Why Do Firms Bundle and Tie? Evidence from Competitive Markets and Implications for Tying Law
Bibliographic record
Abstract
Tying the sale of products that could be sold separately is common in competitive markets - from left and right shoes, to the sports and living sections of daily newspapers, to cars and radios. This paper presents a cost-based theory for why tying occurs in competitive markets and uses this theory to examine bundling and tying in pain relievers and cold medicines, foreign electrical plug adapters, and mid-sized automobile sedans. It shows that product-specific scale economies are needed to understand tying but that these scale economies might be hard to detect even when they are present. We draw two principle conclusions for tying doctrine. First, per se condemnation in its various manifestations is wrong as a matter of economics. Neither the Jefferson-Parish test in the United States nor the Hilti/Tetra-Pak approach in the EU is capable of screening anti-competitive from pro-competitive tying. Second, if it is hard to establish efficiencies when practices could not arise for anticompetitive reasons, it might also be hard to establish the efficiencies required by the rule of reason or per se approaches. Both approaches are therefore likely to result in the frequent condemnation of efficient tying - that is a high rate of false convictions.
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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 | 0.014 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 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".