Accounting Discretion and Managerial Conservatism: An Intertemporal Analysis*
Bibliographic record
Abstract
Abstract Accounting discretion and the principle of conservatism are two salient features embedded in financial reporting systems. Arguably, the practice of conservative accounting choices can never be well understood without incorporating their effect on future periods (the intertemporal effect). This paper provides one explanation for managerial conservatism in a two‐period agency model with hidden information (a binary project type) and hidden actions (the agent's efforts). A piece‐wise linear incentive scheme with accounting earnings as the performance measure is employed. The agent's discretion is the choice of a depreciation method. Discretion is valuable if and only if the agent's marginal productivity of a “bad” project is greater than that of a “good” project, but not to an extreme degree. A conservative depreciation method decreases current compensation in exchange for a “bet” on future compensation and, hence, serves as a commitment device for the agent to signal that the prospect is indeed good. The accounting mechanism replicates the performance of the optimal direct mechanism.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".