The Effect of the Current Expected Credit Loss Model on Conditional Conservatism of Banks and Its Spillover Effect on Borrower Conservatism
Bibliographic record
Abstract
ABSTRACT Under the Current Expected Credit Loss (CECL) model, banks should fully recognize expected lifetime credit losses upon loan origination while gradually recognizing interest revenues. This timelier recognition of losses versus gains (i.e., conditional conservatism) makes banks more capital constrained. To mitigate this, banks may (1) offset timelier credit losses by lowering conservatism in other earnings components and (2) reduce credit losses by demanding greater borrower conservatism. We find that, under CECL, banks increase conservatism in loan losses but decrease conservatism in other earnings components, making overall conservatism only marginally increase. In sharp contrast, their borrowers increase conservatism by 40 percent, and borrowers’ increase is twice that of banks. This substantial spillover effect suggests that, by greatly increasing borrowers’ conservatism, CECL may strengthen debt governance of a broad scope of firms in the economy, thereby having economy-wide consequences beyond the banking industry and potentially enhancing the stability of the entire economy. Data Availability: Data are publicly available from the sources identified in the study. JEL Classifications: G21; M41; M48.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".