Bibliographic record
Abstract
ABSTRACT This study tests the agency cost hypothesis in the context of geographic earnings disclosures. The agency cost hypothesis predicts that managers, when not monitored by shareholders, make self‐maximizing decisions that may not necessarily be in the best interest of shareholders. These decisions include aggressively growing the firm, which reduces profitability and destroys firm value. Geographic earnings disclosures provide an interesting context to examine this issue. Beginning with Statement of Financial Accounting Standards No. 131 (SFAS 131), most U.S. multinational firms are no longer required to disclose earnings by geographic area (e.g., net income in Mexico or net income in East Asia). Such nondisclosure potentially reduces the ability of shareholders to monitor managers' decisions related to foreign operations. Using a sample of U.S. multinationals with substantial foreign operations, we find that nondisclosing firms, relative to firms that continue to disclose geographic earnings, experience greater expansion of foreign sales, produce lower foreign profit margins, and have lower firm value in the post–SFAS 131 period. Our conclusions are strengthened by the fact that these differences do not exist in the pre–SFAS 131 period and do not relate to domestic operations. We find differences in the predicted direction only for foreign operations and only after adoption of SFAS 131. Our results are robust to the inclusion of an extensive set of control variables related to alternative corporate governance mechanisms, operating performance, and the firm's information environment. Overall, the results are consistent with the agency cost hypothesis and the important role of financial disclosures in monitoring managers.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".