Assessing the Influence of Different Interest Groups on International Tax Policy: Evidence from the BEPS Project*
Bibliographic record
Abstract
ABSTRACT This study investigates the influence of three interest groups—businesses, the tax profession, and civil society—on tax rules in the context of the Organisation for Economic Co‐operation and Development (OECD) Base Erosion and Profit Shifting (BEPS) project. Our study is important as prior research has not examined the direct influence of various interest groups on the content of tax rules by means of comment letters. Using content analysis, we seek to explain the lobbying success of the different interest groups by examining the relevance of the kind of information transmitted and the alliance strategies used. Results indicate that lobbying success is mainly explained by the vested interests of the three groups, with businesses less successful than the other two interest groups as long as all interest groups are equally able to provide information. We also find that the lobbying success of businesses increases when proposals require specific expertise. However, bias is still relevant for lobbying success as we find that proposals from tax professionals with practical experience, likely to reflect less bias, are relatively more successful than proposals from businesses. