Monopoly versus Competition in Setting Accounting Standards
Bibliographic record
Abstract
Financial accounting standards are set by organizations granted a significant degree of monopoly power by various governments. While there has been considerable debate on the merits of national (e.g.,USFinancial Accounting Standards Board (FASB)) versus international (International Accounting Standards Board (IASB)) monopolies, little attention has been paid to the merits of using competing standard‐setting organizations (SSOs) for setting accounting standards. We compare the standard‐setting processes of theFASB/IASBto the processes of four technology‐orientedSSOsto assess the role of competition. We also provide a case study of monopoly and competitive standards in telephony. Both telephony and accounting yield some gains from coordination, and similar arguments are used (under the labels of comparability and consistency of accounting) in debates about granting a monopoly to their respectiveSSOs. Our results show that a group of volunteers competing with the government‐sanctioned monopoly of International Telecommunications Union transformed the telephone industry. Thanks to this standards competition, we enjoy free video internet calling and massive cost savings. Implications for accounting standard setting are discussed.
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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 | 0.034 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".