The Core, Transaction Costs, and the Coase Theorem
Bibliographic record
Abstract
This paper clarifies and synthesizes elements of the two decade old debate concerning the Coase theorem and the empty core. Five lessons can be derived from this debate. First, the Coase theorem may break down when there are more than two participants (provided the additional participants bring an additional externality to the table). Second, the problem of the empty core does not disappear in a world of positive transaction costs. Under reasonable assumptions about the transactions technology, transaction costs may well exacerbate the empty-core problem. As a consequence, it is important to differentiate between transaction costs (when the core exists) and costs due to the empty core because each has different implications for rationalizing institutional arrangements. Third, the Coase theorem will not break down when the number of participants increases if the new participants do not bring additional externalities with them. If, however, additional participants bring in additional externalities, then the core may be empty and Pareto efficiency may not emerge from costless negotiations. Fourth, Pareto Optimality can be achieved when the core is empty by judicious use of penalty clauses, binding contracts, and constraints on the bargaining mechanism. Fifth, when a non-excludable public good is involved, a free-rider problem arises as the number of agents increases, and this undermines the Coase theorem; in this case, Coasean efficiency requires the participation of all agents affected by the externality in the writing of binding contracts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".