high: interrupted time-series analysis in Northern Ireland

The original article was published in Crime Science 2021 10 :26

Theorizing globally, but analyzing locally: the importance of geographically weighted regression in crime analysis

Theoretical relationships with crime across cities are explicitly or implicitly assumed to be the same in all places: a one-unit change in X leads to a β change in Y. But why would we assume the impact of unem...

Blowing in the wind? Testing the effect of weather on the spatial distribution of crime using Generalized Additive Models

Oslo, the capital of Norway, is situated in a North European cool climate zone. We investigate the effect of weather on the overall level of crime in the city, as well as the impact of different aspects of wea...

Illegal waste fly-tipping in the Covid-19 pandemic: enhanced compliance, temporal displacement, and urban–rural variation

Illegal dumping of household and business waste, known as fly-tipping in the UK, is a significant environmental crime. News agencies reported major increases early in the COVID-19 pandemic when waste disposal ...

Need to go further: using INLA to discover limits and chances of burglaries’ spatiotemporal prediction in heterogeneous environments

Near-repeat victimization patterns have made predictive models for burglaries possible. While the models have been implemented in different countries, the results obtained have not always been in line with ini...

Anti-social behaviour in the coronavirus pandemic

Anti-social behaviour recorded by police more than doubled early in the coronavirus pandemic in England and Wales. This was a stark contrast to the steep falls in most types of recorded crime. Why was ASB so d...

Say NOPE to social disorganization criminology: the importance of creators in neighborhood social control

Despite decades of research into social disorganization theory, criminologists have made little progress developing community programs that reduce crime. The lack of progress is due in part to faulty assumptio...

Different places, different problems: profiles of crime and disorder at residential parcels

Certain places generate inordinate amounts of crime and disorder. We examine how places differ in their nature of crime and disorder, with three objectives: (1) identifying a typology of profiles of crime and ...

Alone against the danger: a study of the routine precautions taken by voluntary sex workers to avoid victimisation

This article explores the routine precautions taken by sex workers (SW) in Switzerland, a country in which sex work is a legal activity. It is based on approximately 1100 h of non-systematic participant observ...

Explaining offenders’ longitudinal product-specific target selection through changes in disposability, availability, and value: an open-source intelligence web-scraping approach

To address the gap in the literature and using a novel open-source intelligence web-scraping approach, this paper investigates the longitudinal relationships between availability, value, and disposability, and...

Cryptocurrencies and future financial crime

Cryptocurrency fraud has become a growing global concern, with various governments reporting an increase in the frequency of and losses from cryptocurrency scams. Despite increasing fraudulent activity involvi...

More crime in cities? On the scaling laws of crime and the inadequacy of per capita rankings—a cross-country study

Crime rates per capita are used virtually everywhere to rank and compare cities. However, their usage relies on a strong linear assumption that crime increases at the same pace as the number of people in a reg...

Offline crime bounces back to pre-COVID levels, cyber stays high: interrupted time-series analysis in Northern Ireland

Much research has shown that the first lockdowns imposed in response to the COVID-19 pandemic were associated with changes in routine activities and, therefore, changes in crime. While several types of violent...

The Correction to this article has been published in Crime Science 2022 11 :11

Identifying seasonal spatial patterns of crime in a small northern city

To explore spatial patterns of crime in a small northern city, and assess the degree of similarity in these patterns across seasons.

The impact of the COVID-19, social distancing, and movement restrictions on crime in NSW, Australia

The spread of COVID-19 has prompted Governments around the world to impose draconian restrictions on business activity, public transport, and public freedom of movement. The effect of these restrictions appear...

The new normal of web camera theft on campus during COVID-19 and the impact of anti-theft signage

The opportunity for web camera theft increased globally as institutions of higher education transitioned to remote learning during COVID-19. Given the thousands of cameras currently installed in classrooms, ma...

The effect of the COVID-19 pandemic on mental health calls for police service

Drawing upon seven years of police calls for service data (2014–2020), this study examined the effect of the COVID-19 pandemic on calls involving persons with perceived mental illness (PwPMI) using a Bayesian ...

A victim-centred cost–benefit analysis of a stalking prevention programme

Research suggests that stalking inflicts great psychological and financial costs on victims. Yet costs of victimisation are notoriously difficult to estimate and include as intangible costs in cost–benefit ana...

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What the data says (and doesn’t say) about crime in the united states.

From the first day of his presidency to his campaign for reelection, Donald Trump has sounded the alarm about crime in the United States. Trump vowed to end “ American carnage ” in his inaugural address in 2017. This year, he ran for reelection on a platform of “ law and order .”

As Trump’s presidency draws to a close, here is a look at what we know – and don’t know – about crime in the U.S., based on a Pew Research Center analysis of data from the federal government and other sources.

Crime is a regular topic of discussion in the United States. We conducted this analysis to learn more about U.S. crime patterns and how those patterns have changed over time.

The analysis relies on statistics published by the Federal Bureau of Investigation (FBI) and the Bureau of Justice Statistics (BJS), the statistical arm of the U.S. Department of Justice. FBI statistics were accessed through the Crime Data Explorer . BJS statistics were accessed through the National Crime Victimization Survey data analysis tool . Information about the federal government’s transition to the National Incident-Based Reporting System was drawn from the FBI and BJS, as well as from media reports.

To measure public attitudes about crime in the U.S., we relied on survey data from Gallup and Pew Research Center.

How much crime is there in the U.S.?

It’s difficult to say for certain. The two primary sources of government crime statistics – the Federal Bureau of Investigation (FBI) and the Bureau of Justice Statistics (BJS) – both paint an incomplete picture, though efforts at improvement are underway.

The FBI publishes annual data on crimes that have been reported to the police, but not those that haven’t been reported. The FBI also looks mainly at a handful of specific violent and property crimes, but not many other types of crime, such as drug crime. And while the FBI’s data is based on information it receives from thousands of federal, state, county, city and other police departments, not all agencies participate every year. In 2019, the most recent full year available, the FBI received data from around eight-in-ten agencies .

BJS, for its part, tracks crime by fielding a large annual survey of Americans ages 12 and older and asking them whether they were the victim of a crime in the past six months. One advantage of this approach is that it captures both reported and unreported crimes. But the BJS survey has limitations of its own. Like the FBI, it focuses mainly on a handful of violent and property crimes while excluding other kinds of crime. And since the BJS data is based on after-the-fact interviews with victims, it cannot provide information about one especially high-profile type of crime: murder.

All those caveats aside, looking at the FBI and BJS statistics side-by-side does give researchers a good picture of U.S. violent and property crime rates and how they have changed over time.

Which kinds of crime are most and least common?

Property crime in the U.S. is much more common than violent crime. In 2019, the FBI reported a total of 2,109.9 property crimes per 100,000 people, compared with 379.4 violent crimes per 100,000 people.  

By far the most common form of property crime in 2019 was larceny/theft, followed by burglary and motor vehicle theft. Among violent crimes, aggravated assault was the most common offense, followed by robbery, rape, and murder/non-negligent manslaughter.

BJS tracks a slightly different set of offenses from the FBI, but it finds the same overall patterns, with theft the most common form of property crime in 2019 and assault the most common form of violent crime.

How have crime rates in the U.S. changed over time?

Both the FBI and BJS data show dramatic declines in U.S. violent and property crime rates since the early 1990s, when crime spiked across much of the nation.

Using the FBI data, the violent crime rate fell 49% between 1993 and 2019, with large decreases in the rates of robbery (-68%), murder/non-negligent manslaughter (-47%) and aggravated assault (-43%). (It’s not possible to calculate the change in the rape rate during this period because the FBI revised its definition of the offense in 2013 .) Meanwhile, the property crime rate fell 55%, with big declines in the rates of burglary (-69%), motor vehicle theft (-64%) and larceny/theft (-49%).

Using the BJS statistics, the declines in the violent and property crime rates are even steeper than those reported by the FBI. Per BJS, the overall violent crime rate fell 74% between 1993 and 2019, while the property crime rate fell 71%.

How do Americans perceive crime in their country?

Americans tend to believe crime is up, even when the data shows it is down.

In 20 of 24 Gallup surveys conducted since 1993, at least 60% of U.S. adults have said there is more crime nationally than there was the year before, despite the generally downward trend in national violent and property crime rates during most of that period.

While perceptions of rising crime at the national level are common, fewer Americans believe crime is up in their own communities. In all 23 Gallup surveys that have included the question since 1993, no more than about half of Americans have said crime is up in their area compared with the year before.

This year, the gap between the share of Americans who say crime is up nationally and the share who say it is up locally (78% vs. 38%) is the widest Gallup has ever recorded .

Public attitudes about crime also differ by Americans’ partisan affiliation , race and ethnicity and other factors. For example, in a summer Pew Research Center survey , 74% of registered voters who support Trump said violent crime was “very important” to their vote in this year’s presidential election, compared with a far smaller share of Joe Biden supporters (46%).

How does crime in the U.S. differ by demographic characteristics?

There are some demographic differences in both victimization and offending rates, according to BJS.

In its 2019 survey of crime victims , BJS found wide differences by age and income when it comes to being the victim of a violent crime. Younger people and those with lower incomes were far more likely to report being victimized than older and higher-income people. For example, the victimization rate among those with annual incomes of less than $25,000 was more than twice the rate among those with incomes of $50,000 or more.

There were no major differences in victimization rates between male and female respondents or between those who identified as White, Black or Hispanic. But the victimization rate among Asian Americans was substantially lower than among other racial and ethnic groups.

When it comes to those who commit crimes, the same BJS survey asks victims about the perceived demographic characteristics of the offenders in the incidents they experienced. In 2019, those who are male, younger people and those who are Black accounted for considerably larger shares of perceived offenders in violent incidents than their respective shares of the U.S. population. As with all surveys, however, there are several potential sources of error, including the possibility that crime victims’ perceptions are incorrect.

How does crime in the U.S. differ geographically?

There are big differences in violent and property crime rates from state to state and city to city.

In 2019, there were more than 800 violent crimes per 100,000 residents in Alaska and New Mexico, compared with fewer than 200 per 100,000 people in Maine and New Hampshire, according to the FBI .

Even in similarly sized cities within the same state, crime rates can vary widely. Oakland and Long Beach, California, had comparable populations in 2019 (434,036 vs. 467,974), but Oakland’s violent crime rate was more than double the rate in Long Beach. The FBI notes that various factors might influence an area’s crime rate, including its population density and economic conditions.

See also: Despite recent violence, Chicago is far from the U.S. ‘murder capital’

What percentage of crimes are reported to police, and what percentage are solved?

Most violent and property crimes in the U.S. are not reported to police, and most of the crimes that are reported are not solved.

In its annual survey, BJS asks crime victims whether they reported their crime to police or not. In 2019, only 40.9% of violent crimes and 32.5% of household property crimes were reported to authorities. BJS notes that there are a variety of reasons why crime might not be reported, including fear of reprisal or “getting the offender in trouble,” a feeling that police “would not or could not do anything to help,” or a belief that the crime is “a personal issue or too trivial to report.”

Most of the crimes that are reported to police, meanwhile, are not solved , at least based on an FBI measure known as the clearance rate. That’s the share of cases each year that are closed, or “cleared,” through the arrest, charging and referral of a suspect for prosecution, or due to “exceptional” circumstances such as the death of a suspect or a victim’s refusal to cooperate with a prosecution. In 2019, police nationwide cleared 45.5% of violent crimes that were reported to them and 17.2% of the property crimes that came to their attention.

Both the percentage of crimes that are reported to police and the percentage that are solved have remained relatively stable for decades.

Which crimes are most likely to be reported to police, and which are most likely to be solved?

