Accounting Discretion, Horizon Problem, and CEO Retirement Benefits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT: Empirical research on the impact of managerial retirement on discretionary accounting choices is inconclusive, with most studies finding no evidence of earnings management in the pre-retirement period. I argue that income-increasing accounting choices in final pre-retirement years are particularly appealing to managers whose pension depends on firm performance in these years. Using primary data on retired CEOs of Fortune 1000 firms, I investigate the impact of CEO pension plans on discretionary accruals. Consistent with the prediction, I find evidence of income-increasing earnings management in the pre-retirement period only when CEO pension is based on firm performance. I also report evidence of negative abnormal market reaction to CEO retirement in firms with performance-contingent CEO pensions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it