Does the Threat of a PCAOB Inspection Mitigate US Institutional Investors' Home Bias?*
Bibliographic record
Abstract
ABSTRACT We exploit the staggered introduction of the PCAOB's international inspection program to examine the role that the stringency of public audit oversight plays in shaping US institutional investors' home bias. Analyzing a sample of foreign firms listed in the United States, we evaluate whether US institutional investors hold larger equity stakes in these firms—a longstanding issue that reflects investor portfolio decisions—if their auditors are exposed to the threat of a PCAOB inspection. In a differences‐in‐differences framework, we find that US‐listed foreign firms enjoy an increase in US institutional investors' equity positions after their auditors become subject to PCAOB inspection access. Cross‐sectional analysis implies that the benefit of the PCAOB inspection threat in mitigating US institutional investors' home bias is concentrated in foreign countries without a strict local audit oversight system; active US institutional investors that are known to value accounting transparency; and firms from countries that grant PCAOB access later (after the onset of its international inspection program in 2005). Our evidence suggests that foreign firms become better known in the capital markets under the PCAOB inspection program, which induces US institutional investors to acquire larger equity stakes in US‐listed foreign firms given the lower information asymmetry that ensues under the PCAOB inspection threat.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".