Do Foreign Component Auditors Harm Financial Reporting Quality? A <scp>Subsidiary‐Level</scp> Analysis of Foreign Component Auditor Use*
Bibliographic record
Abstract
ABSTRACT We hypothesize and find that financial reporting quality at the foreign subsidiaries of US multinational companies (MNCs) is higher when the MNC's principal auditor engages a component auditor to audit the foreign subsidiary on its behalf. An important innovation of this study is that we focus on comparing the financial reporting quality of equivalent subsidiaries with and without component auditor work. Our approach contrasts with extant studies that examine the consequences of variation in the total amount of component auditor work at the MNC level. Our results are important for two reasons. First, we provide an alternative view on the consequences of component auditor use compared to the emerging literature in this area, which typically finds a negative association between the extent of component auditor use and financial reporting quality at the MNC level. Thus, we show that a different research design, conducted at the level at which component auditors actually perform their work, yields different inferences. Second, we demonstrate that using component auditors on US MNC group audits is an avenue through which US auditing institutions can affect financial reporting quality in foreign locations. We also reconcile our subsidiary‐level results to the MNC level by introducing a new MNC‐level component auditor “coverage” variable. Overall, we highlight that the best way to audit a foreign subsidiary is likely to be with a component auditor in the local country, which informs the debate surrounding recently proposed PCAOB guidance.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".