Around eight-in-ten motor vehicle thefts (79.5%) were reported to police in 2019, making it by far the most commonly reported property crime tracked by BJS. Around half (48.5%) of household burglary and trespassing offenses were reported, as were 30% of personal thefts/larcenies and 26.8% of household thefts.

Among violent crimes, aggravated assault was the most likely to be reported to law enforcement (52.1%). It was followed by robbery (46.6%), simple assault (37.9%) and rape/sexual assault (33.9%).

The list of crimes cleared by police in 2019 looks different from the list of crimes reported. Law enforcement officers were generally much more likely to solve violent crimes than property crimes, according to the FBI.

The most frequently solved violent crime tends to be homicide. Police cleared around six-in-ten murders and non-negligent manslaughters (61.4%) last year. The clearance rate was lower for aggravated assault (52.3%), rape (32.9%) and robbery (30.5%).

When it comes to property crime, law enforcement agencies cleared 18.4% of larcenies/thefts, 14.1% of burglaries and 13.8% of motor vehicle thefts.

Is the government doing anything to improve its crime statistics?

Yes. The FBI has long recognized the limitations of its current data collection system and is planning to fully transition to a more comprehensive system beginning in 2021.

The new system, known as the National Incident-Based Reporting System (NIBRS), will provide information on a much larger number of crimes , as well as details such as the time of day, location and types of weapons involved, if applicable. It will also provide demographic data, such as the age, sex, race and ethnicity of victims, known offenders and arrestees.

One key question looming over the transition is how many police departments will participate in the new system, which has been in development for decades. In 2019, the most recent year available, NIBRS received violent and property crime data from 46% of law enforcement agencies, covering 44% of the U.S. population that year . Some researchers have warned that the transition to a new system could leave important data gaps if more law enforcement agencies do not submit the requested information to the FBI.

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U.S. public divided over whether people convicted of crimes spend too much or too little time in prison

What we know about the increase in u.s. murders in 2020, america’s incarceration rate falls to lowest level since 1995, under trump, the federal prison population continued its recent decline, trump used his clemency power sparingly despite a raft of late pardons and commutations, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

April 11, 2024

Why We Believe the Myth of High Crime Rates

The crime issue, a focus of the 2024 presidential election, is sometimes rooted in the misplaced fears of people who live in some of the safest places

By Sara Novak

David Wall/Getty Images

Americans are convinced that they are living in a world ravaged by crime. In major cities, we fear riding public transportation or going out after dark. We buy weapons for self-defense and skip our nightly jogs . Next to the weather, the explosion of crime is a favorite topic of conversation. The overwhelming consensus is that crime is only getting worse. According to a Gallup poll, in late 2022, 78 percent of Americans contended that there was more crime than there used to be.

These perceptions would make sense if they were accurate, but they aren’t. Crime, in fact, is down in the U.S., rivaling low levels that haven’t been seen since the 1960s. According to FBI data , violent crime rates dropped by 8 percent and property crime dropped by about 6 percent by the third quarter of last year, compared with the same period in 2022. Still, the reality of these optimistic statistics doesn’t quell people’s fears.

New York City is a prime example. Crime was down by 6 percent in July 2023 from a year earlier. Specifically, murder was down by 11 percent, rape was down by 11 percent, and robbery was down by 6 percent. Yet at the time that these statistics were released in 2023, a poll of New Yorkers’ feelings around crime painted a grim picture of a city riddled with violence. The poll found that 61 percent of New Yorkers were worried about being the target of crime and that 36 percent fretted about the safety of public places. It is true that crime increased during the pandemic, and revising the way we view public safety after such a spike in crime statistics has ended happens at a slow pace. The pandemic is a case in point. The figures on major crime perceptions have remained inflated for years.

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The reasons for such misperceptions are manifold. Crime tends to concentrate in certain places, mostly within poorer neighborhoods in cities such as Baltimore, New Orleans, Detroit, Birmingham, Ala., and Memphis, Tenn. This means that most of us will never experience it. Only 2 percent of people are ever affected by violent crime, and 15 percent are affected by any type of misconduct. Perceptions matter because in most cases, they are the sole basis for the fears that fuel the idea that crime is rampant. That can affect people in different ways. The older you are, for example, the more likely you are to fear crime even though you’re half as likely to ever experience it , compared with other age groups.

Even more surprisingly, if you live in a part of the country with little crime, you’re probably more frightened of it than people who actually live in the relatively few neighborhoods where it is commonplace, such as the Belmont neighborhood in Detroit or Hopkins–Middle East in Baltimore, where violent crimes are respectively 150 and 300 times more likely to happen, compared with other neighborhoods. A study published in the April 2018 issue of the journal Humanities and Social Sciences Communications found that “significant levels of fear are often reported by people who enjoy low levels of victimization.” Study co-author Steven Bishop , a social science expert at University College London, says that if you experience crime or the threat of it more often, you’re more likely to have adjusted your fears in line with reality. In neighborhoods with more crime, people harness their expectations and avoid the areas where criminals congregate. But when you’re never exposed to crime, you’re a poor judge of the risk of encountering it.

Oftentimes, our perceptions of crime are built around an imaginary “elsewhere” in which, in the most extreme scenario, civil order has collapsed, says Wesley G. Skogan , a professor of crime policy and the politics of crime at Northwestern University. When we’re asked about our own neighborhood, for example, we’re more likely to be moderately realistic about crime levels, compared with when we’re asked about other parts of the country. “Within your neighborhood, you’re talking about your own experiences and the experiences of those whom you know,” Skogan says. In other words, your own insights into levels of public safety are the most accurate.

There are many reasons for these attitudes. Partisanship plays a growing role in fueling these perspectives. Our perspectives on crime are shaped by the politics of the moment — and have been for some time. During both George H.W. Bush's and Bill Clinton's presidencies, opposing parties held similar views on crime. But around 2000, perspectives began to depend on who was in power. According to the 2022 Gallup Poll, with President Joe Biden in the White House, 73 percent of Republicans said crime in their area was growing while only 42 percent of Democrats agreed. “Crime has become a big partisan split with the biggest gap in history happening during the Biden administration,” says Skogan. What’s more, Republicans are also more likely to be somewhat older, which could be a factor contributing to their fear of crime. ( The average Republican is 50 years old , while the average Democrat is 47.)

Watershed events such as September 11 also contribute to how we think about crime. According to John Roman , director of the Center on Public Safety & Justice at NORC at the University of Chicago, the 9/11 attacks marked a period under the George W. Bush administration when our views about crime began to fall out of line with reality. Roman contends that, as a nation, we didn’t foresee the largest terrorist attack in our country’s history, and that makes us worry that even when crime statistics show a reduction, an unforeseen event may still strike without warning. “Ever since 9/11 we’ve been bombarded with warnings and messages that didn’t exist before,” Roman says. Every time you’re in an airport or train station, there are warning signs that say things like “if you see something, say something”—reminders that lead to hypervigilance.

After 9/11 the federal government took on a partial role as public safety overseer, instituting a color-coded warning system to alert us to pending terrorist attacks. But that warning system is gone—and according to Roman, people have been left to make their own judgments about what it means to be safe, which can translate into the feeling of having to be on guard all the time. Similar issues arise when changes are made in policing. More and more cities have the police patrol with their lights flashing; while the goal is to raise police visibility, that tactic can result in the opposite of its intended effect.

Additionally, features that create a sense of disorder within a given neighborhood—for example, graffiti, broken-down buildings and trash—are often wrongly associated with an increased risk of crime. According to a September 2023 study published in the journal Landscape and Urban Planning , “public space regeneration significantly improves safety perceptions for both genders.” Seeing drug use in public places, graffiti and people sleeping on public transportation all send psychological warning signs to the average person who’s just trying to get home from work. Still, it’s largely inaccurate messaging, in Roman’s view. “Disorder and danger really aren’t as highly correlated as people think,” he says.

Media reports contribute to these flawed perceptions. Crimes that happen halfway across the globe have no real impact on our personal experience of safety on the street but can still weigh heavily on our psyche, Bishop says. This is also true for social media. “Overall, media and social media influenced perceptions of how frequently crime occurs,” reported a September 2017 study published in the American International Journal of Social Science .

Pablo Navarrete Hernandez of the University of Sheffield in England, lead author of the 2023 Landscape and Urban Planning study, says that misplaced fears have been shown to have a major impact on perceptions about the neighborhoods in which we live and how public resources are allocated to them. Spending on police for neighborhoods with relatively low crime rates can divert expenditures from needier areas.

Misperceptions also affect those who end up alone and indoors because of fears of nonexistent crime. Parents may be afraid to take their kids to the park or on walks around the neighborhood. Fears of taking the metro or the bus may keep us at home. And older people may forgo their favorite hobbies and social contacts, contributing to the national epidemic of loneliness and the health risks that come with it. In the end, fear begins to hold us hostage much more than the risk of crime ever could.

Crime Rates in a Pandemic: the Largest Criminological Experiment in History

• Published: 16 June 2020
• Volume 45 , pages 525–536, ( 2020 )

Cite this article

• Ben Stickle   ORCID: orcid.org/0000-0001-8561-2070 1 &
• Marcus Felson   ORCID: orcid.org/0000-0003-3173-072X 2  

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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.

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The COVID-19 pandemic of 2020 is unquestionably one of the most significant world-wide events in recent history, impacting culture, government operations, crime, economics, 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, governments began legally mandating persons to stay-at-home to reduce the transmission 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 fluctuated 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 were down 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, and Xu ( 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.

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. Specifically, 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 throughout 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’s movement). Specifically, the researchers found residential burglary fell by 23%, commercial burglary 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 unequally 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 experience 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.

Crime in Lock-Down: Theoretical Implications

During the few hours before a legal stay-at-home order was implemented, and throughout 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 criminological 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 outright closed or by de-facto closed broad swaths of 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 perspectives 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. Specifically, 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.

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 understand 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.

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’s routine 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 measurements 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 on the 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 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 (including in CSV format) by both Google ( 2020 ) and Apple ( 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, criminologists 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 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, offenders may have also been impacted by 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 entertainment 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 reduction 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, 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 ) data on injuries 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 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 availability, 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 theories 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 experiment in crime reduction and extraordinary test of human determination and resiliency.

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Stickle, B., Felson, M. Crime Rates in a Pandemic: the Largest Criminological Experiment in History. Am J Crim Just 45 , 525–536 (2020). https://doi.org/10.1007/s12103-020-09546-0

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Using Research to Improve Hate Crime Reporting and Identification

Hate crimes harm whole communities. They are message crimes that tell all members of a group—not just the immediate victims—that they are unwelcome and at risk.

The damage that bias victimization causes multiplies when victims and justice agencies don’t recognize or report hate crimes as such. In addition, in cases for which law enforcement agencies fail to respond to or investigate hate crimes, relationships between law enforcement and affected communities can suffer, and public trust in police can erode. [1]

While it is known that hate crimes are underreported throughout the United States, there is not a clear understanding of exactly why reporting rates are low, to what extent, and what might be done to improve them. An even more elementary question, with no single answer, is: What constitutes a hate crime? Different state statutes and law enforcement agencies have different answers to that question, which further complicates the task of identifying hate crimes and harmonizing hate crime data collection and statistics.

See "Hate Crimes: A Distinct Category."

A recent series of evidence-based research initiatives supported by the National Institute of Justice (NIJ) is helping to narrow this critical knowledge gap and illuminate a better path forward. The study findings fill in vital details on causes of hate crime underreporting in various communities, including

• hate crime victims’ reluctance to engage with law enforcement;
• victims’ and law enforcement agencies’ inability to recognize certain victimizations as hate crimes;
• a very large deficit of hate crime reporting by law enforcement agencies of all sizes; and
• variations in hate crime definitions across jurisdictions.

