Novel Configuration of Formulary Apportionment Using the Correlated Random Effect Approach
Bibliographic record
Abstract
This paper examines various configurations of the formula under the formulary apportionment methodology from the perspective of the explanatory power of the variability in profitability of multinational companies with the aim to identify the best-performing formula based on analytical evidence of panel microeconomic data.The considered configurations of the formula are based on the novel composition of the allocation formula indicated under the BEFIT proposal, preceding the CCCTB proposal, and traditionally used formulas, at the sub-national level, in Canada and the United States.The empirical analysis uses microeconomic panel data obtained from the Orbis database for 77,087 subsidiaries affiliated with 2,283 parent companies observed from 2011 to 2020.Utilising the correlated random effect approach, accounting for time-specific effects, including the time-constant explanatory variables such as economic activity, classified by NACE codes and the EU Member States' jurisdiction, this paper devises a novel formula configuration.Besides a novel configuration of the apportionment formula, consisting of sales, costs of employees, tangible and intangible assets, this paper estimates proportional weights of apportionment factors and concludes with policy recommendations.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".