The post-SOX comparative dynamics of public accounting firm efficiency
Bibliographic record
Abstract
Purpose This paper aims to analyze the differences in the efficiency of public accounting firms across both firms and countries in the post-Sarbanes-Oxley world. It also investigates the issues surrounding the dynamics of their efficiency gaps. Design/methodology/approach This study uses four-stage data envelopment analysis to estimate the efficiency of public accounting firms in the USA, the UK and Canada over the period 2008–2015. The ß - and σ- convergence tests are applied to analyze the dynamics of the efficiency gaps across firms and countries. Findings The results show that market competition in the accounting sector increases efficiency. Gross domestic product growth also increases it while inflation decreases it. The analytical results indicate that the lagging public accounting firms are catching up to the leading public accounting firms within the same country, within the Big 4 group and within the non-Big 4 group. They also show that the non-Big 4 groups are catching up to the Big 4 group and that the countries with less efficient accounting firms are catching up to the country with the more efficient accounting firms. Originality/value This study accounts explicitly for the effect of business environmental factors on public accounting firm efficiency. Furthermore, the research also adds to the literature by investigating the comparative dynamics of the efficiency gaps of public accounting firms.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".