Significant insights to emerge from those studies include the following: [2]

• A growing number of members of the Latino community, particularly those who recently immigrated to the United States, reported experiencing bias victimization. (But Black communities endure more hate crimes than any other racial or ethnic group.)
• Many Latino individuals, especially immigrants, tend to report bias victimization only to friends and family. They are often highly reluctant to share incidents with law enforcement or other authorities.
• LGBTQ+ community members also reported an elevated rate of bias victimization. Some victims hesitate to report hate crimes to authorities out of fear of reprisals from law enforcement or because, among other reasons, they don’t want their sexual orientation or gender identity exposed.
• Many hate crimes, particularly those targeting the LGBTQ+ community, are the product of mixed motivations—for example, hate and theft. This likely results from a perception that certain victim groups are vulnerable and less likely to report the crimes.
• Law enforcement officers often lack the training and knowledge needed to investigate, identify, and report hate crimes. The presence of a dedicated officer or unit enhances a law enforcement agency’s ability to identify, respond to, and report hate crimes.
• Law enforcement agencies with policies in place that support hate crime investigation and enforcement are more likely to report investigating possible hate crimes in their jurisdiction.

In the end, knowledge gained from the NIJ-supported research on bias victimization and hate crime can strengthen hate crime recognition, reporting, and response.

See "Hate Crime vs. Bias Victimization."

Hate Crime Reporting Deficit Driven by Fear, Lack of Knowledge

Federal data captures roughly 1 in 31 hate crimes.

The disparity between the number of hate crime victimizations that actually occur and the number reported by law enforcement is vast and long-standing. As hate crimes continue to rise in the United States, especially in vulnerable populations, the search for ways to reduce that disparity becomes more urgent.

A representative sample of hate crime victimizations across the United States, collected from the National Crime Victimization Survey, revealed that only a small portion of all hate crimes find their way into official hate crime reporting. [3] An annual average of 243,770 hate crime victimizations of persons 12 or older occurred between 2010 and 2019. [4] In the same period, law enforcement agencies reported an annual average of 7,830 hate crimes to the FBI’s Hate Crime Statistics program. Those figures suggest that roughly 1 of every 31 hate crimes is captured in U.S. federal statistics.

The FBI has published hate crime statistics provided by law enforcement since 1996. However, submitting hate crime data to the FBI is voluntary, and many state and local law enforcement agencies either report that their jurisdictions experience no hate crimes or do not report any hate crime data. [5]

Three Conditions for a Hate Crime to Enter National Statistics

The overall investigation and prosecution of hate crimes suffer from the prevalence of inaccurate hate crime data. The COVID-19 Hate Crimes Act of 2021 acknowledges that incomplete data from federal, state, and local jurisdictions have hindered our understanding of hate crimes. [6]  Without a full, data-informed understanding of the problem, law enforcement and communities will be unable to provide an adequate response.

Three steps must occur for federal statistics to capture a hate crime incident.

• A victim, a victim’s friend or family member, or another person with knowledge of the incident must report the incident to law enforcement.
• Upon receiving an incident report, law enforcement must recognize and record it as a hate crime by establishing sufficient evidence through an investigation. [7]
• The law enforcement agency must report the hate crime to the FBI’s Uniform Crime Reporting Program.

Reporting barriers are present at each step of the process, which results in chronic and acute underreporting of hate crimes.

Dealing With Divergent Hate Crime Definitions

Although the FBI has a definition for hate crimes, their definition only affects state data-reporting obligations. The FBI definition has no impact on states’ own criminal code definitions. State and local hate crime definitions vary widely in terms of whom they protect and the types of offenses they include. [8]  The varying hate crime definitions make it challenging to obtain an even-handed and reliable summary of hate crime statistics across jurisdictions. When recording cases for the FBI, law enforcement agencies are required to adhere to the federal definition of offenses and protected groups. [9]  An offense that constitutes a federal hate crime may not constitute a hate crime in a state or local jurisdiction; the reverse could also be true. As a result, hate crime counts based on jurisdiction-specific definitions are not always comparable to counts reported by the FBI.

Understanding Victim Reluctance to Report Hate Crimes

NIJ-sponsored research on hate crimes that affect Latino and LGBTQ+ communities suggests that many factors influence whether individuals who experience or witness hate crimes report them to law enforcement. Those factors vary across communities.

Researchers at Northeastern University, the University of Massachusetts Lowell, the University of Texas Medical Branch, and the University of Delaware conducted a study of victimization bias affecting three large, geographically diverse Latino populations. The study found that victims who experienced bias victimization overwhelmingly sought help from friends or family and not from formal authorities, particularly law enforcement. [10] The report rates to formal authorities by nonimmigrant and immigrant Latinos were similar, though nonimmigrant Latinos were more likely than immigrant Latinos to report experiencing bias victimization. It’s important to note that the Latino community is large and varied in the United States, and victimization bias varies by nature and degree across Latino communities. Many Latino study participants said that their past experiences as victims of personal or indirect discrimination have made them less willing to report their bias victimization to authorities or to trust those outside of their community.

Among Latino populations, several factors influenced their reluctance to contact law enforcement about hate crimes, including concern over retaliation by the offending party, harassment by police, and worries over the victim’s immigration status.

Florida International University conducted a study of LGBTQ+ Latinos in Miami, Florida, that established an additional factor that inhibited victim reporting of hate crimes to law enforcement: concern about the consequences of revealing their sexual orientation or gender identity. [11] The study also found that friends’ encouragement to report a crime was “by far” the strongest predictor of hate crime reporting, which increased the likelihood of the victim reporting the crime at least ten-fold.

Multiple Sources of Initial Hate Crime Reporting to Law Enforcement

Although it is vital for victims to report hate crimes, it is not the only way that law enforcement finds out about these types of crimes. The National Hate Crime Investigation Study found that, of all incidents reported to law enforcement in a nationally representative sample, victims reported 45 percent of those hate crimes and other individuals reported 52 percent. [12]

The Miami-based study reported that criminal justice practitioners perceived that law enforcement initiates most hate crime cases in response to media coverage of bias-motivated events rather than in response to victims’ reporting. 13

Low rates of formal reporting obscure the significant impact that hate crimes have on victims. Bias victimization can be just as, or more, damaging to Latino victims than other types of victimization, such as assault or theft. Among the three large metro Latino communities that the Latino bias research study examined, bias victimization had more of a mental health impact on community members than other forms of victimization. [14] In fact, bias victimization is unique in its negative impact on mental health. This has notable implications for both prevention and intervention within the community.

The Miami-Dade study of LGBTQ+ Latino individuals reported these other consequences of bias victimization (perhaps influenced by mental health impacts):

• Victims began to avoid LGBTQ+ venues or friends (13 percent).
• Victims had to change their housing (23 percent).
• Victims tried to act more “straight” (35 percent).

The strongest predictor of a victim changing their housing was that the victim experienced a hate crime involving the use of a weapon.

Recognizing Hate Crime Incidents

It isn’t enough for a law enforcement agency to receive a report of a hate crime incident if the agency doesn’t recognize and report the incident as such. But identifying a bias motivation can be challenging. It’s not always clear what motivated a person to commit a crime, and other factors unrelated to bias may mask an incident’s hate-based status. Further, it’s not typical for law enforcement to be required to identify a motive.

Mixed-Motive Hate Offenses: Choosing Victims for Their Vulnerability

Bias motives can emerge during disputes or incidents that are unrelated to bias, which potentially complicates law enforcement’s ability to identify a motive. NIJ-sponsored research by the National Consortium for the Study of Terrorism and Responses to Terrorism (START), a research center at the University of Maryland, found that these mixed-motive hate offenses are common. [15] START developed a database known as BIAS (the Bias Incidents and Actors Study), which collected information from 960 adult individuals who committed hate crimes from 1990 to 2018.

BIAS found that almost a quarter of hate crimes targeting victims due to their sexual orientation or gender had mixed motives. Additionally, nearly all hate crimes that targeted persons because of their age or physical or mental disabilities had mixed motives, such as a combination of hate and theft. The researchers noted that, in those cases in particular, the crime likely results from the fact that the person committing it perceives that certain victim groups are vulnerable and less inclined to report incidents to authorities. The study also found that mixed-motive hate crimes were more likely to be spontaneous or otherwise unpredictable than crimes motivated only by bias.

Varying Hate-Based Forms of Messaging – How to Identify a Crime as Hate-Motivated

A University of New Hampshire research team identified the top four indicators of hate motivation that law enforcement identified and reported in the National Hate Crime Investigation Study (NHCIS):

• Hate-related verbal comments (reported by victims in 51.83 percent of hate crimes in the NHCIS database)
• Victim belief that they were targeted because of hate or bias (28.96 percent)
• Hate-related written comments (24.75 percent)
• Hate-related drawings or graffiti found at the crime scene (23.39 percent) [16]

Characteristics of Primary Suspects in Hate Crime Investigations

The NHCIS examined characteristics of suspects from a sample of 783 hate crime investigations in 2018 where law enforcement identified a suspect. [17] The primary suspects were white in nearly three-quarters (73.69 percent) of those cases. See Table 1 for a breakdown of characteristics of primary suspects.

Table Note: Percentages for primary suspect gender, race/ethnicity, and age presented for cases in which information was known. Unknown/missing data: gender=59, 7.27% (weighted); race/ethnicity=145, 18.41% (weighted); age=221, 29.9% (weighted).

Varying Traits of Those Who Commit Hate Crimes

The START database study, BIAS, found that the behaviors, experiences, and characteristics of those who commit hate crimes in the United States varied significantly.

• Some offenders were fully engaged in the world of bigotry and hate when they committed a bias-based offense, while others were acting on bias themes that pervade U.S. communities.
• Some committed crimes of opportunity, while others carefully premeditated their acts.
• Some were susceptible to negative peer influences or were struggling with mental health issues or substance abuse. [18]

The study also established that the characteristics of persons who commit hate crimes also varied considerably, depending on the nature of the prejudice involved. For example:

• Those who committed hate crimes based on their victims’ religious beliefs were often older, better educated, and had higher rates of military experience than those who committed hate crimes based on other motivations.
• Those motivated by religious bias displayed high rates of mental health concerns and were most likely to plan or commit hate crimes.
• Those motivated by bias based on sexual orientation, gender, or gender identity were often young, unmarried, and unemployed. They were also most likely to commit hate crimes with accomplices and while under the influence of drugs or alcohol.

Agency Reporting of Zero Hate Crimes

Data analyses from the NIJ-supported NHCIS showed that many U.S. law enforcement agencies, regardless of size, reported that they conducted no hate crime investigations within 2018. This is consistent with the FBI’s assessment from hate crime statistics provided by law enforcement agencies. According to the FBI, generally, around 85 percent of law enforcement agencies said that no hate crimes occurred in their jurisdiction. [19] That is good news if no hate crimes occurred, but it is problematic if hate crimes are occurring without being reported or investigated as such.

The hate crime investigations study authors noted that although over half of large agencies (100+ officers) reported no hate crimes investigations in 2018, several large agencies reported more than 50 hate crimes investigations that year. Based on an assessment of case summaries, the researchers concluded that better documentation increased the number of investigations. They also found that agency policies and procedures increased the number of hate crime investigations.

Implications and Recommendations: How Research Can Enhance Hate Crime Reporting, Investigations

NIJ-supported hate crime research identified several proposals to improve hate crime investigation and reporting. One promising area is for agencies to implement certain hate crime policies and practices. The NHCIS surveyed agencies on whether they had implemented five specific policies and practices. The study found four of the five were significantly related to an increased number of reported hate crime investigations, even when controlling for agency type and size: [20]

• Assigning a dedicated officer or unit to investigate hate crimes.
• Reviewing procedures for cases with possible hate or bias motivation.
• Developing written policy guidelines for investigating hate crimes.
• Conducting outreach to local groups on hate crimes.

