Slippery Fish: Enforcing Regulation when Agents Learn and Adapt
Author(s) -
Andrés González-Lira,
Ahmed Mushfiq Mobarak
Publication year - 2021
Language(s) - English
Resource type - Reports
DOI - 10.3386/w28610
Subject(s) - fish <actinopterygii> , slippery slope , business , computer science , fishery , law , biology , political science
Attempts to curb undesired behavior through regulation gets complicated when agents can adapt to circumvent enforcement. We test a model of enforcement with learning and adaptation, by auditing vendors selling illegal fish in Chile in a randomized controlled trial, and tracking them daily using mystery shoppers. Conducting audits on a predictable schedule and (counter-intuitively) at high frequency is less effective, as agents learn to take advantage of loopholes. A consumer information campaign proves to be almost as cost-effective and curbing illegal sales, and obviates the need for complex monitoring and policing. The Chilean government subsequently chooses to scale up this campaign. JEL Codes: K42, O1, L51
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