City Learning
Evidence of Policy Information Diffusion from a Survey of U.S. Mayors
Bibliographic Data
| ID | 7352199 |
|---|---|
| Authors | Katherine Levine Einstein (0000-0001-7487-3646, Boston University, MA, USA), David M Glick (0000-0002-2869-9752, Boston University, MA, USA, corresponding author), Maxwell Palmer (0000-0001-7991-2499, Boston University, MA, USA) |
| Year | 2018 |
| Volume | 72 |
| Issue | 1 |
| Pages | 243-258 |
| Publication date | 2018-07-11 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Research Quarterly (JOURNAL) |
| Journal identifiers | ISSN: 1065-9129 • E-ISSN: 1938-274X |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/1065912918785060 |
| OpenAlex | W2871208659 |
| Language | EN |
| Citations received | 15 |
| References cited | 33 |
Most studies of policy diffusion attempt to infer the processes through which policies spread by observing outputs (policy adoptions). We approach these issues from the other direction by directly analyzing a key policymaking input—information about others’ policies. Moreover, we do so by investigating policy diffusion in cities rather than states. Using a survey of U.S. mayors, more specifically, mayors’ own lists of cities they look to for ideas, we find evidence that distance, similarity, and capacity all influence the likelihood of a policy maker looking to a particular jurisdiction for policy information. We also consider whether these traits are complements or substitutes and provide some evidence for the latter. Specifically, we find that, at times, mayors eschew similarity and distance to look to highly respected “high capacity” cities but that there is no tradeoff between distance and similarity
Computer security · Diffusion · Economics · Jurisdiction · Key (lock · Policy learning · Political science · Public economics · Similarity (geometry · Computer Science · Electoral Systems and Political Participation · Law · Policy Transfer and Learning · Public Policy and Administration Research · Artificial Intelligence
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| Unique citing works | 15 |
|---|---|
| Citations per year | 2,5 |
| Citation span | 2020 - 2026 (7) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 15 |