Could Accounting Have Saved Itself from the Antitrust Laws?Revisiting the Antitrust Investigations into the US Accounting Profession 1966–1990
Bibliographic record
Abstract
While the role of lobbying in the US public accounting profession has been the subject of several studies, what has not been addressed is the profession's historic reluctance to lobby and the impact this may have had on the profession. This paper provides a case study of public accounting's interaction with government and the need for the profession to articulate the impact of government policies on the practice of accounting. It reviews and assesses the antitrust investigations by the US Justice Department and Federal Trade Commission that led to the repeal of the profession's anticompetitive ethics rules, rules that had governed American public accounting for most of the 20th century. These investigations are often blamed for an increased competitive atmosphere in public accounting that prioritized growth and profitability over quality in attest services. Using records obtained from Freedom of Information Act requests and archival sources, I attempt to reconstruct the US Government's motivations and the efforts of the American Institute of CPAs. I find a troubling lack of understanding of the audit profession by executive branch regulators and Congress and a reticence by the American Institute of Certified Public Accountants to advocate for the profession that led to what many observers see as a profound misapplication of the antitrust laws. While this study deals only with the US, similar regulatory changes took place in Canada, the UK, Australia, and New Zealand.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".