Contract Law is Not Enough: The Many Legal Institutions That Support Contractual Commitments
Bibliographic record
Abstract
One of the fundamental contributions of transaction cost theory and institutional economics has been to focus attention on opening the "black box" of contract enforcement, drawing attention to the institutions required to achieve effective and low-cost contract enforcement. The idea that the effectiveness of contract law is critical to the growth of economic activity is widespread in the literature on development and transition economies. Recent studies attempting to document toe relative strength of contract enforcement in different settings (La Porta, et al., 19982; Djankov, et al., 2003), however, have focused on relatively abstract notions of "courts" and "legal systems" and have yet to investigate the detailed institutional features that make contract law effective and low-cost. Even if it is correct to identify "common law legal systems" as productive of greater economic growth, for example, we still do not know what it is about those systems that produces this growth and in particular how these systems achieve more effective and lower cost contract enforcement. This paper explores in a detailed way the multiple legal institutions at work in the organization of courts, the judiciary, the legal profession, enforcement services, and the process of lawmaking and legal innovation, all of which play significant roles in structuring an effective and cost-effective regime of contract enforcement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".