Salience, Scientific Uncertainty, and the Agenda-Setting Power of Science

Rebecca Bromley-Trujillo, Andrew Karch

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Discussions of science and its relevance to public policy have recently taken center stage in political discourse, illustrating the values-based nature of scientific policy decisions. This article uses an original data set of media coverage to examine the ways in which salience and media portrayals of scientific uncertainty affect the agenda-setting process for scientific policy issues. We build upon two scholarly literatures by incorporating research on the use of science in the policy process into the study of policy diffusion among the American states. In doing so, we develop a framework for how media portrayals of scientific information can affect agenda setting. We test this framework on three scientific issues: Genetically modified food labeling, human papillomavirus vaccinations, and indoor tanning. Our results indicate that lawmakers introduce more legislation on salient issues, while higher reported levels of scientific uncertainty reduce the likelihood of bill introductions. This finding illustrates the potential impact of national media coverage as an information transmission mechanism and the necessity of treating policy characteristics as subject to change over time.

Original languageEnglish (US)
Pages (from-to)992-1018
Number of pages27
JournalPolicy Studies Journal
Issue number4
StatePublished - Nov 2021

Bibliographical note

Funding Information:
). The states promote science and technology to varying degrees and those with stronger research infrastructures might be more likely to consider scientific legislation. The models therefore include a proxy for state scientific capacity—an inflation‐adjusted per capita measure of research funds received from the National Institutes of Health. Policy‐specific factors also might drive the consideration of policies. For example, GM food labeling is an issue of particular concern to farmers and the food industry, who often contend that the labels will reduce consumer demand for the products on which their livelihoods depend. The GM‐food‐labeling model therefore includes a proxy for the potential political importance of this constituency: The proportion of the state’s gross domestic product based on agriculture. We expect fewer GM labeling bill introductions in states where agriculture makes up a larger proportion of their economic output. For bills related to the HPV vaccine, the policy‐specific measure is the annual cervical cancer rate. One can think of public policies as efforts to address pressing societal conditions (Nice, ) and the cervical cancer rate indicates the pervasiveness of the “problem” that access to the HPV vaccine might resolve. A similar logic underlies the inclusion of the annual melanoma rate in the indoor tanning model. As state disease rates increase, all else being equal, we expect legislators to introduce more bills on the relevant policy. While salience and perceived scientific uncertainty are the central independent variables in the analysis that follows, it is necessary to consider other correlates of state policymaking. The ACF emphasizes the importance of dynamic external features of the political context and existing diffusion research identifies several factors that might affect agenda setting. One potential influence is “state scientific capacity” (Mintrom, ; Boushey, ; Walker, ). The models therefore include the percentage of the state population living in urban areas. The models also include an inflation‐adjusted logged measure of state per capita income because existing research has consistently found that fiscal capacity facilitates the early adoption of innovations (Boehmke & Skinner, ; Gray, ; Walker, ). The models include additional factors that might influence patterns of bill introduction. Two of these state attributes are proxies for resource availability. Urbanization is associated with the availability of slack resources and multiple studies have found that states with higher urbanization levels tend to be more innovative (Boehmke & Skinner, ). Existing research also highlights the potential impact of partisanship, which is an especially important consideration in an era of polarization. State officials’ reactions to innovative policies can depend on their partisan affiliation, so the models include variables that account for the partisanship of the governor and the state legislature. The ACF and state politics research imply that the political context is likely to influence the adoption of policy innovations. States possess distinct ideological environments and policies are more likely to gain enactment when they reflect those fundamental political proclivities. The models therefore include the annual measure of government ideology that first appeared in Berry, Ringquist, Fording, and Hanson ( ; Daley & Garand, ). During the agenda‐setting stage of the policy process, lawmakers might become aware of a policy that is under review in a neighboring state, leading to additional bill introductions. The models therefore include the percentage of a state’s neighbors in which at least one bill was introduced during the previous year. The second diffusion‐related variable is a measure of ideological proximity. Lawmakers might be more inclined to place a policy option on the political agenda when states with similar political leanings already have considered it. The models therefore include a measure of ideological proximity: The absolute value of the difference between a state’s government ideology score and the average scores of all the states in which lawmakers introduced bills during the prior year. Policy diffusion scholarship assesses the effect of developments in other jurisdictions, and the models include two measures that grow out of this research tradition. The first is a proxy for geographic proximity. Existing studies posit that the existence of a policy in neighboring or nearby states may spur officials to act based on their belief that nearby states are demographically or culturally similar to their own or because they have strong communications networks with their nearby counterparts (Berry & Berry, ). Legislators in highly professionalized chambers may be better able to identify and evaluate new policy options (Boushey, ). Moreover, the chambers might process more legislation during the typical year due to their lengthier sessions and other institutional resources. Finally, the models include two variables that represent the legislative context in which state officials operate. First, they include a dichotomous “previous adopter” variable that indicates whether the policy in question has already gained enactment. For obvious reasons we expect fewer bill introductions to occur in those states. Second, the models include a measure of legislative professionalism, which is generally associated with higher salaries, longer sessions, and greater staff resources (Squire,

Publisher Copyright:
© 2019 Policy Studies Organization


  • media coverage
  • policy diffusion
  • science policy
  • state politics


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