Do Family Firms Issue More Readable Annual Reports? Evidence From the United States
Bibliographic record
Abstract
Using a sample of 22,442 firm-year observations for 3,721 U.S. listed firms, we show that family firms, on average, issue annual reports with higher readability than non-family firms. Higher readability could occur due to lower obfuscation or less information conveyance. By controlling complexity and choosing readability measures linked to obfuscation, we attribute the higher readability to lower obfuscation. Our investigation into the heterogeneity in family firms shows that the positive effect of family control on reporting readability exists for eponymous family firms but not for non-eponymous family firms. We also find that family firms managed by founders or heirs issue more readable 10-K reports than non-family firms, but family firms managed by outsiders do not exhibit such a difference. Cross-sectional analyses show that the difference in readability between family and non-family firms diminishes for firms with more earnings manipulation, weaker board governance, and dual-class shares. Further, we find that investors perceive family firms’ annual reports with higher readability to be more informative. Finally, we use state-level succession tax cuts as an exogenous shock to link the higher readability to family insiders’ incentives and preferences. Our findings are consistent with the view that family insiders’ incentive to maintain family reputation contributes to lower obfuscation in 10-K narrative disclosures.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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".