Researchers found no significant differences in hate crime reporting rates between agencies that had provided officers with training on hate crime investigations, and those that had not; however, the study did not look at the nature and quality of the hate crime training the officers received. The NHCIS noted that officers with minimal training are often tasked with identifying hate crimes based on their state’s legal definition. The report also noted that bias-based crimes are often hard to classify, even with good training. More information is needed to determine the optimal type and focus of hate crime training. The Latino bias studies identified a need to both identify hate crimes and increase community education on hate crimes. The research team identified the following policy and process needs, among others, to improve identification and reporting:

• Enhance police training about risks associated with bias victimization in Latino communities.
• Increase education and awareness about bias victimization among Latino population groups.
• Build support for community-based agencies to facilitate the formal process of helping victims and reporting hate crimes to law enforcement. [21]

The study of LGBTQ+ Latinos in Miami identified the following recommendations to improve how law enforcement agencies report and identify anti-LGBTQ hate crimes:

• Establish a hate crime detection protocol for emergency dispatchers, patrol officers, police detectives, case screeners, and prosecutors.
• Develop a specialized workforce to identify, tackle, and prevent hate crimes; the workforce should be composed of prosecutors, detectives, patrol officers, victims’ liaisons, emergency dispatchers, researchers, and community experts.
• Create a dedicated support center for hate crime victims.
• Recruit police officers and prosecutors from the LGBTQ+ community.
• Develop formal policies to affirm and support transgender colleagues, victims, and witnesses.
• Encourage cooperation by pursuing victim engagement alternatives to subpoenas. Train criminal justice practitioners to improve victim engagement and hate crime detection, evidence gathering, and case screening.
• Engage in effective communication and awareness-building campaigns, such as initiatives to encourage victims to tell friends about the incident, as well as encouraging friends to persuade a victim to report the crime. [22]

It is critical for both communities and law enforcement to improve their methods of reporting and identifying hate crimes. Only then will they be able to prevent and respond to incidents and link victims to services they need. Doing so will also enable the field to develop a more comprehensive understanding of the scope and nature of the problem. The current gap between the number of hate crime victimizations and the number of hate crimes that law enforcement reported and investigated threatens the relationship between law enforcement and targeted communities. Chronic, widespread underreporting of hate crimes also greatly reduces the likelihood of justice for victims.

Findings from NIJ-supported research provide important insight into the causes of underreporting and under-identification of hate crimes. These studies also offer policy and practice recommendations to improve how law enforcement agencies report and identify hate crimes.

Sidebar: Hate Crimes: A Distinct Crime Category

The codification of hate crime laws began in the 1980s, as jurisdictions acted to redress the harm, beyond victim impact, that bias-based victimizations inflict on society. [23] In a 2022 solicitation for further hate crime research, NIJ noted,

Hate crimes are a distinct category of crime that have a broader effect than most other kinds of crime because the victims are not only the crime’s immediate target but also others in the targeted group. [24]

Hate crimes are traditional criminal offenses with an added element of bias motivation. They are not limited to crimes against persons; the crimes can target businesses, religious institutions, other organizations, and society at large. Additionally, hate crimes are not limited to one type of motivating prejudice. The FBI defines a hate crime as:

a criminal offense committed against a person or property which is motivated, in whole or in part, by the offender’s bias against race, religion, disability, ethnic or national origin group, or sexual orientation group. [25]

Hate crimes can be violent or nonviolent, but the acts must be recognized criminal offenses even if the bias element is set aside. Yet the wide net cast by hate crime laws has not resulted in high rates of hate crime prosecution or punishment. As noted in an NIJ-sponsored report on findings from the National Hate Crime Investigations Study, only 4 percent of hate crimes investigated by law enforcement resulted in someone being criminally charged. [26]

Return to the text.

Sidebar: Hate Crime vs. Bias Victimization

Hate crimes are a form of bias victimization. A criminal offense is a core element of every hate crime. However, not every bias victimization is a crime. In simple math terms, hate crimes are a subset of all bias victimizations.

The FBI’s Uniform Crime Reporting Program (UCR) defines a hate crime as a “committed criminal offense which is motivated, in whole or in part, by the offender’s bias(es)” against a

• sexual orientation
• gender identity27

State and local jurisdictions have their own hate crime statutes proscribing some or all of those or other types of bias. An act of bias victimization can be, but need not be, a criminal offense.

Return to text.

[note 1]  International Association of Chiefs of Police,  Responding to Hate Crimes: A Police Officer’s Guide to Investigation and Prevention  (Washington, DC: U.S. Department of Justice, Bureau of Justice Administration, 1999).

[note 2]  The five NIJ-supported hate crime study reports covered in this article are: Carlos A. Cuevas, et al.,  Understanding and Measuring Bias Victimization Against Latinos , October 2019, NCJ 253430; Carlos A. Cuevas, et al.,  Longitudinal Examination of Victimization Experiences of Latinos (LEVEL): Extending the Bias Victimization Study , August 2021, NCJ 30167; Michael A. Jensen, Elizabeth A. Yates, and Sheehan E. Kane,  A Pathway Approach to the Study of Bias Crime Offenders , February 2021, NCJ 300114; Besiki Luka Kutateladze,  Anti-LGBTQ Hate Crimes in Miami: Research Summary and Policy Recommendations , September 2021, NCJ 302239; Lisa M. Jones, Kimberly J. Mitchell, and Heather A. Turner,  U.S. Hate Crime Investigation Rates and Characteristics: Findings from the National Hate Crime Investigations Study (NHCIS) , December 2021, NCJ 304531.

[note 3]  Grace Kena and Alexandra Thompson,  Hate Crime Victimization, 2005–2019  (Washington, DC: U.S. Department of Justice (DOJ), Bureau of Justice Statistics (BJS), 2021).

[note 4]  Erica Smith,  Hate Crime Recorded by Law Enforcement, 2010–2019  (Washington, DC: U.S. DOJ, BJS, 2021.

[note 5]  Smith,  Hate Crime Recorded by Law Enforcement .

[note 6]  COVID-19 Hate Crimes Act, Pub. L. No. 117-17, 123 Stat. 2835 and 135 Stat. 265, 266, 267, 268, 269, 270, 271 and 272 (2021).

[note 7] Global Law Enforcement Support Section (GLESS) Crime and Law Enforcement Statistics Unit (CLESU),  Hate Crime Data Collection Guidelines and Training Manual  (Washington, DC: Federal Bureau of Investigation: Criminal Justice Information Division Uniform Crime Reporting Program, 2022).

[note 8] U.S. Department of Justice, “ Laws and Policies .”

[note 9] GLESS CLESU,  Hate Crime Data Collection Guidelines and Training Manual.

[note 10] Cuevas, et al.,  Understanding and Measuring Bias Victimization Against Latinos ; Cuevas, et al.,  Longitudinal Examination of Victimization Experiences of Latinos (LEVEL) .

[note 11] Kutateladze,  Anti-LGBTQ Hate Crimes in Miami .

[note 12] Jones, Mitchell, and Turner,  U.S. Hate Crime Investigation Rates and Characteristics .

[note 13] The practitioners were prosecutors who handled hate crime cases in the Miami-Dade State Attorney’s Office, detectives from the Miami-Dade Police Department, one victim liaison from the police department, and one from the prosecutor’s office; Kutateladze,  Anti-LGBTQ Hate Crimes in Miami .

[note 14] The three-community bias study survey sampled Latino community members generally, not limited to self-identified bias victims. Respondents reported on their own bias experiences. Overall, 52.9 percent of participants experienced some form of bias event in their lifetime.

[note 15] Jensen, Yates, and Kane,  A Pathway Approach to the Study of Bias Crime Offende rs .

[note 16] Jones, Mitchell, and Turner,  U.S. Hate Crime Investigation Rates and Characteristics .

[note 17] Jones, Mitchell, and Turner,  U.S. Hate Crime Investigation Rates and Characteristics .

[note 18] Jensen, Yates, and Kane,  A Pathway Approach to the Study of Bias Crime Offenders .

[note 19] Smith,  Hate Crime Recorded by Law Enforcement .

[note 20] Jones, Mitchell, and Turner,  U.S. Hate Crime Investigation Rates and Characteristics .

[note 21] Cuevas, et al.,  Understanding and Measuring Bias Victimization Against Latinos ; Cuevas, et al.,  Longitudinal Examination of Victimization Experiences of Latinos (LEVEL) .

[note 22] Kutateladze,  Anti-LGBTQ Hate Crimes in Miami .

23 Ryken Grattet and Valerie Jenness,  Making Hate a Crime: From Social Movements to Law Enforcement  (New York, NY: Russel Sage Foundation, 2001).

[note 24] NIJ FY22 Research and Evaluation on Hate Crime  (Washington, DC: U.S. Department of Justice, National Institute of Justice, 2022).

[note 25] FBI, “Defining a Hate Crime.”

[note 26] Lisa M. Jones, Kimberly J. Mitchell, and Heather A. Turner,  U.S. Hate Crime Investigation Rates and Characteristics: Findings from the National Hate Crime Investigations Study (NHCIS) , December 2021, NCJ 304531.

[note 27] FBI Hate Crime Statistics Reports, UCR, “Definition of a Hate Crime.”

About the author

Kaitlyn Sill, Ph.D. is a Social Science Research Analyst at the National Institute of Justice. Paul A. Haskins, JD, is a contract Writer-Editor supporting the National Institute of Justice.

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Gang ties don’t always bind

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Research from CU Boulder sociology professor shows that for many prisoners, gang affiliation tends to drop off once they are released back into their communities

Nearly everyone who enters prison in the United States eventually leaves. In fact, every year about 600,000 people are released from federal and state prisons, according to U.S. Department of Justice data.

Meanwhile, other data suggest that nearly 20% of the prison population belongs to a gang, which prompts the question: Do prisoners who are gang members maintain their gang affiliations after being released?

Perhaps surprisingly, there has been very little empirical research into that topic until now, according to David C Pyrooz , a University of Colorado Boulder professor of sociology whose research focus includes gangs, incarceration and reentry, and criminal justice policy and practice.

David C Pyrooz, a University of Colorado Boulder professor of sociology, researches gangs, incarceration and reentry, as well as criminal justice policy and practice.

“In terms of gangs, it’s a harder topic to study,” he explains. “For one, there’s a lot of sensitivity around it. Information about gangs is generally treated as intelligence in the sense that it’s privileged information that law enforcement and correctional agencies don’t necessarily want to share with the general public.”

Additionally, tracking an inmate after their release can be challenging because, as Pyrooz notes, “former prisoners often live chaotic lives. Once they’re out, they’re worried about food insecurity, about family reunification, about jobs, about housing and all these other things. So, it’s a tough population to study. Research obviously ranks low on their list of priorities.”

Convinced there was value in knowing whether people maintained their gang ties once released back into their communities, Pyrooz and his fellow researchers conducted a survey of 802 men in Texas prisons—representing a mix of active gang members, ex-gang members and non-gang members—who were interviewed once prior to their release and reinterviewed twice afterward. Their research findings were published in Justice Quarterly , the flagship publication of the Academy of Criminal Justice Sciences.

The study findings showed that gang activity declined for all three groups—including active gang members—as the pressure to maintain gang involvement subsides, contrary to what some speculation and anecdotes would indicate, Pyrooz says.

While some active gang members do maintain their involvement after being released, “it simply doesn’t occur in a manner that we expected—it’s not like it’s a straight line from the prison to the street. There’s something specific to the prison environment that gives rise to this sort of excess gang activity,” he says.

