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5. Regulatory Effectiveness
- The MIT Press
- Chapter
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Regulatory Effectiveness 5 The regulatory problem in focus for international regimes is to establish a set of behavioral rules that jointly reflect the best available knowledge on how to achieve the social purpose of the regime. In fisheries management , that purpose is to maximize the long-term yield from the resource, which includes safeguarding its ability to replenish—in other words, it is a question of balancing utilization and conservation. Chapter 4 showed that the Barents Sea fisheries regime has helped improve the accuracy of scientific forecasts of how levels of harvesting will affect replenishment and sustainability. In this chapter we will see how the state of such scientific knowledge is one of the factors that can explain variation in regulatory problem solving. This chapter is the second to apply the disaggregate approach to international regime effectiveness developed in chapter 2, centered on counterfactual path analysis and the Oslo-Potsdam yardstick. This yardstick compares actual problem-solving scores with those that would most plausibly be achieved if the regime had not existed, so the first section examines various bases for measuring the regulatory problem. The scale used here is based on the scientific advice that ICES provides concerning various options for total harvesting levels and allows us to specify in concrete terms what would constitute full problem solving. Considerable variation emerges when we apply this scale to twenty-five actual decisions on cod quotas under the regime. Substantiating the most plausible level of problem solving that would pertain if there were no regime requires a good account of the drivers of and impediments to problem solving. That is why I will specify a model for explaining the observed variation in regulatory problem solving. Among the factors included here, two emerge from utilitarian considerations: malignancy is about short-term incentives to exceed the quota advice, whereas collaboration concerns ways to reduce the costs 154 Chapter 5 of heeding the advice. The knowledge factor derives from theories of persuasion and learning, while a fourth causal factor, urgency, refers to a bargaining dynamics that includes utilitarian considerations as well as learning. Validating this model empirically is the business of the subsequent section, which identifies, by means of fuzzy set qualitative comparative analysis (QCA), certain ideal-type combinations of causal properties that reliably deliver either high or low regulatory problem solving. Those success and failure paths allow us to rephrase the ultimate counterfactual question in regime-effectiveness analysis, concerning what the outcome would have been if the regime had not existed, into a much simpler one—what the scores on the causal factors would be. Whenever such counterfactual paths fit one or more reliable failure or success paths, we can place upper or lower bounds, or both, on the plausible range of counterfactual outcome estimates. That is because counterfactual cases should be expected to behave similarly to what equivalent actual cases do—and actual cases reliably achieve problem-solving scores equal to or greater than their score on the ideal-type causal combination. Equipped with actual problem-solving scores and empirically based estimates of counterfactual scores, both defined by their distance to a full problemsolving score, we can proceed to calculate regulatory effectiveness scores for each year under study. The Problem: Keeping Quotas within Scientific Advice Since the mid-1970s, the setting of annual total allowable catches (TACs) for individual stocks has served as the foremost regulatory instrument in international fisheries management and thus is a natural point of departure for examining regulatory problem solving. The main purpose of such quantitative caps is to retain enough of the spawning stock to ensure replenishment and, where ecosystem concerns are prominent, to accommodate the needs of other predators besides man. The NorwegianRussian Joint Commission on Fisheries meets annually to adopt and allocate total quotas and other regulations for each of the shared stocks, including Northeast Arctic cod. These quotas and regulations are binding on both parties unless they opt out within two months. This section first considers how to specify the best available knowledge on balancing utilization and conservation. It then develops a yardstick for measuring degrees of regulatory problem solving, and finally it assigns actual problem-solving scores to each of the years under study. [54.144.95.36] Project MUSE (2024-03-28 19:32 GMT) Regulatory Effectiveness 155 Specifying Regulatory Problem Solving Scientific advice is not a self-evident reference for evaluating the quota decisions of the Joint Commission, but it is the best one. At least two complementary or alternative yardsticks deserve...