Great Lobbying Allocation And Dissipation Of Rents In A Dynamic Commonagency Model
Bibliographic record
Abstract
This paper examines a model in which harvesters act strategically in a dynamic game in which they compete for a commonly owned biological resource. Rather than choosing the quantity they harvest in each period, harvesters choose the amount of lobbying effort they expend to obtain a harvest quota from a regulator. The resulting Truthful Markov Perfect steadystate equilibrium is Pareto optimal, given that regulation is in place. However, under certain plausible conditions, the regulated equilibrium is inferior to the non-cooperative common property equilibrium---thus regulation may not be socially beneficial. Furthermore, welfare improving regulation may not be adopted by harvesters, since the regulator may take too large of a share of the rents. Key words: Common agency; Common property; Markov equilibrium. JEL classification: D72, Q22, C73 # I would like to thank Chris Bruce, Jeff Church, Aidan Hollis, Ron Johnson, Ken McKenzie, Liz Wilman, and seminar participants at the University of Calgary for helpful suggestions. All remaining errors are my own. 2 THE GREAT FISH LOBBYING WAR: THE ALLOCATION AND DISSIPATION OF RENTS IN A DYNAMIC COMMONAGENCY MODEL Abstract: This paper examines a model in which harvesters act strategically in a dynamic game in which they compete for a commonly owned biological resource. Rather than choosing the quantity they harvest in each period, harvesters choose the amount of lobbying effort they expend to obtain a harvest quota from a regulator. The resulting Truthful Markov Perfect steadystate equilibrium is Pareto optimal, given that regulation is in place. However, under certain plausible conditions, the regulated equilibrium is inferior to the non-cooperative common property equilibrium---thus regulation may not be socially benefici...
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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".