Pyrooz recently spoke with Colorado Arts and Sciences Magazine about this research. His responses have been lightly edited for style and condensed for space considerations.

Question: Why did you choose to focus on Texas prisons for your study?

Pyrooz: It’s the largest state prison system in the country. It’s large and it’s diverse in terms of race and ethnicity. The prison population is about a third Black, a third white and a third Hispanic. So, it gives a good racial ethnic representation. …

And it’s got a large gang population as well. There’s a large number of white, Black and Hispanic gangs with a lot of variation in how they’re organized and structured, which gives us an opportunity to examine whether patterns of behavior are consistent across gang types.

Question: Do you have thoughts about why prisoners were open to speaking with you, particularly when sharing details about gang activity?

Pyrooz: There was the longstanding belief going into the study that prisoners would not be open to speaking with researchers, much less telling the truth. In fact, it’s one of the major reasons that people have offered us to as to why we don’t know a lot about prison gangs, even setting aside the reentry issue.

Every year, nearly 600,000 men and women are released from state and federal prisons. Up until now, little empirical research has been done on whether prisoners who are active gang members maintain their gang affiliations after they are released. (Photo: Tom Pennington/Fort Worth Star-Telegram)

So, we treated the prison interview like an exit interview, in the sense that we tried to target a period of time where we thought ties to gangs … could be waning, such that gangs couldn’t exercise as much influence over a prisoner … as much (because prisoners are removed from the general population prior to their release). Interviewing prisoners about 48 hours prior to their release is something that we targeted. That was strategic. …

As to why they spoke with us, we’re a neutral party. It’s not like speaking with a correctional officer, where incriminating information might be used against them. It’s not like a girlfriend who is making decisions about whether she wants to stay with you, an estranged child or anything of that sort with incredible emotional baggage.

There’s no past history between us and the person. It’s like a blank slate. So, it just gives them the opportunity to be able to reflect on things that they felt comfortable sharing with us.

There were times during interviews where prisoners would say, ‘I haven't told anybody about this in the entire time I’ve been incarcerated. It felt great to just get it off of my chest to talk to someone.’

Not everybody was like that. There were some interviews that were difficult.

Question: In your paper you say, ‘Not all gang members are created equal.’ What do you mean by that? Does it relate to what you refer to in your paper as ‘gang embeddedness’?

Pyrooz: A lot of people have this black or white view of, you’re a gang member or not. But that doesn’t really tell the full picture; it doesn’t really capture the different dimensions of involvement…

Gang embeddedness captures immersion in gangs. In the same way that you could differentiate people who are really religious—they’re going to church more than one time a week, they’re praying at home and they may be a church volunteer for church activities. In contrast, you have people who are sort of the Christmas and Easter crowd, or agnostic or completely atheists. These two groups aren’t the same, and there are many shades of gray between them.

There’s a belief that, just like you give religion importance, you give the gang importance, and just like people fall away from the church, people fall away from gangs.

Question: As part of your research, your team interviewed prisoners once while in prison and two separate times after their release. Why was that format important?

Pyrooz: We really wanted to get a first interview while they were inside of prison. We wanted to understand, while they were in that environment, what they were thinking.

But we also wanted to understand, for continuity and change, what spills over from the inside to the outside, versus what stays inside. So, what’s sort of this remnant of their past life, of being an incarcerated person, versus returning back to the community. …

That’s what we really were trying to understand, and then to differentiate between, the short-term changes, like within a couple of weeks of getting out, versus how did you start to settle in your life 10 months later? And what percent of them went back to prison, got arrested or were killed after their release?

Question: How does this latest paper on gang involvement in and out of prison fit in with your overall areas of research?

Pyrooz: I’ve been studying gangs for upwards of 15 years, focused on the contours of gang involvement: when people join, how long they stay, when they leave and what the long-term consequences are.

There was this longstanding perception that once you join a gang, you can never get out of these groups—which is a myth. Since I’ve been doing my research, we’ve found that not only does it happen, but that’s the norm—as opposed to the exception—that people do leave.

I took my first job out of grad school at Sam Houston State University in Huntsville, Texas, which is known colloquially as Prison City, USA, because there’s so many prisons within not just the city limits, but within Walker County, Texas, including one that was just about two blocks from my office.

There was this longstanding perception that once you join a gang, you can never get out of these groups—which is a myth. Since I’ve been doing my research, we’ve found that not only does it happen, but that’s the norm—as opposed to the exception—that people do leave."

And not only is that where the state conducts all of the executions, but it’s also one of the major (prisoner) release centers in the state of Texas. So, continuing with the theme on continuity and change, prisons represent this next frontier to understand whether these gang associations spill out of the prisons to the street and also, when these transitions occur, are (ex-prisoners) able to leave these associations behind when they return to the community?

It fits within a broader agenda of focusing on gangs, but also on this broader criminological interest in continuity and change in the life course.

Question: What can corrections officials and law enforcement gain from your latest research, both as it relates to felons while still in prison and once they are released?

Pyrooz: To me, what it suggests right off the bat is that the prison systems need to do something about gangs in their institutions. And by do something, I’m not just talking about housing them differently, akin to rearranging the deck chairs on the Titanic. … I’m talking about actual prevention and actual intervention. In other words, blocking the onramps and widening the offramps to gang involvement. Housing might be a part of it, but it could also be work programs; it could be therapeutic interventions; it could be religion; it could be a whole host of different things that are done to keep people occupied, to change mindsets and to alter risks and threats to their livelihoods.

Given that prisons are operating as this vector of gang activity, (prison administrators) need to be doing something more than just business as usual, because that certainly hasn’t put a dent in the activity or the violence behind bars. …

You want to keep (prisoners) occupied, versus stewing and getting into trouble. It’s like the saying, ‘Idle hands are the devil’s workshop.’ And behind bars, there’s a lot of idle hands. …

Once people are released, one factor that can determine gang involvement is if they go back to a gang-active neighborhood. If they do, they are more likely to be gang active. So, there’s a lot of practical relevance here that matters for parole officers and anyone involved with the supervision of people after their release.

Question: Is there anything else from your research that you would like to share?

Pyrooz: I think that for a lot of people, when it comes to prisoners, they’re sort of out of sight, out of mind. They may not have a lot of concern for people who are behind bars, in part because they believe that they’ve earned that prison sentence.

But when you really start thinking about the fact that (ex-prisoners) do return home—and we don’t want them to go back to prison—it really starts reshaping the public’s calculus with regard to the sort of humanity afforded to people in prisons.

And once you realize that they can be your neighbors, that they could go to your church and work similar jobs, for most people, it starts to give you a different meaning behind imprisonment. What are we willing and what aren’t we willing to do? And just how much we care about what happens to these people in prisons?

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Research trends in cybercrime victimization during 2010–2020: a bibliometric analysis

Huong thi ngoc ho.

1 School of Journalism and Communication, Huazhong University of Science and Technology, Wuhan, Hubei China

Hai Thanh Luong

2 School of Global, Urban and Social Studies, RMIT University, Melbourne, Australia

Associated Data

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Research on cybercrime victimization is relatively diversified; however, no bibliometric study has been found to introduce the panorama of this subject. The current study aims to address this research gap by performing a bibliometric analysis of 387 Social Science Citation Index articles relevant to cybercrime victimization from Web of Science database during the period of 2010–2020. The purpose of the article is to examine the research trend and distribution of publications by five main fields, including time, productive authors, prominent sources, active institutions, and leading countries/regions. Furthermore, this study aims to determine the global collaborations and current gaps in research of cybercrime victimization. Findings indicated the decidedly upward trend of publications in the given period. The USA and its authors and institutions were likely to connect widely and took a crucial position in research of cybercrime victimization. Cyberbullying was identified as the most concerned issue over the years and cyber interpersonal crimes had the large number of research comparing to cyber-dependent crimes. Future research is suggested to concern more about sample of the elder and collect data in different countries which are not only European countries or the USA. Cross-nation research in less popular continents in research map was recommended to be conducted more. This paper contributed an overview of scholarly status of cybercrime victimization through statistical evidence and visual findings; assisted researchers to optimize their own research direction; and supported authors and institutions to build strategies for research collaboration.

Introduction

To date, the debate of cybercrime definition has been controversial which is considered as one of the five areas of cyber criminology (Ngo and Jaishankar 2017 ; Drew 2020 ). 1 Several terms are used to illustrate ‘cybercrime’, such as ‘high-tech crime’ (Insa 2007 ), ‘computer crime’ (Choi 2008 ; Skinner and Fream 1997 ), ‘digital crime’ (Gogolin 2010 ), or ‘virtual crime’ (Brenner 2001 ). ‘Cybercrime’, however, has been the most popular in the public parlance (Wall 2004 ). A propensity considers crime directly against computer as cybercrime, while other tendency asserts that any crime committed via internet or related to a computer is cybercrime (Marsh and Melville 2008 ; Wall 2004 ). Hence, there is a distinction between ‘true cybercrime’ or ‘high-tech’ cybercrime and ‘low-tech’ cybercrime (Wagen and Pieters 2020 ). Council of Europe defines ‘any criminal offense committed against or with the help of a computer network’ as cybercrime (Abdullah and Jahan 2020 , p. 90). Despite different approaches, cybercrime generally includes not only new types of crimes which have just occurred after the invention of computer and internet (Holt and Bossler 2014 ; Drew 2020 ) but also traditional types of crimes which took the advantages of information communication technology (ICT) as vehicle for illegal behaviors (Luong 2021 ; Nguyen and Luong 2020 ; Luong et al. 2019 ). Two main cybercrime categories identified, respectively, are cyber-dependent crime (hacking, malware, denial of service attacks) and cyber-enable crime (phishing, identity theft, cyber romance scam, online shopping fraud). Nevertheless, there are several different classifications of cybercrime such as cybercrime against certain individuals, groups of individuals, computer networks, computer users, critical infrastructures, virtual entities (Wagen and Pieters 2020 ); cyber-trespass, cyber-deceptions, cyber-pornography, and cyber-violence (Wall 2001 ).

Due to the common prevalence of cybercrime, the increasing threats of cybercrime victimization are obviously serious. Cybercrime victimization has become a crucial research subfield in recent years (Wagen and Pieters 2020 ). It is difficult to differ “forms of online victimization” and “acts that actually constitute a crime”, then it is usual for researchers to focus less on perspective of criminal law and consider any negative experiences online as cybercrime (Näsi et al. 2015 , p. 2). It was likely to lead to practical gaps between theory and practice in terms of investigating the nexus of offender and victims on cyberspace. In the light of literature review, numerous specific aspects of cybercrime victimization were investigated by questionnaire surveys or interview survey such as the prevalence of cybercrime victimization (Näsi et al. 2015 ; Whitty and Buchanan 2012 ); causes and predictors of cybercrime victimization (Abdullah and Jahan 2020 ; Algarni et al. 2017 ; Ilievski 2016 ; Jahankhani 2013 ; Kirwan et al. 2018 ; Näsi et al. 2015 ; Reyns et al. 2019 ; Saad et al. 2018 ); and the relationship between social networking sites (SNS) and cybercrime victimization (Das and Sahoo 2011 ; Algarni et al. 2017 ; Benson et al. 2015 ; Seng et al. 2018 ). To some extent, therefore, the current study examines cybercrime victimization in the large scale, referring to any negative experiences on cyberspace or computer systems. Nevertheless, no bibliometric analysis was found to show the research trend and general landscape of this domain.

