Disaggregating International Regimes: A New Approach to Evaluation and Comparison

1. Introduction: A Disaggregate Approach

Introduction: A Disaggregate Approach 1 Do international regimes actually work? How can we measure the effectiveness of a regime, or the difference it makes to the problem it is meant to address? What conditions promote or impede effectiveness? This book develops a new approach to analyzing international regime effectiveness and applies it to the regional regime for managing fisheries in the Barents Sea.1 The approach is disaggregate in three respects. It decomposes the problem addressed by the regime in a way that applies to all or most international institutions. It splits into three parts the difficult counterfactual analysis of what the outcome would have been had there been no regime. And it decomposes the empirical evidence to maximize the number of observations. This disaggregate approach responds to the need for a bridge between the intensive case study analyses that have dominated empirical studies of regime performance and the increasingly ambitious recent efforts to devise quantitative methods for examining the causal impacts of institutions. Decomposing the specific problem dealt with by an international regime into its cognitional, regulatory, and behavioral components is a key characteristic of the disaggregate approach. The cognitional component entails building a shared and well-founded understanding of what measures available to regime members will best achieve the social purpose of the regime. When the social purpose is the management of living resources, this means clarifying how various levels of harvesting pressure will affect the state of the stocks and their long-term ability to support employment, yield incomes, and provide food.The regulatory component has to do with translating this shared understanding of means–end relationships into normative commitments. The behavioral problem, finally, is to ensure that those normative commitments shape the performance of target groups. Those components are different enough to invite distinctive causal modeling—the factors influencing success or failure are 2 Chapter 1 not necessarily the same—yet general enough to allow comparison with other regimes, which may be beyond the realm of environmental governance. Disaggregating the problem makes the causal analysis more valid and determinate. The key tasks of evaluating regime effectiveness, such as defining what constitutes full problem solving, measuring actual problem solving, and explaining variation in outcomes, are far more tractable when the phenomenon under study is sharply defined and closely related to concrete and observable regime activities such as research, regulation, and compliance control. A second main characteristic of the disaggregate approach is to decompose the counterfactual analysis that forms the backbone of all regime-effectiveness research: one must substantiate what the state of affairs would be in a counterfactual situation in which the regime did not exist. I decompose this analysis by first building and empirically validating a causal model that accounts well for actual variation in problem solving, then go on to examine how the regime affects modeled factor properties, and finally use the results from the causal analysis to narrow the plausible range of counterfactual outcomes. This decomposition of the counterfactual analysis minimizes those aspects of an effectiveness assessment that cannot be firmly linked to observation of actual cases. The third characteristic of the disaggregate approach is that it decomposes the empirical evidence by segmenting the timeline into distinct phases and, where appropriate, by measuring causal factors and outcome diversity at unit levels that maximize the number of relevant observations . Such a decomposition of the evidence allows the use of a wider range of comparative or statistical techniques to substantiate any causal connections between the international regime and the state of the problem the regime is intended to address. The transparency and analytical rigor inherent in the disaggregate approach are reinforced by qualitative comparative analysis (QCA), a set theory–based tool that complements statistical techniques and narrative comparison. The QCA tool, here in the fuzzy set version appropriate for multichotomous or continuous variables, is especially relevant for examining complex causal relationships with intermediate numbers of observations , which is often a predicament in empirical research on regime effectiveness. This book shows how this technique can help identify evidence-based paths to success and failure in the various aspects of problem solving, and the joint ability of the different aspects to achieve Introduction 3 the social purpose. Such paths, or combinations of causal conditions that reliably deliver either high or low performance, help answer the counterfactual question of what problem solving would be like if the regime did not exist. They also facilitate the formulation of...