How Do People Learn from Not Being Caught? An Experimental Investigation of a 'Non-Occurrence Bias'

43 Pages Posted: 3 Aug 2022 Last revised: 29 Sep 2022

Date Written: July 27, 2022


The law and economics literature has long theorized that one of the goals of law enforcement is specific deterrence, which relies on the conjecture that imperfectly informed offenders learn about the probability of apprehension from their prior interactions with law enforcement agencies. Surprisingly, however, no empirical study has rigorously tried to identify the learning process that underlies the theory of specific deterrence and, more specifically, whether potential repeat offenders learn from getting caught in the same way as they learn from not getting caught. This paper presents novel evidence from a pre-registered randomized controlled trial that sheds new light on these questions. In each of the two stages of the experiment, participants could cheat for an increased monetary payoff at the risk of paying a fine, in the face of an uncertain chance of being audited. Using an incentive-compatible procedure, participants’ beliefs regarding the probability of being audited were rigorously elicited both before and after they were either audited or not audited, allowing us to establish the unique rational-Bayesian benchmark and any deviation thereof for each participant. We find that being audited induces a stronger adjustment in one’s estimate compared to the adjustment induced by not being audited, providing novel evidence for what we call a “non-occurrence bias.” We show that this bias reduces the specific deterrence effect. It also reduces the marginal benefit from investing in enforcement and thus lowers the optimal investment level.

Keywords: Law Enforcement, Learning, Experimental Behavioral Economics, Bayes’ Rule, Belief Updating

JEL Classification: K42, K14, D83, D91

Suggested Citation

Zur, Tom, How Do People Learn from Not Being Caught? An Experimental Investigation of a 'Non-Occurrence Bias' (July 27, 2022). Available at SSRN: or

Tom Zur (Contact Author)

Harvard Law School ( email )

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