Bibliometric is a kind of statistical analysis which uses information in a database to provide the depth insight into the development of a specified area (Leung et al. 2017 ). The present study aims to address this research gap by providing a bibliometric review of the relevant SSCI articles in WoS database during the period of 2010–2020. The pattern of publications, the productivity of main elements (authors, journals, institutions, and countries/regions), statistic of citations, classification of key terms, research gaps, and other collaborations will be presented and discussed in section four and five after reviewing literatures and presenting our methods conducted. This article contributes an overview of research achievements pertaining to cybercrime victimization in the given period through statistical evidence and visual findings; assists researchers to perceive clearly about the key positions in research maps of this field, and obtain more suggestions to develop their own research direction.

Literature review

Cybercrime victimization.

Cybercrime victimization may exist in two levels including institutional and individual level (Näsi et al. 2015 ). For the former, victim is governments, institutions, or corporations, whereas for the latter, victim is a specific individual (Näsi et al. 2015 ). A wide range of previous studies concerned about individual level of victim and applied Lifestyle Exposure Theory (LET), Routine Activity Theory (RAT) and General Theory of Crime to explain cybercrime victimization (Choi 2008 ; Holt and Bossler 2009 ; Ngo and Paternoster 2011 ). Basing on these theories, situational and individual factors were supposed to play an important role in understanding cybercrime victimization (Choi 2008 ; Van Wilsem 2013 ). However, there was another argument that situational and individual factors did not predict cybercrime victimization (Ngo and Paternoster 2011 ; Wagen and Pieters 2020 ). Overall, most of those studies just focused only one distinctive kind of cybercrime such as computer viruses, malware infection, phishing, cyberbullying, online harassment, online defamation, identity theft, cyberstalking, online sexual solicitation, cyber romance scams or online consumer fraud. Referring to results of the prior research, some supported for the applicability of mentioned theories but other did not share the same viewpoint (Leukfeldt and Yar 2016 ). It was hard to evaluate the effect of LET or RAT for explanation of cybercrime victimization because the nature of examined cybercrime were different (Leukfeldt and Holt 2020 ; Leukfeldt and Yar 2016 ).

Previous research determined that cybercrime victimization was more common in younger group compared to older group because the young is the most active online user (Näsi et al. 2015 ; Oksanen and Keipi 2013 ) and males tended to become victims of cybercrime more than females in general (Näsi et al. 2015 ). However, findings might be different in research which concerned specific types of cybercrime. Women were more likely to be victims of the online romance scam (Whitty and Buchanan 2012 ) and sexual harassment (Näsi et al. 2015 ), while men recorded higher rate of victimization of cyber-violence and defamation. Other demographic factors were also examined such as living areas (Näsi et al. 2015 ), education (Oksanen and Keipi 2013 ; Saad et al. 2018 ) and economic status (Oksanen and Keipi 2013 ; Saad et al. 2018 ). Furthermore, several prior studies focus on the association of psychological factors and cybercrime victimization, including awareness and perception (Ariola et al. 2018 ; Saridakis et al. 2016 ), personality (Kirwan et al. 2018 ; Orchard et al. 2014 ; Parrish et al. 2009 ), self-control (Ilievski 2016 ; Ngo and Paternoster 2011 ; Reyns et al. 2019 ), fear of cybercrime (Lee et al. 2019 ), online behaviors (Al-Nemrat and Benzaïd 2015 ; Saridakis et al. 2016 ). Psychological factors were assumed to have effects on cybercrime victimization at distinctive levels.

Another perspective which was much concerned by researchers was the relationship between cybercrime victimization and SNS. SNS has been a fertile land for cybercriminals due to the plenty of personal information shared, lack of guard, the availability of communication channels (Seng et al. 2018 ), and the networked nature of social media (Vishwanath 2015 ). When users disclosed their personal information, they turned themselves into prey for predators in cyberspace. Seng et al. ( 2018 ) did research to understand impact factors on user’s decision to react and click on suspicious posts or links on Facebook. The findings indicated that participants’ interactions with shared contents on SNS were affected by their relationship with author of those contents; they often ignored the location of shared posts; several warning signals of suspicious posts were not concerned. Additionally, Vishwanath ( 2015 ) indicated factors that led users to fall victims on the SNS; Algarni et al. ( 2017 ) investigated users’ susceptibility to social engineering victimization on Facebook; and Kirwan et al. ( 2018 ) determined risk factors resulting in falling victims of SNS scam.

Bibliometric of cybercrime victimization

“Bibliometric” is a term which was coined by Pritchard in 1969 and a useful method which structures, quantifies bibliometric information to indicate the factors constituting the scientific research within a specific field (Serafin et al. 2019 ). Bibliometric method relies on some basic types of analysis, namely co-authorship, co-occurrence, citation, co-citation, and bibliographic coupling. This method was employed to various research domains such as criminology (Alalehto and Persson 2013 ), criminal law (Jamshed et al. 2020 ), marketing communication (Kim et al. 2019 ), social media (Chen et al. 2019 ; Gan and Wang 2014 ; Leung et al. 2017 ; Li et al. 2017 ; You et al. 2014 ; Zyoud et al. 2018 ), communication (Feeley 2008 ), advertising (Pasadeos 1985 ), education (Martí-Parreño et al. 2016 ).

Also, there are more and more scholars preferring to use bibliometric analysis on cyberspace-related subject such as: cyber behaviors (Serafin et al. 2019 ), cybersecurity (Cojocaru and Cojocaru 2019 ), cyber parental control (Altarturi et al. 2020 ). Serafin et al. ( 2019 ) accessed the Scopus database to perform a bibliometric analysis of cyber behavior. All documents were published by four journals: Cyberpsychology, Behavior and Social Networking (ISSN: 21522723), Cyberpsychology and Behavior (ISSN: 10949313) , Computers in Human Behavior (ISSN: 07475632) and Human–Computer Interaction (ISSN: 07370024), in duration of 2000–2018. Findings indicated the use of Facebook and other social media was the most common in research during this period, while psychological matters were less concerned (Serafin et al. 2019 ). Cojocaru and Cojocaru ( 2019 ) examined the research status of cybersecurity in the Republic of Moldavo, then made a comparison with the Eastern Europe countries’ status. This study employed bibliometric analysis of publications from three data sources: National Bibliometric Instrument (database from Republic of Moldavo), Scopus Elsevier and WoS. The Republic of Moldavo had the moderate number of scientific publications on cybersecurity; Russian Federation, Poland, Romania, Czech Republic, and Ukraine were the leading countries in Eastern Europe area (Cojocaru and Cojocaru 2019 ). Altarturi et al. ( 2020 ) was interested in bibliometric analysis of cyber parental control, basing on publications between 2000 and 2019 in Scopus and WoS. This research identified some most used keywords including ‘cyberbullying’, ‘bullying’, ‘adolescents’ and ‘adolescence’, showing their crucial position in the domain of cyber parental control (Altarturi et al. 2020 ). ‘Cyber victimization’ and ‘victimization’ were also mentioned as the common keywords by Altarturi et al. ( 2020 ). Prior research much focus on how to protect children from cyberbullying. Besides, four online threats for children were determined: content, contact, conduct and commercial threats (Altarturi et al. 2020 ).

Generally, it has been recorded several published bibliometric analyses of cyber-related issues but remained a lack of bibliometric research targeting cybercrime victimization. Thus, the present study attempts to fill this gap, reviewing the achievements of existed publications as well as updating the research trend in this field.

In detail, our current study aims to address four research questions (RQs):

What is overall distribution of publication based on year, institutions and countries, sources, and authors in cybercrime victimization?

Which are the topmost cited publications in terms of cybercrime victimization?

Who are the top co-authorships among authors, institutions, and countries in research cybercrime victimization?

What are top keywords, co-occurrences and research gaps in the field of cybercrime victimization?

Data collection procedure

Currently, among specific approaches in cybercrime’s fileds, WoS is “one of the largest and comprehensive bibliographic data covering multidisciplinary areas” (Zyoud et al. 2018 , p. 2). This paper retrieved data from the SSCI by searching publications of cybercrime victimization on WoS database to examine the growth of publication; top keywords; popular topics; research gaps; and top influential authors, institutions, countries, and journals in the academic community.

This paper employed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data collection procedure. For timeline, we preferred to search between 2010 and 2020 on the WoS system with two main reasons. First, when the official update of the 2009 PRISMA Statement had ready upgraded with the specific guidelines and stable techniques, we consider beginning since 2010 that is timely to test. Secondly, although there are several publications from the early of 2021 to collect by the WoS, its updated articles will be continued until the end of the year. Therefore, we only searched until the end of 2020 to ensure the full updates.

To identify publications on cybercrime victimization, the study accessed WoS and used two keywords for searching: ‘cybercrime victimization’ or ‘cyber victimization’ after testing and looking for some terminology-related topics. Accordingly, the paper applied a combination of many other searching terms besides two selected words such as “online victimization”, “victim of cybercrime”, “phishing victimization”, “online romance victimization”, “cyberstalking victim”, “interpersonal cybercrime victimization”, or “sexting victimization”, the results, however, were not really appropriate. A lot of papers did not contain search keywords in their titles, abstracts, keywords and were not relavant to study topic. After searching with many different terms and comparing the results, the current study selected the two search terms for the most appropriate articles. The query result consisted of 962 documents. Basing on the result from preliminary searching, retrieved publications were refined automatically on WoS by criteria of timespan, document types, language, research areas, and WoS Index as presented in Table ​ Table1. 1 . Accordingly, the criteria for automatic filter process were basic information of an articles and classified clearly in WoS system so the results reached high accuracy. The refined results are 473 articles.

Criteria for automatic filter

After automatic filters, file of data was converted to Microsoft Excel 2016 for screening. The present study examined titles and abstracts of 473 articles to assess the eligibility of each publication according to the relevance with given topic. There are 387 articles are eligible,while 86 irrelevant publications were excluded.

Data analysis

Prior to data analysis, the raw data were cleaned in Microsoft Excel 2016. Different forms of the same author’s name were corrected for consistency, for example “Zhou, Zong-Kui” and “Zhou Zongkui”, “Van Cleemput, Katrien” and “Van Cleemput, K.”, “Williams, Matthew L.” and “Williams, Matthew”. Similarly, different keywords (single/plural or synonyms) used for the same concept were identified and standardized such as “victimization” and “victimisation”; “adolescent” and “adolescents”; “cyber bullying”, “cyber-bullying” and “cyberbullying”; “routine activity theory” and “routine activities theory”.

The data were processed by Microsoft Excel 2016 and VOS Viewer version 1.6.16; then it was analyzed according to three main aspects. First, descriptive statistic provided evidence for yearly distribution and growth trend of publications, frequency counts of citations, the influential authors, the predominant journals, the top institutions and countries/territories, most-cited publications. Second, co-authorship and co-occurrence analysis were constructed and visualized by VOS Viewer version 1.6.16 to explore the network collaborations. Finally, the current study also investigated research topics through content analysis of keywords. The authors’ keywords were classified into 15 themes, including: #1 cybercrime; #2 sample and demographic factors; #3 location; #4 theory; #5 methodology; #6 technology, platforms and related others; #7 psychology and mental health; #8 physical health; #9 family; #10 school; #11 society; #12 crimes and deviant behaviors; #13 victim; #14 prevention and intervention; and #15 others. Besides, the study also added other keywords from titles and abstracts basing on these themes, then indicated aspects examined in previous research.

In this section, all findings corresponding with four research questions identified at the ouset of this study would be illustrated (Fig.  1 ).

PRISMA diagram depicts data collection from WoS database

Distribution of publication

Distribution by year, institutions and countries.

Basing on retrieved data, it was witnessed an increasing trend of articles relevant to cybercrime victimization in SSCI list during the time of 2010–2020 but it had slight fluctuations in each year as shown in Fig.  2 . The total number of articles over this time was 387 items, which were broken into two sub-periods: 2010–2014 and 2015–2020. It is evident that the latter period demonstrated the superiority of the rate of articles (79.33%) compared to the previous period (20.67%). The yearly quantity of publications in this research subject was fewer than forty before 2015. Research of cybercrime victimization reached a noticeable development in 2016 with over fifty publications, remained the large number of publications in the following years and peaked at 60 items in 2018.

