Learning in Standard-Form Contracts: Theory and Evidence
Bibliographic record
Abstract
Abstract Why are some contractual terms revised continuously while others are stubbornly fixed? We offer an account of both change and stickiness in standard-form contracts. We hypothesize that drafters (sellers) are more likely to revise their standard terms when they have an opportunity to learn about the terms’ costs from experience. Consider a warranty. Offering a warranty in an initial period will expose sellers to claims about malfunction by purchasers, allowing sellers to learn whether it is desirable to offer it going forward. When drafters are unable to learn in this manner, either because they fail to experiment or because the term in question is one where there is no increased opportunity to learn from experience, such terms will be revised relatively less frequently. While learning and change occur through various channels, we posit that, all else equal, terms that carry an opportunity to learn from experience will be revised more frequently, whereas terms or term modalities that do not will contribute to stickiness and stagnation. Our results support this hypothesis. Using a large sample of changes in business and consumer standard-form contracts over a period of seven years, we find that sellers are more likely to revise terms that offer an opportunity to learn from experience than those that do not. These findings are further illustrated and supported by interviews with in-house counsel. The results suggest that standard-form contract terms evolve over time as sellers learn experientially about their costs and risks. Our analysis offers new accounts for the use of boilerplate, stickiness, and change and has normative implications for the optimal design of default rules and product features (JEL codes: K12).
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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 | 0.042 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".