Relationship-Specificity, Contract Enforceability, and Income Smoothing
Bibliographic record
Abstract
ABSTRACT: Contracting parties, such as the firm and its supplier, have cost-reducing incentives to make investments that support the unique transactions between them. However, to the extent that one party may renege on its contractual obligations, the other party incurring the cost of the relationship-specific investment bears additional risk and is less willing to invest such that sub-optimal investment occurs. In countries where enforceability of explicit contracts is particularly weak, parties have incentives to signal their willingness to fulfill implicit claims and maintain long-term relationships. We predict that firms engage in income smoothing to send such a signal to their suppliers. Consistent with these expectations, we find that firms that both reside in countries with weak contract enforceability and operate in industries with a greater need for relationship-specific investments tend to smooth reported income more. We further decompose income smoothing into “informational” and “garbled” components and find that results are driven by the informational component of income smoothing. Our results support the important role that accruals play in providing information in the presence of incomplete contracts. JEL Classifications: F14, K12, L14, M41, M43
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".