Avoiding Covid-19 risk information in the United States: The role of attitudes,norms, affect,social dominance orientations,and perceived trustworthiness of scientists

This study seeks guidance from the planned risk information avoidance model to explore drivers of risk information avoidance in the context of COVID-19. Data were collected early during the pandemic. Among our most notable results is that participants who are more oriented toward social dominance and are more skeptical of scientists’ credibility have (1) more supportive attitudes toward risk information avoidance and (2) feel social pressure to avoid risk information. The findings of this study highlight how the role of skepticism in science and intergroup ideologies, such as social dominance, can have important implications for how people learn about health-related information, even in times of heightened crisis.     

Beyond Spatial Models: A New Perspective on American Operational Ideology Using Latent Class Analysis

Analyses of operational ideology—the pattern of correlations between different political attitudes—in the American public generally assume “spatial” models of ideology. Using Latent Class Analysis, we relax many of these assumptions by treating operational ideology as a latent categorical variable and analyze the changing structure of American operational ideology between 2004 and 2020. We find that some Americans during this period held consistently liberal or conservative views and were well sorted into the “correct” political parties. For other Americans, however, we observe complex and shifting relationships between partisanship and economic, moral, and racial attitudes. We find that Racial Justice Communitarians consistently prefer to identify as Democrats, while Nativist Communitarians and Libertarians both tended to identify with whatever party won the most recent presidential election. Future studies of operational ideology should be wary of simplifying assumptions that obscure important dynamics in American politics.     

Directed Matching and Monetary Exchange

We develop a model of monetary exchange where, as in the random matching literature, agents trade bilaterally and not through centralized markets. Rather than assuming they match exogenously and at random, however, we determine who meets whom as part of the equilibrium. We show how to formalize this process of directed matching in dynamic models with double coincidence problems, and present several examples and applications that illustrate how the approach can be used in monetary theory. Some of our results are similar to those in the random matching literature; others differ significantly.     

ONE APPROACH TO REALLOCATING INSTRUCTIONAL RESOURCES: THE HOUSTON MODEL

In this paper a model for deciding which instructional departments should receive and surrender new or reallocatable resources is developed. It combines a two-dimensional assessment of resource needs based on instructional characteristics and the academic priority of the department to arrive at resource allocation priorities. Unusual features of the model are its reliance on judgments of departmental centrality to institutional mission as the discriminating variable in ascribing academic priorities and the careful avoidance of “halo effects” through independent assessment of resource needs and academic priorities. The model is sensitive enough to discriminate between competing claims for scarce resources while retaining a simplicity of logic that makes its mechanics understandable to those with little grounding in the decision sciences.