By E. Lopez

With the holiday season upon us, shopping at store locations throughout the nation will increase. Bigger crowds are a welcome sight for retailers, but they also amplify concerns about shoplifting and the safety of the shopping experience for consumers and employees alike.

This report builds on a previous Council on Criminal Justice (CCJ) shoplifting report and CCJ’s ongoing crime trends reports by exploring recent trends in shoplifting for the nation’s three largest cities—Chicago, Los Angeles, and New York. It also examines seasonal trends for a sample of 23 cities and takes a closer look at shoplifting data available from the FBI.

Key Takeaways

Data collected through the fall of 2024 for Chicago, Los Angeles, and New York suggest that shoplifting levels remain higher than pre-2020 rates. Chicago, in particular, experienced notably elevated rates of reported shoplifting through the first 10 months of this year. In 2023, rates were 10% lower in Chicago, 87% higher in Los Angeles, and 55% higher in New York than in 2019.

Over the past several years, shoplifting rates were higher in November and December than they were during earlier months of the year, coinciding with increased in-person retail activity. Because shoplifting rates in a 23-city sample for the first half of 2024 are higher than in 2023, it is likely that the reported shoplifting rate for the full year will rise from 2023 to 2024.

Two national sources of law enforcement data on reported shoplifting—both available from the FBI—show different trends. Statistics from the Summary Reporting System (SRS) suggest that reported shoplifting in 2023 was the same level as in 2019. However, rates from the National Incident-Based Reporting System (NIBRS), show that shoplifting was 93% higher in 2023 than it was in 2019.

It is unclear why there is a sizable difference between these two sources. One possibility is that law enforcement agencies recently added to the group providing data through NIBRS reported disproportionally higher levels of shoplifting, even after adjusting for an increase in population coverage. Clear guidance from the FBI on the limitations of the data and the implications of using certain sources of FBI crime data is needed

Washington, DC: Council on Criminal Justice. 2024. 9p.

By Ernesto Lopez, Robert Boxerman and Keley CundiffEErnes

Since shortly after the onset of the COVID-19 pandemic, the Council on Criminal Justice has tracked changing rates of violent and property crime in large cities across the United States. The pandemic, as well as the social justice protests during the summer of 2020 and other factors, have altered the motives, means, and opportunities to commit crimes.

Prepared for the Council on Criminal Justice’s Crime Trends Working Group, this report focuses on trends in shoplifting, a subset of retail theft which, in turn, is a subset of overall larceny-theft. The FBI defines larceny-theft as the unlawful taking of property without force, violence, or fraud.

The report looks at shoplifting patterns from before the onset of the COVID-19 pandemic through mid-year 2023. To date, attempts to measure changes in retail theft, including organized retail theft, have relied on retail industry data5 or have been limited to one state.

The city-specific data included in this report are drawn from open-data sources from 24 cities that, over the past five years, have consistently reported specific shoplifting data. Additional data come from the U.S. Justice Department’s National Incident-Based Reporting Program (NIBRS).7 The NIBRS data include a sample of 3,812 local law enforcement agencies. The analyses examine the changing frequency of reported shoplifting, trends in other property offenses, changes in the value of stolen goods, offenses that co-occur with shoplifting, and the number of people involved in each incident.

This report does not discuss in detail shoplifting data from the National Retail Federation’s Retail Security Survey.8 The 2021 survey (data ending in 2020) was the last year the survey reported figures on the number of incidents and the value of stolen goods. Because of this change, data from the survey could not be included.

Due to a lack of available data, this report does not examine factors that could be influencing the trends. Potential factors include changes in retailers’ anti-theft measures and changes in how retailers report shoplifting to law enforcement, which could be based on their perceptions of the extent to which local police or prosecutors will apprehend suspects and pursue criminal charges. Because these data rely on reported incidents, they almost certainly undercount total shoplifting. The findings presented here should be viewed with these considerations in mind.

Washington, DC: Council on Criminal Justice, 2023. 8p.

By John Aloysius, Ankur Arora, and Viswanath Venkatesh

Purpose: Retailers are implementing technology-enabled mobile checkout processes in their stores to improve service quality, decrease labor costs and gain operational efficiency. These new checkout processes have increased customer convenience primarily by providing them autonomy in sales transactions in that store employee interventions play a reduced role. However, this autonomy has the unintended consequence of altering the checks and balances inherent in a traditional employee-assisted checkout process. Retailers, already grappling with shoplifting, with an estimated annual cost of billions of dollars, fear that the problem may be exacerbated by mobile checkout and concomitant customer autonomy. The purpose of this paper is to understand the effect of mobile checkout processes in retail stores on cybercrime in the form of shoplifting enabled by a technology transformed the retail environment.

Design/methodology/approach The authors conducted an online survey of a US sample recruited from a crowdsourced platform. The authors test a research model that aims to understand the factors that influence the intention to shoplift in three different mobile checkout settings − namely, smartphone checkout settings, store-provided mobile device checkout settings, and employee-assisted mobile checkout settings − and compare it with a traditional fixed location checkout setting.

Findings The authors found that, in a smartphone checkout setting, intention to shoplift was driven by experiential beliefs and peer influence, and experiential beliefs and peer influence had a stronger effect for prospective shoplifters when compared to experienced shoplifters; in a store-provided mobile devices checkout setting, experiential beliefs had a negative effect on shoplifters’ intention to shoplift and the effect was weaker for prospective shoplifters when compared to experienced shoplifters. The results also indicated that in an employee-assisted mobile checkout setting, intention to shoplift was driven by experiential beliefs and peer influence, and experiential beliefs had a stronger effect for prospective shoplifters when compared to experienced shoplifters.

Originality/value This study is the among the first, if not first, to examine shoplifters’ intention to shoplift in mobile checkout settings. We provide insights into how those who may not have considered shoplifting in less favorable criminogenic settings may change their behavior due to the autonomy provided by mobile checkout settings and also provide an understanding of the shoplifting intention for both prospective and experienced shoplifters in different mobile checkout settings.

Information Technology and People 32(5):1234-1261 , April 2019, p 39