Note on Self-Selection of Auditors in the Municipal Sector
Bibliographic record
Abstract
The objective of this article is to revisit the literature on Big-N audit fee premiums in the municipal setting using a methodology that controls for self-selection bias. Because auditor choices can be predicted based on certain client characteristics, using standard one-stage ordinary least squares regressions to draw inferences about the presence or absence of such a premium in the extant public-sector audit fee studies may not be appropriate.Results indicate that, after controlling for a self-selection bias, Big-6 (non-Big-6) municipal clients on average pay a fee premium, compared to the case if they were to retain a non-Big-6 (Big-6) auditor. Results continue to hold when we conduct further analyses on a subset of municipalities with access to both Big-6 and non-Big-6 auditors in a local market defined by a 60-km radius, rather than over a province-wide audit market. The existence of non-Big-6 audit fee premiums has not been documented previously in the private- or public-sector audit fee literature. We surmise that it may be caused by the dominance (79.4 percent) of non-Big-6 auditors in the Ontario municipal market, compared to most private-sector audit markets where their market share generally does not exceed 20 percent. The strong market position of non-Big-6 firms in turn may have allowed these auditors to command a fee premium for the subset of municipalities that self-selects to be audited by them.An implication from our study is that Ontario municipalities often choose to be audited by more costly auditors, even though they could have paid lower audit fees by switching to an alternative auditor type. These results do not support those reported by Chaney et al. (2004), who find that U.K. private firms are audited by the least costly auditor type. The conflicting findings may be attributable to the fact that the Ontario municipal audit market is subject to regulation by not just the audit profession but also the Ontario government and that, unlike business corporations, municipalities receive funding from provincial governments to fulfill much of their financial requirements. Thus, municipal clients may be relatively more willing to accept higher audit fees provided their chosen auditor (or auditor type) matches their needs.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".