Annual distribution of publications

Distribution by institutions and countries

Table ​ Table2 2 shows the top contributing institutions according to the quantity of publications related to cybercrime victimization. Of the top institutions, four universities were from the USA, two ones were from Spain, two institutions were from Australia and the rest ones were from Czech Republic, Belgium, Greece, and Austria. Specifically, Masaryk University (17 documents) became the most productive publishing institution, closely followed by Michigan State University (16 documents). The third and fourth places were University of Antwerp (13 documents) and Weber State University (10 documents). Accordingly, the institutions from The USA and Europe occupied the vast majority.

Top contributing institutions based on total publications

TP total publications, TC total citations for the publications reviewed, AC average citations per document

In Table ​ Table2, 2 , University of Seville (total citations: 495, average citations: 70.71) ranked first and University of Cordoba (total citations: 484, average citations: 60.50) stayed at the second place in both total citations and average citations.

Referring to distribution of publications by countries, there were 45 countries in database contributing to the literature of cybercrime victimization. The USA recorded the highest quantity of papers, creating an overwhelming difference from other countries (159 documents) as illustrated in Fig.  3 . Of the top productive countries, eight European countries which achieved total of 173 publications were England (39 documents), Spain (34 documents), Germany (22 documents), Netherlands (18 documents), Italy (17 documents) and Czech Republic (17 documents), Belgium (14 documents), Greece (12 documents). Australia ranked the fourth point (32 documents), followed by Canada (30 documents). One Asian country which came out seventh place, at the same position with Netherlands was China (18 documents).

Top productive countries based on the number of publications

Distribution by sources

Table ​ Table3 3 enumerates the top leading journals in the number of publications relevant to cybercrime victimization. The total publications of the first ranking journal— Computers in Human Behavior were 56, over twice as higher as the second raking journal— Cyberpsychology, Behavior and Social Networking (24 articles). Most of these journals have had long publishing history, starting their publications before 2000. Only three journals launched after 2000, consisting of Journal of School Violence (2002), Cyberpsychology: Journal of Psychosocial Research on Cyberspace (2007) and Frontiers in Psychology (2010). Besides, it is remarked that one third of the top journals focuses on youth related issues: Journal of Youth and Adolescence , Journal of Adolescence, School Psychology International and Journal of School Violence .

Top leading journals based on the quantity of publications

SPY Started Publication Year

In Table ​ Table3, 3 , relating to total citations, Computers in Human Behavior remained the first position with 2055 citations. Journal of Youth and Adolescence had total 1285 citations, ranked second and followed by Aggressive Behavior with 661 citations. In terms of average citations per documents, an article of Journal of Youth and Adolescence was cited 67.63 times in average, much higher than average citations of one in Computers in Human Behavior (36.70 times). The other journals which achieved the high number of average citations per document were School Psychology International (59.00 times), Journal of Adolescence (44.83 times) and Aggressive Behavior (44.07 times).

Distribution by authors

Table ​ Table4 4 displays ten productive authors based on article count; total citations of each author and their average citations per document are also included. Michelle F. Wright from Pennsylvania State University ranked first with twenty publications, twice as higher as the second positions, Thomas J. Holt (10 articles) from Michigan State University and Bradford W. Reyns (10 articles) from Weber State University. Rosario Ortega-Ruiz from University of Cordoba stayed at the third place in terms of total publications but the first place in aspect of total citations (483 citations) and the average citations (60.38 times).

Top productive authors based on article count

Of the most productive authors based on total publications, there were three authors from universities in the USA; one from the university in Canada (Brett Holfeld); the others were from institutions in Euro, including Spain (Rosario Ortega-Ruiz), Greece (Constantinos M. Kokkinos) and Belgium (Heidi Vandebosch), Netherlands (Rutger Leukfeldt) and Austria (Takuya Yanagida and Christiane Spiel).

Most-cited publications

The most-cited literature items are displayed in Table ​ Table5. 5 . The article which recorded the highest number of citations was ‘Psychological, Physical, and Academic Correlates of Cyberbullying and Traditional Bullying’ (442 citations) by Robin M. Kowalski et al. published in Journal of Adolescent Health , 2013. Seven of ten most-cited articles were about cyberbullying; focused on youth population; made comparisons between cyberbullying and traditional bullying; analyzed the impact of several factors such as psychological, physical, academic factors or use of Internet; discussed on preventing strategies. The other publications studied victimization of cyberstalking and cyber dating abuse. All most-cited articles were from 2015 and earlier.

The most-cited publications in subject of cybercrime victimization during 2010–2020

Of the top productive authors, only Bradford W. Reyns had an article appeared in the group of most-cited publications. His article ‘Being Pursued Online: Applying Cyberlifestyle-Routine Activities Theory to Cyberstalking Victimization’ (2011) was cited 172 times.

Co-authorship analysis

“Scientific collaboration is a complex social phenomenon in research” (Glänzel and Schubert 2006 , p. 257) and becomes the increasing trend in individual, institutional and national levels. In bibliometric analysis, it is common to assess the productivity and international collaboration of research; identify key leading researchers, institutions, or countries (E Fonseca et al. 2016 ) as well as potential collaborators in a specific scientific area (Romero and Portillo-Salido 2019 ) by co-authorship analysis which constructs networks of authors and countries (Eck and Waltman 2020 ).

This section analyses international collaboration relevant to research of cybercrime victimization among authors, institutions, and countries during 2010–2020 through visualization of VOS Viewer software.

Collaboration between authors

Referring to the threshold of choose in this analysis, minimum number of documents of author is three and there were 80 authors for final results. Figure  4 illustrates the relationships between 80 scientists who study in subject of cybercrime victimization during 2010–2020. It shows several big groups of researchers (Wright’s group, Vandebosch’s group, or Holt’s group), while numerous authors had limited or no connections to others (Sheri Bauman, Michelle K. Demaray or Jennifer D. Shapka).

Collaboration among authors via network visualization (threshold three articles for an author, displayed 80 authors)

Figure  5 displayed a significant network containing 23 authors who were active in collaboration in detail. The displayed items in Fig.  5 are divided into five clusters coded with distinctive colors, including red, green, blue, yellow, and purple. Each author item was represented by their label and a circle; the size of label and circle are depended on the weight of the item, measured by the total publications (Eck and Waltman 2020 ). The thickness of lines depends on the strength of collaboration (Eck and Waltman 2020 ).

Collaboration among authors via network visualization (threshold three articles for an author, displayed 23 authors)

The most significant cluster was red one which is comprised of six researchers: Michelle F. Wright, Sebastian Wachs, Yan Li, Anke Gorzig, Manuel Gamez-Guadix and Esther Calvete. The remarked author for the red cluster was Michelle F. Wright whose value of total link strength is 24. She had the strongest links with Sebastian Wachs; closely link with Yan Li, Anke Gorzig, Manuel Gamez-Guadix and collaborated with authors of yellow cluster, including Shanmukh V. Kamble, Li Lei, Hana Machackova, Shruti Soudi as well as Takuya Yanagida of blue cluster. Michelle F. Wright who obtained the largest number of published articles based on criteria of this study made various connections with other scholars who were from many different institutions in the world. This is also an effective way to achieve more publications.

Takuya Yanagida was the biggest node for the blue cluster including Petra Gradinger, Daniel Graf, Christiane Spiel, Dagmar Strohmeier. Total link strength for Takuya Yanagida was 28; twelve connections. It is observed that Takuya Yanagida’ s research collaboration is definitely active. Besides, other research groups showed limited collaborations comparing with the red and blue ones.

Collaboration between institutions

The connections among 156 institutions which published at least two documents per one are shown in Fig.  6 . Interestingly, there is obvious connections among several distinctive clusters which were coded in color of light steel blue, orange, purple, steel blue, green, red, yellow, light red, dark turquoise, light blue, brown and light green. These clusters created a big chain of connected institutions and were in the center of the figure, while other smaller clusters or unlinked bubbles (gray color) were distributed in two sides. The biggest chain consisted of most of productive institutions such as Masaryk University, Michigan State University, University of Antwerp, Weber State University, University of Cordoba, Edith Cowan University, University of Cincinnati, University of Victoria, University of Vienna, and University of Seville.

Collaboration among institutions via network visualization (threshold two articles for an institution, 156 institutions were displayed)

Light steel blue and orange clusters presented connections among organizations from Australia. Light green included institutions from Netherland, while turquoise and light blue consisted of institutions from the USA. Yellow cluster was remarked by the various collaborations among institutions from China and Hong Kong Special Administrative Region (Renmin University of China and South China Normal University, University of Hong Kong, the Hong Kong Polytechnic University and the Chinese University of Hong Kong), the USA (University of Virginia), Cyprus (Eastern Mediterranean University), Japan (Shizuoka University), India (Karnataka University) and Austria (University Applied Sciences Upper Austria). Central China Normal University is another Chinese institution which appeared in Fig.  5 , linking with Ministry of Education of the People’s Republic of China, Suny Stony Brook and University of Memphis from the USA.

Masaryk University and Michigan State University demonstrated their productivity in both the quantity of publications and the collaboration network. They were active in research collaboration, reaching twelve and eleven links, respectively, with different institutions, but focused much on networking with institutions in the USA and Europe.

Collaboration between countries

The collaboration among 45 countries which published at least one SSCI documents of cybercrime victimization during the given period was examined in VOS Viewer but just 42 items were displayed via overlay visualization. Figure  7 depicts the international collaborations among significant countries. The USA is the biggest bubble due to its biggest number of documents and shows connections with 26 countries/regions in Euro, Asia, Australia, Middle East. Excepting European countries, England collaborate with the USA, Australia, South Korea, Japan, Thailand, Singapore, Sri Lanka, and Colombia. Spain and Germany almost focus on research network within Euro. China has the strongest tie with the USA, link with Australia, Germany, Czech Republic, Austria, Cyprus and Turkey, Japan, Indian, Vietnam.

Collaboration among countries via overlay visualization

Color bar in Fig.  7 is determined by the average publication year of each country and the color of circles based on it. It is unsurprised that the USA, Australia, England, or Spain shows much research experience in this field and maintain the large number of publications steadily. Interestingly, although the average publication year of South Korea or Cyprus was earlier than other countries (purple color), their quantities of documents were moderate. The new nodes (yellow circles) in the map included Vietnam, Norway, Pakistan, Ireland, Scotland, Switzerland.

Keywords and co-occurrence

The present paper examined the related themes and contents in research of cybercrime victimization during 2010–2020 through collecting author keywords, adding several keywords from tiles and abstracts. Besides, this study also conducted co-occurrence analysis of author keywords to show the relationships among these keywords.

The keywords were collected and categorized into 15 themes in Table ​ Table6, 6 , including cybercrime; sample and demographic factors; location; theory; methodology; technology, platform, and related others; psychology and mental health; physical health; family; school; society; crimes and other deviant behaviors; victim; prevention and intervention; and others.

Statistic of keywords in themes

These keywords were most of author keyword, adding a few selected keywords from the titles and abstracts by the author of this current study

In the theme of cybercrime, there were numerous types of cybercrimes such as cyberbullying, cyber aggression, cyberstalking, cyber harassment, sextortion and other cyber dating crimes, cyber fraud, identity theft, phishing, hacking, malware, or ransomware. Generally, the frequency of interpersonal cybercrimes or cyber-enable crimes was much higher than cyber-dependent crimes. Cyberbullying was the most common cybercrime in research.

