Bibliographic record
Abstract
This study finds a strong relationship at the firm level between differences in the rates of return reported for the financial and tax consolidated groups on one hand and differences in tax return and financial statement capital structure measures on the other. In particular, after controlling for size, profitability, global character (domestic or multinational), industry, and the lack of eliminating entries for intercompany transactions, the authors find that the more the rate of return for the financial group exceeds the rate of return for the tax group, the more the tax measures of leverage, debt, interest, and assets exceed financial measures for the same items. Their results, they note, are consistent with several explanations, including the use of special purpose entities, hybrid financing arrangements, state tax reporting, deconsolidating from worldwide to U.S. reporting, transfer pricing, and the foreign tax credit limitation. The results are also consistent with the premise that taxpayers may be taking advantage of differences in the financial and tax consolidation rules to overreport income performance and underreport risk to shareholders relative to what they report to the IRS. This report is based in part on confidential tax return data obtained from the IRS Statistics of Income Division. The data are protected by nondisclosure requirements under the Internal Revenue Code, and all statistics are presented in an aggregated form.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".