Causal Effect of Small Businesses on Street Theft: Evidence from a Natural Experiment of the Beijing Cleanup Campaign

To examine the causal impact of small businesses on street theft and the underlying mechanisms. The “Cleanup Holes in the Wall” campaign in Beijing, China, provides a rare opportunity for a natural experiment. Drawing on street view images processed by deep learning algorithms and other big data sources such as court judgments and location-based service (LBS) population, we use difference-in-difference (DID) models to investigate how the disappearance of small businesses leads to changes in the occurrence of theft. We further examine the mechanisms by introducing mediators, including ambient population and social activity. The treatment units that experienced a mass loss of small businesses showed a significant reduction in street theft compared to the control units that were less affected by the cleanup campaign. Ambient population and social activity played a mediating role in promoting and deterring crime, respectively, with the former dominating. The results remain robust after including covariates in the models, balancing covariates using the propensity score matching method, and adopting alternative thresholds to classify the treatment group. There are two competing yet coexisting mechanisms through which small businesses influence street theft. On the one hand, commercial premises provide large numbers of criminal opportunities for potential offenders; on the other hand, they are central to local social control and order. While small businesses exercise a certain amount of natural surveillance power, as a whole, they function primarily as crime generators. Implications for implementing targeted policies tailored to the nature of small businesses are discussed.