Relating to sample and demographic factors, there were sample of children, adolescent, adults, and the elder who were divided into more detail levels in each research; however, adolescent was the most significant sample. Besides, demographic factor of gender received a remarked concern from scholars.

It is usual that most of the research were carried out in one country, in popular it was the USA, Spain, Germany, England, Australia, Canada or Netherland but sometimes the new ones were published such as Chile, Vietnam, Thailand or Singapore. It was witnessed that some studies showed data collected from a group of countries such as two countries (Canada and the United State), three countries (Israel, Litva, Luxembourg), four countries (the USA, the UK, Germany, and Finland), or six Europe countries (Spain, Germany, Italy, Poland, the United Kingdom and Greece).

A wide range of theories were applied in this research focusing on criminological and psychological theories such as Routine Activities Theory, Lifestyle—Routine Activities Theory, General Strain Theory, the Theory of Reasoned Action or Self-control Theory.

Table ​ Table6 6 indicated a lot of different research methods covering various perspective of cybercrime victimization: systematic review, questionnaire survey, interview, experiment, mix method, longitudinal study, or cross-national research; many kinds of analysis such as meta-analysis, social network analysis, latent class analysis, confirmatory factor analysis; and a wide range of measurement scales which were appropriate for each variable.

Topic of cybercrime victimization had connections with some main aspects of technology (information and communication technologies, internet, social media or technology related activities), psychology (self-esteem, fear, attitude, personality, psychological problems, empathy, perceptions or emotion), physical health, family (parents), school (peers, school climate), society (norms, culture, social bonds), victim, other crimes (violence, substance use), prevention and intervention.

Co-occurrence analysis was performed with keywords suggested by authors and the minimum number of occurrences per word is seven. The result showed 36 frequent keywords which clustered into five clusters as illustrated in Fig.  8 .

Co-occurrence between author keywords via network visualization (the minimum number of occurrences per word is seven, 36 keywords were displayed)

Figure  8 illustrates some main issues which were concerned in subject of cybercrime victimization, as well as the relationship among them. Fifteen most frequent keywords were presented by big bubbles, including: ‘cyberbullying’ (174 times), ‘cyber victimization’ (90 times), ‘adolescent’ (79 times), ‘bullying’ (66 times), ‘victimization’ (56 times), ‘cybercrime’ (40 times), ‘cyber aggression’ (37 times), ‘depression’ (23 times), ‘aggression’ (14 times), ‘routine activities theory’ (13 times), ‘cyberstalking’ (11 times), ‘gender’ (11 times), ‘longitudinal’ (10 times), ‘peer victimization’ (10 times) and ‘self-esteem’ (10 times).

‘Cyberbullying’ linked with many other keywords, demonstrating the various perspectives in research of this topic. The thick lines which linked ‘cyberbullying’ and ‘bullying’, ‘adolescent’, ‘cyber victimization’, ‘victimization’ showed the strong connections between them; there were close relationship between ‘cyber aggression’, ‘bystander”, ‘self-esteem’ or ‘moral disengagement’ and ‘cyberbullying’.

‘Cybercrime’ had strong links with ‘victimization’, ‘routine activities theory’. In Fig.  8 , the types of cybercrime which occurred at least seven times were: cyberbullying, cyber aggression, hacking, cyberstalking, and cyber dating abuse.

The increasing trend over the years reveals the increasing concern of scholarly community on this field, especially in the boom of information technology and other communication devices and the upward trend in research of cyberspace-related issues (Altarturi et al. 2020 ; Leung et al. 2017 ; Serafin et al. 2019 ). It predicts the growth of cybercrime victimization research in future.

Psychology was the more popular research areas in database, defeating criminology penology. As part of the ‘human factors of cybercrime’, human decision-making based on their psychological perspectives plays as a hot topic in cyber criminology (Leukfeldt and Holt 2020 ). Then, it is observed that journals in psychology field was more prevalent in top of productive sources. Besides, journal Computers in Human Behavior ranked first in total publications, but Journal of Youth and Adolescence ranked higher place in the average citations per document. Generally, top ten journals having highest number of publications on cybercrime victimization are highly qualified ones and at least 10 years in publishing industry.

The USA demonstrated its leading position in the studied domain in terms of total publications as well as the various collaborations with other countries. The publications of the USA occupied much higher than the second and third countries: England and Spain. It is not difficult to explain for this fact due to the impressive productivity of institutions and authors from the USA. A third of top twelve productive institutions were from the USA. Three leading positions of top ten productive authors based on document count were from institutions of the USA, number one was Michelle F. Wright; others were Thomas J. Holt and Bradford W. Reyns.

Furthermore, these authors also participated in significant research groups and become the important nodes in those clusters. The most noticeable authors in co-authors network were Michelle F. Wright. The US institutions also had strong links in research network. The USA was likely to be open in collaboration with numerous countries from different continents in the world. It was assessed to be a crucial partner for others in the international co-publication network (Glänzel and Schubert 2006 ).

As opposed to the USA, most of European countries prefer developing research network within Europe and had a limited collaboration with other areas. Australia, the USA, or Japan was in a small group of countries which had connections with European ones. Nevertheless, European countries still showed great contributions for research of cybercrime victimization and remained stable links in international collaboration. The prominent authors from Euro are Rosario Ortega-Ruiz, Constantinos M. Kokkinos or Rutger Leukfeldt.

It is obvious that the limited number of publications from Asia, Middle East, Africa, or other areas resulted in the uncomprehensive picture of studied subject. For example, in the Southeast Asia, Malaysia and Vietnam lacked the leading authors with their empirical studies to review and examine the nature of cybercrimes, though they are facing to practical challenges and potential threats in the cyberspace (Lusthaus 2020a , b ). The present study indicated that Vietnam, Ireland, or Norway was the new nodes and links in research network.

Several nations which had a small number of publications such as Vietnam, Thailand, Sri Lanka, or Chile started their journey of international publications. It is undeniable that globalization and the context of global village (McLuhan 1992 ) requires more understanding about the whole nations and areas. Conversely, each country or area also desires to engage in international publications. Therefore, new nodes and clusters are expected to increase and expand.

The findings indicated that cyberbullying was the most popular topic on research of cybercrime victimization over the given period. Over a half of most-cited publications was focus on cyberbullying. Additionally, ‘cyberbullying’ was the most frequent author keyword which co-occurred widely with distinctive keywords such as ‘victimization’, ‘adolescents’, ‘bullying’, ‘social media’, ‘internet’, ‘peer victimization’ or ‘anxiety’.

By reviewing keywords, several research gaps were indicated. Research samples were lack of population of the children and elders, while adolescent and youth were frequent samples of numerous studies. Although young people are most active in cyberspace, it is still necessary to understand other populations. Meanwhile, the elderly was assumed to use information and communication technologies to improve their quality of life (Tsai et al. 2015 ), their vulnerability to the risk of cybercrime victimization did not reduce. Those older women were most vulnerable to phishing attacks (Lin et al. 2019 ; Oliveira et al. 2017 ). Similarly, the population of children with distinctive attributes has become a suitable target for cybercriminals, particularly given the context of increasing online learning due to Covid-19 pandemic impacts. These practical gaps should be prioritized to focus on research for looking the suitable solutions in the future. Besides, a vast majority of research were conducted in the scope of one country; some studies collected cross-national data, but the number of these studies were moderate and focused much on developed countries. There are rooms for studies to cover several countries in Southeast Asia or South Africa.

Furthermore, although victims may be both individuals and organizations, most of research concentrated much more on individuals rather than organizations or companies. Wagen and Pieters ( 2020 ) indicated that victims include both human and non-human. They conducted research covering cases of ransomware victimization, Bonet victimization and high-tech virtual theft victimization and applying Actor-Network Theory to provide new aspect which did not aim to individual victims. The number of this kind of research, however, was very limited. Additionally, excepting cyberbullying and cyber aggression were occupied the outstanding quantity of research, other types of cybercrime, especially, e-whoring, or social media-related cybercrime should still be studied more in the future.

Another interesting topic is the impact of family on cybercrime victimization. By reviewing keyword, it is clear that the previous studies aimed to sample of adolescent, hence, there are many keywords linking with parents such as ‘parent-adolescent communication’, ‘parent-adolescent information sharing’, ‘parental mediation’, ‘parental monitoring of cyber behavior’, ‘parental style’. As mentioned above, it is necessary to research more on sample of the elder, then, it is also essential to find out how family members affect the elder’s cybercrime victimization.

It is a big challenge to deal with problems of cybercrime victimization because cybercrime forms become different daily (Näsi et al. 2015 ). Numerous researchers engage in understanding this phenomenon from various angles. The current bibliometric study assessed the scholarly status on cybercrime victimization during 2010–2020 by retrieving SSCI articles from WoS database. There is no study that applied bibliometric method to research on the examined subject. Hence, this paper firstly contributed statistical evidence and visualized findings to literature of cybercrime victimization.

Statistical description was applied to measure the productive authors, institutions, countries/regions, sources, and most-cited documents, mainly based on publication and citation count. The international collaborations among authors, institutions, and countries were assessed by co-authors, while the network of author keywords was created by co-occurrence analysis. The overall scholarly status of cybercrime victimization research was drawn clearly and objectively. The research trend, popular issues and current gaps were reviewed, providing numerous suggestions for policymakers, scholars, and practitioners about cyber-related victimization (Pickering and Byrne 2014 ). Accordingly, the paper indicated the most prevalent authors, most-cited papers but also made summary of contributions of previous research as well as identified research gaps. First, this article supports for PhD candidates or early-career researchers concerning about cybercrime victimization. Identifying the leading authors, remarked journals, or influencing articles, gaps related to a specific research topic is important and useful task for new researchers to start their academic journey. Although this information is relatively simple, it takes time and is not easy for newcomers to find out, especially for ones in poor or developing areas which have limited conditions and opportunities to access international academic sources. Thus, the findings in the current paper provided for them basic but necessary answers to conduct the first step in research. Secondly, by indicating research gaps in relevance to sample, narrow topics or scope of country, the paper suggests future study fulfilling them to complete the field of cybercrime victimization, especial calling for publications from countries which has had a modest position in global research map. Science requires the balance and diversity, not just focusing on a few developed countries or areas. Finally, the present study assists researchers and institutions to determined strategy and potential partners for their development of research collaborations. It not only improve productivity of publication but also create an open and dynamic environment for the development of academic field.

Despite mentioned contributions, this study still has unavoidable limitations. The present paper just focused on SSCI articles from WoS database during 2010–2020. It did not cover other sources of databases that are known such as Scopus, ScienceDirect, or Springer; other types of documents; the whole time; or articles in other languages excepting English. Hence it may not cover all data of examined subject in fact. Moreover, this bibliometric study just performed co-authorship and co-occurrence analysis. The rest of analysis such as citation, co-citation and bibliographic coupling have not been conducted. Research in the future is recommended to perform these kinds of assessment to fill this gap. To visualize the collaboration among authors, institutions, countries, or network of keywords, this study used VOS Viewer software and saved the screenshots as illustrations. Therefore, not all items were displayed in the screenshot figures.

Data availability

Declarations.

The authors declare that they have no competing interest.

1 In the ‘commemorating a decade in existence of the International Journal of Cyber Criminoogy’, Ngo and Jaishankar ( 2017 ) called for further research with focusing on five main areas in the Cyber Criminiology, including (1) defining and classifying cybercrime, (2) assessing the prevalence, nature, and trends of cybercrime, (3) advancing the field of cyber criminology, (4) documenting best practices in combating and preventing cybercrime, and (5) cybercrime and privacy issues.

Contributor Information

Huong Thi Ngoc Ho, Email: moc.liamg@252nhgnouH .

Hai Thanh Luong, Email: [email protected] .

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