"PROFITS" VERSUS "RENTS" IN ANTITRUST ANALYSIS: AN APPLICATION TO THE CANADIAN WASTE SERVICES MERGER
Bibliographic record
Abstract
Measures of profit the difference between revenue and costs can appear implicitly or explicitly as evidence of market power in antitrust cases. Such evidence is important, since market power is a central condition in deciding whether antitrust laws are violated. In a classic article, Franklin Fisher and John McGowan showed that reliance on measures of accounting rates of return to capital as proxies for economic profits often leads to incorrect inferences about market power from aggregate data.1 In the following, we examine the potential for mistakes in identifying market power in antitrust economics that stem from a confusion between profits and rents} This article is complementary to the Fisher/ McGowan article in two respects: it focuses on the dangers of misconstruing profits in antitrust decisions rather than in aggregate data; and, it highlights a conceptual failure rather than a measurement failure in the definition of profits. We discuss, in particular, the elusive distinction
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".