Competition Conditions in Taiwan’s Public Accounting Industry
Bibliographic record
Abstract
This paper investigates the degree of market competition in the public accounting industry of Taiwan over the period 1994-2008, using the ‘H statistic’ proposed by Panzar & Rosse (1987). Differing from previous works, this paper applies a newly developed model, i.e., the censored stochastic frontier (CSF) model, to test whether the audit market has achieved its long-run equilibrium. The model is superior to the conventional model that requires researchers adding a unity to the dependent variable of returns on assets (ROA) for all observations, forcing the transformed dependent variable to be non-negative. One can then take the natural logarithm of this dependent variable. Evidence shows that Taiwan’s accounting industry is characterized as monopolistic competition with a trend towards perfect competition. The result will help to build up the empirical model for public accounting industry. The CSF model confirms that this industry is already in a long-run equilibrium in the second half of the sample, which validates the use of the Panzar-Rosse model. Conversely, the employment of the conventional approach leads to a rejection of the long-run equilibrium over the entire sample period.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".