Improvements in investment efficiency prior to a mandated accounting change: Evidence from <scp>ASC</scp> 842
Bibliographic record
Abstract
Abstract Prior literature on the relationship between financial reporting and investment efficiency generally overlooks the connection between firms' financial and managerial reporting systems. As a result, it is difficult to determine whether increases in the quality of firms' internal information environments (IIQ) and/or the quality of their external information environments (EIQ) explain improvements in investment efficiency following financial reporting changes. Leveraging the transition window to the new lease standard (Accounting Standards Codification [ASC] 842), we use a difference‐in‐differences design and find that firms that materially change their internal controls due to ASC 842 (treatment firms) significantly improve their investment efficiency in the final year of the transition window. Multiple falsification tests rule out that contemporaneous improvements in treatment firms' EIQ explain our finding. Additional channel analyses suggest the increases in IIQ for treatment firms predominantly alleviate moral hazard risk between central and divisional managers within the firm, leading to a reduction in empire building. Our findings extend the literature on the relationship between financial reporting and investment efficiency. They also contribute to the literature on the consequences of ASC 842 by answering the FASB's call for research on how ASC 842 affects firms' asset utilizations.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".