Modeling Fisheries Agreements with Side Payments: The Case of Western Atlantic Bluefin Tuna
Bibliographic record
Abstract
This paper tries to fill a gap in the fisheries economics literature by proposing static and dynamic models with side payments in a stochastic and sequentially harvested fishery. The incentive problem considered in this paper arises between two central regulatory authorities who manage an internationally shared fishery, i.e., when the regulator of one fishing nation (Principal) provides monetary compensation to induce the regulator of another country (Agent) to restrict own fishing activities. Most of the previous analysis for international fishing agreements had either focused on cooperation without side payments (and particular in the context of common-pool resources), or had introduced payments under the implicit assumption that it is politically acceptable for a fishing nation to completely “buy-out” another. Compensation in this paper, instead, induces the Agent to harvest less extensively, keeping nonetheless the right to operate exclusively in her own area. The conditions characterizing the solution of the dynamic model in this paper are the analogue of the Martingale Property from the finance literature. The Western Atlantic Bluefin Tuna fishery serves as an example to illustrate the model with some plausible parameter values. The calibrated model predicts that side payments from Canada to the US could increase Canadian welfare by US$5–10 million, if predicted by the static model; and between US$9.7–16.3, if predicted by the two-period model. Overall, the theoretical development and empirical application of this paper illustrate how side payments can be a helpful additional instrument for designing international fishing agreements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".