Bibliographic record
Abstract
ABSTRACT Managerial career concerns could affect firm efficiency through financial reporting quality, but this important link has received relatively little attention in the literature. The present study examines this link by developing a model that has the following elements. A risk‐neutral manager provides effort to increase the market value of the firm and to favorably influence the market assessment of the manager's ability. Depending on the magnitude of career concerns, the manager either underinvests or overinvests effort relative to an efficiency‐maximizing level. The analysis identifies conditions under which higher‐quality reporting induces the manager to invest more effort. Under these conditions, the model is extended to a setting in which the manager also chooses the quality of financial reporting at some cost. In doing so, managers seek to reduce distortion in their effort investment. The equilibrium reporting quality and effort investment are determined by a trade‐off between them. In the presence of high uncertainty about the firm's future cash flows, if the manager's career concerns exceed a threshold the manager underinvests in reporting quality and overinvests effort. The empirical implication is a negative relation between managerial career concerns and financial reporting quality. To a large extent, this is consistent with findings in prior empirical studies. Thus, the present study offers a theoretical explanation for the empirical findings as an equilibrium outcome.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".