Do Retailers Pass the Costs of Organized Retail Crime on to Consumers?

Hase and Kasinger (2025) examine whether organized retail crime leads stores to raise prices and how those increases affect consumers. They study the Washington State cannabis market, matching store-level robbery and burglary data to detailed scanner data covering every retail transaction between 2018 and 2021. Using a difference-in-differences design, they find that victimized stores raise prices by about 1.8 percent within four months of a crime. Nearby rival stores raise prices by roughly 1.7 percent. They find no short-run demand shift and little change in wholesale costs, suggesting crime acts like a marginal cost shock passed on to consumers.

Why This Article Was Selected for The Policy Scientist

This article addresses a policy issue with wide economic relevance: whether rising retail crime ultimately operates as a hidden tax on consumers. In a period when organized retail theft has moved to the forefront of public debate, understanding its price effects is central to assessing its full social cost. The study broadens the crime literature beyond property values and business exit by focusing directly on price formation and welfare incidence. In doing so, it connects to foundational work on crime’s social costs and to the tax pass-through literature, extending those frameworks to a contemporary retail setting.

The data set is unusually strong. The authors assemble universe-level scanner data linked to precisely dated crime incidents, allowing credible identification. Their stacked difference-in-differences design exploits quasi-experimental timing variation and includes extensive robustness checks. The findings—roughly 1.7–1.8 percent price increases—are economically meaningful. Generalizability beyond cannabis is plausible, particularly in cash-intensive retail sectors, though institutional differences across jurisdictions warrant further study.

Full Citation and Link to Article

Hase, C., & Kasinger, J. (2025). The Pass-Through of Retail Crime. American Economic Journal: Economic Policy. https://doi.org/10.1257/pol.20240889 

Central Research Question

This article asks whether organized retail crime leads retailers to increase prices and, if so, what the resulting welfare consequences are for consumers and society. The authors focus on the “pass-through” of crime-related cost shocks to retail prices. Specifically, they examine whether robberies and burglaries generate marginal cost increases that are subsequently transmitted to consumers in the form of higher prices. The central issue is not merely whether crime harms businesses, but whether it distorts market outcomes in a way analogous to a hidden tax. By framing retail crime as a potential marginal cost shock, the study evaluates both price responses and the broader distributional and efficiency implications.

Previous Literature

The article builds on two major strands of research. The first examines the economic consequences of crime. Prior studies have linked crime to property values, urban depopulation, business exit, savings behavior, labor supply, and sectoral output. However, little causal evidence exists on whether retail crime directly affects consumer prices. Existing work has often emphasized aggregate economic outcomes or relied on cross-sectional correlations. By contrast, this study isolates store-level crime shocks and measures their effects on prices in a clearly defined market. In doing so, it extends the crime literature into the domain of industrial organization and price formation.

The second strand concerns the pass-through of cost shocks. A large theoretical and empirical literature, including foundational contributions by Weyl and Fabinger (2013), analyzes how marginal cost changes transmit to consumer prices under imperfect competition. This study applies that framework to an unconventional cost shock—organized retail crime. Unlike traditional shocks such as taxes or input price changes, crime-related costs may arise from increased security expenditures, insurance adjustments, or revised expectations about future risk. The authors situate their analysis within this pass-through literature and contribute new evidence on how localized, quasi-random cost shocks influence pricing behavior.

Data

The study uses a uniquely detailed dataset from the Washington State cannabis market covering March 2018 through December 2021. The authors match store-level data on robberies and burglaries to universe-level scanner data capturing every retail transaction across all active cannabis retailers in the state. The transaction data include retail prices, quantities sold, and wholesale prices paid by retailers to producer-processors. Because compliance reporting is mandatory and enforced by the state Liquor and Cannabis Board, the data are comprehensive and highly reliable.

This dataset is unusually well suited for causal analysis. It provides exact timing and location of crime incidents and complete information on store-level pricing and wholesale costs. Such granularity is rarely available in other retail sectors. Over the sample period, the data capture more than $5 billion in retail sales across 511 active stores. The availability of wholesale transaction data allows the authors to test whether retail price increases are driven by upstream cost changes or are instead consistent with crime-induced marginal cost shocks at the retail level. The richness and completeness of the data substantially strengthen the credibility of the findings.

Methods

The authors employ a stacked difference-in-differences (DiD) design that exploits quasi-experimental variation in the timing of robberies and burglaries. Each crime incident defines a sub-experiment in which treated stores are compared to geographically distant “clean” control stores that are not yet treated. This approach addresses concerns about staggered treatment timing and reduces bias from spillover effects to nearby stores. The main dependent variable is a store-level price index constructed from product-level prices and revenue weights, expressed in logarithmic first differences to measure inflation.

The identification strategy relies on the parallel trends assumption and no anticipation. Event-study specifications show no evidence of pre-treatment trends in prices, quantities, or wholesale costs, supporting the validity of the design. The authors cluster standard errors at the store level and conduct extensive robustness checks, including alternative estimators, placebo tests, and variations in control group definitions. Although the study is not a randomized controlled trial, the quasi-experimental design reflects rigorous causal inference principles. The empirical strategy is substantially stronger than simple multivariate regression and is consistent with modern best practices in applied microeconomics.

Findings/Size Effects

The core finding is that organized retail crime leads to economically meaningful price increases. Victimized stores raise prices by approximately 1.8 percent within four months of a robbery or burglary. These increases persist for at least twelve months. Nearby rival stores within a five-mile radius raise prices by roughly 1.7 percent, though with a two-month lag. The similarity in magnitude across victimized and rival stores suggests that crime affects local market conditions rather than only directly targeted firms.

The study finds little evidence that these price increases are driven by short-run demand substitution. Quantities sold do not significantly decline immediately following a crime incident. Wholesale costs also show negligible changes, indicating that upstream suppliers do not adjust prices in response to retail crime. Instead, the pattern is consistent with a marginal cost shock at the retail level. The authors estimate that the implied hidden “crime tax” is approximately $0.28 per unit, or about 1 percent of the average retail price.

Using a sufficient-statistics framework derived from imperfect competition models, the authors estimate an annual welfare cost of $30.7 million in the Washington cannabis market attributable to crime pass-through, with an excess burden of $18.5 million. Consumers bear roughly 67 percent of these costs. A cautious extrapolation to the broader U.S. retail sector suggests that aggregate excess burdens could be substantial, though the authors appropriately note institutional differences across industries.

Heterogeneity analyses reveal that independent stores exhibit stronger pass-through than chain stores. Price increases are also larger in less concentrated markets. These results suggest that market structure and organizational form influence how firms respond to localized cost shocks. The magnitude of the estimated effects—near 2 percent price increases—indicates that retail crime has measurable implications for consumer welfare, even when the underlying shock originates from relatively infrequent criminal events.

Conclusion

This study demonstrates that organized retail crime has direct and persistent effects on consumer prices. By linking store-level crime incidents to universe-level transaction data, the authors provide rare causal evidence that retail crime functions as a marginal cost shock passed on to consumers. The findings extend the economic literature on crime beyond property values and firm exit, showing that crime alters price formation and generates welfare losses analogous to taxation.

The article makes a significant contribution by integrating crime economics with the pass-through literature and by applying rigorous quasi-experimental methods to a detailed administrative dataset. While the institutional features of the cannabis industry—particularly its reliance on cash transactions—may limit full generalizability, the mechanisms identified are likely relevant in other cash-intensive retail sectors. The empirical design reflects strong causal inference, though future research in other industries or jurisdictions could further test external validity. Overall, the study provides systematic evidence that retail crime imposes measurable and distributed economic costs through price channels.