Learning from laboratory mistakes: How policy entrepreneurs catalyze city ordinance repeals in the United States

Publication Date01 Jul 2021
AuthorMegan E Hatch,Joseph W Mead
DOI10.1177/0952076719840070
SubjectRegular Articles
Article
Learning from laboratory
mistakes: How policy
entrepreneurs catalyze
city ordinance repeals
in the United States
Megan E Hatch and Joseph W Mead
Maxine Goodman Levin College of Urban Affairs, Cleveland State
University, Cleveland, OH, USA
Abstract
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 vio-
lence. The seven case cities repealed their laws so survivors of domestic violence would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.
Keywords
Local government, policy entrepreneur, policy learning, policymaking, policy repeal and
regional
Introduction
The U.S. federalist system allows state and local governments to act as laboratories
of democracy, trying out policies and adapting them to local conditions. However,
some laboratory experiments fail while others have unintended consequences. How
Public Policy and Administration
2021, Vol. 36(3) 361–378
!The Author(s) 2019
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DOI: 10.1177/0952076719840070
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Corresponding author:
Megan E Hatch, Maxine Goodman Levin College of Urban Affairs, Cleveland State University, 1717 Euclid
Avenue, UR 316, Cleveland, OH 44115, USA.
Email: m.e.hatch@csuohio.edu
and when do local governments learn from these policy experiments? While some
literature explores why the federal government and states repeal legislation
(Corder, 2004; Lowry, 2005; Ragusa, 2010; Ragusa and Birkhead, 2015; Thom
and An, 2017; Volden, 2016), less is known about cities. We explore this question
through a case study of the repeal process of criminal activity nuisance ordinances
(CANOs) with domestic violence provisions in seven cities in Cuyahoga County,
Ohio. CANOs penalize property owners when emergency services are called a
certain number of times in a specif‌ied period. The common response from land-
lords is to evict their tenant (Desmond and Valdez, 2013). As originally written,
these CANOs meant a survivor of domestic violence could be evicted from their
home because police were called during an attack. The seven cities amended their
CANO to exclude domestic violence calls from the nuisance protocol. We ask why
these cities decided to take this legislative action.
Two common explanations for the spread of public policies across jurisdictions
are policy dif‌fusion and decision-theoretic learning. Policy dif‌fusion occurs when
policies disperse from one jurisdiction to another (Berry and Berry, 1990).
Dif‌fusion through policy learning happens when policymakers observe that a par-
ticular policy is successful in another jurisdiction and decide to adopt the policy
(Lundin et al., 2015; Shipan and Volden, 2008). In contrast, the decision-theoretic
theory posits jurisdictions may adopt the same policy because each independently
found the policy to be the best solution to their problem using information gath-
ered without reference to the decision-making or policies of other jurisdictions
(Volden et al., 2008). In other words, policy dif‌fusion occurs when cities learn
from other jurisdictions’ policies and then choose to adopt their own similar or
identical policy, while decision-theoretic learning is internal to a jurisdiction and
not based on an evaluation of other jurisdictions’ success or failure.
Neither policy dif‌fusion nor decision-theoretic learning suf‌f‌iciently explains
CANO repeals. Instead, we develop a sub-theory that contributes to the policy
learning literature by emphasizing the role of policy entrepreneurs as knowledge
brokers, facilitating the connection between the tangible consequences of a jur-
isdiction’s specif‌ic policies and legislators’ ability to change the law. Prior litera-
ture has identif‌ied knowledge brokers as those who spread information about
other jurisdictions’ solutions to a common problem (Koski, 2010; Nowlin, 2011).
We focus on the role of knowledge brokers highlighting the failures of a policy
within a particular jurisdiction. Policy entrepreneurs do this directly and indir-
ectly, as cities learn about the laws from the entrepreneurs and their ef‌forts in
other cities. The entrepreneurs are successful when other actors (including inter-
nal actors such as policymakers and bureaucrats and external nonprof‌its) are not
because they provide the information and analysis resource-constrained city
councils lack to evaluate their low-salience policies. Our entrepreneur catalyzed
learning hypothesis explains how city councils learn about unsuccessful policies.
In addition, it supports the idea that policy adoption and repeals occur for dif-
ferent reasons (Ragusa and Birkhead, 2015). This article develops a variant of
policy learning theory using a particular geographic scope and type of policy;
362 Public Policy and Administration 36(3)

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