Enhancing Economic Analysis for Fishery Management: Discussion

Each of these papers deals with the application of economic analysis to fisheries management, specifically welfare analysis applied to allocation decisions and to computation of losses from damaged fisheries. The demands on economic analysis to help resolve allocation, natural resource damage, and other management issues have intensified over the past decade and these papers raise several issues common to one or several of these policy questions. The first question is, what should we count as net benefits from policy change in fisheries? One option is to take existing institutional configurations as given, and then measure actual rents in the system. There is a potential dilemma here because, if open access dissipates those rents, then net benefits will be zero before and after policy changes. I do not believe that this is a significant problem in real policy settings. In fact, rents aren't driven to zero in most fisheries because fisheries are no longer pure openaccess fisheries. Most fisheries are either (i) regulated open-access (open-entry but regulated with season, gear, and area controls), or (ii) regulated restricted-access (traditional controls and limited entry). Thus the extent to which rents are actually dissipated depends upon the nature of the regulatory system and specifically upon how well the system binds up input choices. In restricted-access fisheries, evidence suggests that inputs are bound up to some degree and hence license values provide a good measure of changes in rents in the system. What about regulated openaccess fisheries? Data suggests that even these fisheries are bound up by biological controls, are sluggish, and thus can and do sustain quasirents for considerable periods. There are two implications resulting from these observations. First, if we wish to use actual rents generated from existing institutions to measure net benefits from policy changes, in most cases there will be something that we can measure, either changes in license values, or in computed quasi-rents. Second, there are significant difficulties associated with forecasting long-run policy effects, because we need not only to forecast how the industry (technology) will evolve as a result of policy changes, but also how the institutional (regulatory) structure will evolve. This is necessary because actual rents are jointly det rmined by the interactions and behavior of both the industry and the regulatory system. Moreover, the forecasting problem is nontrivial, because it is likely that the process is dynamic and volutionary, with the industry responding to regulatory changes, followed by regulators responding to technology changes, and so on. An alternative to measuring what does emerge as rent is to measure what might emerge under ideal circumstances; ie. to sidestep the whole issue of forecasting the actual interaction and evolution of the regulated system and instead try to estimate what an efficient system might generate as rents. This principle of using (maximum) potential rents to benchmark policy changes is conceptually attractive since it is akin to policy analysis using potential Pareto improvements. In theory, this standard of comparison should prove easier to use, because it avoids having to fo cast the joint evolution of technology and regulatory structure discussed above. However, determining efficient technologies in practice may not necessarily be as straightforward as it might seem at first. For example, we might attempt to calculate potential rents by projecting how much of the existing fleet is necessary to harvest curr nt total allowable catches and then compute rents that could accrue to such a reduced fleet.