Jeb Barnes

Courts and the Puzzle of Institutional Stability and Change: Administrative Drift and Judicial Innovation in the Case of Asbestos

The institutional development literature has begun to move beyond the concept of punctuated equilibrium and consider how the forces of stability and change interact. A central theme involves drift—the shifting of the effect of stable institutions through changing circumstances. This article uses the case of asbestos injury compensation to highlight how the very features of American government that make drift likely also promise to displace it, as courts step in when Congress fails to act. The broader implication is that drift is best understood as a transitional stage of development, not a dominant mode of change, in fragmented policy-making systems with multiple access points.

Is There an Empirical Literature on Rights

The empirical literature that attempts to study rights is at an impasse. It can demonstrate that big claims about how some rights structure politics are overblown, but it has struggled to go beyond this step. This is in large part because studying rights is much more difficult than is commonly appreciated. A study of rights promises implicitly to be a study of how rights politics differs from other kinds of politics. But rights are so ubiquitous and so diverse in form that it is often unclear what the excluded other is. We examine three books on rights that we admire: two by political scientists, Gerald Rosenbergs The Hollow Hope and Michael McCanns Rights at Work, and one by an anthropologist, Sally Merrys Human Rights and Gender Violence. These books conceptualize rights in diverse ways, in diverse settings, using diverse methodologies; yet they run up against similar difficulties in trying to think beyond the cases they study. At the conclusion, we make some humble suggestions for how researchers might try to overcome these problems.

Pathway Analysis and the Search for Causal Mechanisms

The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting cases to study causal mechanisms. We outline a general approach for selecting cases for pathway analysis: a mode of qualitative research that is part of a mixed-method research agenda, which seeks to (1) understand the mechanisms or links underlying an association between some explanatory variable, X1, and an outcome, Y, in particular cases and (2) generate insights from these cases about mechanisms in the unstudied population of cases featuring the X1/Y relationship. The gist of our approach is that researchers should choose cases for comparison in light of two criteria. The first criterion is the expected relationship between X1/Y, which is the degree to which cases are expected to feature the relationship of interest between X1 and Y. The second criterion is variation in case characteristics or the extent to which the cases are likely to feature differences in characteristics that can facilitate hypothesis generation. We demonstrate how to apply our approach and compare it to a leading example of pathway analysis in the so-called resource curse literature, a prominent example of a correlation featuring a nonlinear relationship and multiple causal mechanisms.

The Promise of Process Tracing: Case selection in a World of Causal Complexity

Case studies are widely used in political science, providing a richness of detail that standard quantitative studies often lack. Process-tracing case studies have gained significant prominence recently for their promise to use this strength to explicate causal mechanisms behind robust empirical correlations. Effective case selection is obviously central for process tracing to deliver on this promise, but there are no generally applicable methods for case selection when exploring complex relationships that are non-linear and feature multiple causal mechanisms. To fill this gap, we develop guidelines for selecting process-tracing cases that focus on the functional form of the relationship between relevant variables and the distribution of possible causal mechanisms. Through general examples and specific applications, we show how this approach can reveal opportunities for researchers, while failing to account for these factors can result in poor case selection, mistaken inferences, and an inability to generalize.

Finding Pathways: Case Selection for Studying Causal Mechanisms in Mixed-Methods Research

The study of causal mechanisms interests scholars across the social sciences. Statistical approaches to studying mechanisms can be used to bolster our confidence in the causal connection between variables or estimate the average effect of a mechanism on an outcome. These approaches, however, often require more knowledge about mechanisms than we possess. Case studies can be a valuable tool in developing the knowledge need to utilize these statistical tools to study mechanisms. The usefulness of case studies in the search for causal mechanisms depends on effective case selection and there are few guidelines for selecting cases to study causal mechanisms. We fill that gap by focusing on case selection for “pathway analysis”: the use of case studies to probe causal links between statistically related variables across settings. Our case selection procedures are tied to the goal of building knowledge that can ultimately be utilized in statistical studies of mechanisms. Our approach focuses on selecting cases that allow scholars to both observe the mechanism being studied and to understand what, if any, generalizations can be made from the chosen cases. We demonstrate how to apply our approach and compare our approach to a leading example of pathway analysis in the so-called “resource curse” literature, a prominent example of a well-established X1/Y correlation featuring a non-linear relationship and multiple causal mechanisms.