Learning from Laboratory Mistakes: How Policy Entrepreneurs Catalyze City Ordinance Repeals in the United States

Public policies are not static; rather, they change with the context and as consequences become known. We ask how city councils learn about the negative consequences of laws by evaluating the policy diffusion and decision-theoretic learning hypotheses using a case study of criminal activity nuisance ordinance repeals in several cities within one county. These laws as originally written designated properties as “nuisances” if emergency services were called too frequently, including in cases of domestic violence. The seven case cities repealed their laws so survivors of domestic violence would not risk a fine or eviction because they called for help. We argue neither theory is sufficient to explain the repeal of these laws and instead suggest a new variant of policy learning, the entrepreneur catalyzed learning hypothesis, to highlight the importance of policy entrepreneurs in facilitating policy learning and the repeal of unsuccessful laws at the city level