Information‐Processing Costs and Breadth of Ownership
Bibliographic record
Abstract
ABSTRACT Using the U.S. Securities and Exchange Commission's mandate of eXtensible Business Reporting Language (XBRL) as a natural experiment, this study investigates whether and how the decreased information‐processing costs brought about by XBRL influence firms’ breadth of share ownership. We find that the XBRL mandate is associated with an increase in the total number of a firm's shareholders. This finding is consistent with the notion that XBRL facilitates a more transparent environment and decreases information‐processing costs, thereby attracting more shareholders in general. More interestingly, we find that while XBRL adoption is associated with an increase in share ownership of individual and non‐U.S. foreign institutional investors, it is associated with a decrease in share ownership of U.S. domestic institutional investors. Further evidence shows that this asymmetric shift in share ownership is more pronounced for more complex firms. Our findings, taken together, suggest that the decreased information‐processing costs brought about by XBRL help firms establish a level playing field by reducing the information disadvantages of individual and foreign institutional investors over domestic institutional investors. Our results are robust to potential endogeneity concerns and alternative research designs.
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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 | 0.003 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".