Bibliographic record
Abstract
We investigate whether regional social capital is associated with a company’s discretionary disclosures of non-GAAP earnings. The US county level social capital is used to capture the informal supervisory instrument of regional social capital. The two dimensions of social capital, dense networks and strong norms in regions with high social capital, cause reciprocity, integrity, and organizational citizenship. Using a panel of US companies, we find that companies located in regions with high social capital (1) are more likely to disclose non-GAAP earnings, and (2) their non-GAAP earnings disclosures have higher quality. These findings are robust to controlling for demographic features, substitute proxies for regional social capital, and using two-stage least squares and propensity score matching approaches. Furthermore, we implement a mediator analysis and show that companies in high social capital regions have lower costs of equity capital because they are more likely to disclose non-GAAP earnings, and their disclosures are of higher quality. Overall, the findings indicate that the informal institutional factor of regional social capital is associated with the crucial voluntary disclosure tool of managers’ non-GAAP earnings.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".