Labor Costs of Implementing New Accounting Standards
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
SYNOPSIS Although much research focuses on informational benefits of new accounting standards, the costs of implementing them remain largely unexamined. We consider one such cost in the adoption of two recent standards: lease accounting and revenue recognition. We find an increase in the number of job postings demanding skills related to accounting for those standards around their issuance. Firms most affected by new standards, measured by accounting complexity and early adoption behavior, post more accounting jobs. Using job postings as a proxy for hiring, we estimate incremental labor costs at about 30 percent of median audit fees for each standard for the most affected firms. Our tests indicate greater regulatory compliance burden for smaller firms. We provide large-sample evidence on the lower bound for the costs of implementing new accounting standards. Our findings should interest standard setters as they evaluate the cost-benefit tradeoffs of issuing new standards. Data Availability: Data are commercially available from the sources cited in the text. JEL Classifications: J23; M41; M51.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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