The Silent Majority: Private U.S. Firms and Financial Reporting Choices
Bibliographic record
Abstract
ABSTRACT This study uses a comprehensive panel of tax returns to examine the financial reporting choices of medium‐to‐large private U.S. firms, a setting that controls over $9 trillion in capital, vastly outnumbers public U.S. firms across all industries, yet has no financial reporting mandates. We find that nearly two‐thirds of these firms do not produce audited GAAP financial statements. Guided by an agency theory framework, we find that size, ownership dispersion, external debt, and trade credit are positively associated with the choice to produce audited GAAP financial statements, while asset tangibility, age, and internal debt are generally negatively related to this choice. Our findings reveal that (1) equity capital and trade credit exhibit significant explanatory power, suggesting that the primary focus in the literature on debt is too narrow; (2) firm youth, growth, and R&D are positively associated with audited GAAP reporting, reflecting important monitoring roles of financial reporting; and (3) many firms violate standard explanations for financial reporting choices and substantial unexplained heterogeneity in financial reporting remains. We conclude by identifying opportunities for future research.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".