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Whom Do Bureaucrats Believe? A Randomized Controlled Experiment Testing Perceptions of Credibility of Policy Research
Author(s) -
Doberstein Carey
Publication year - 2017
Publication title -
policy studies journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 69
eISSN - 1541-0072
pISSN - 0190-292X
DOI - 10.1111/psj.12166
Subject(s) - credibility , legitimacy , political science , government (linguistics) , public relations , heuristics , test (biology) , subject (documents) , perception , public administration , content analysis , public economics , economics , sociology , psychology , politics , law , social science , paleontology , philosophy , linguistics , neuroscience , library science , computer science , biology , operating system
More than ever before, analysts in government have access to policy‐relevant research and advocacy, which they consume and apply in their role in the policy process. Academics have historically occupied a privileged position of authority and legitimacy, but some argue this is changing with the rapid growth of think tanks and research‐based advocacy organizations. This article documents the findings from a randomized controlled survey experiment using policy analysts from the British Columbia provincial government in Canada to systematically test the source effects of policy research in two subject areas: minimum wage and income‐splitting tax policy. Subjects were asked to read research summaries of these topics and then assess the credibility of each article, but for half of the survey respondents the affiliation/authorship of the content was randomly reassigned. The experimental findings lend evidence to the hypothesis that academic research is perceived to be substantially more credible than think tank or advocacy organization research, regardless of its content. That increasingly externalized policy advice systems are not a pluralistic arena of policy research and advice, but instead subject to powerful heuristics that bureaucrats use to sift through policy‐relevant information and advice, demands added nuance to both location and content‐based policy advisory system models.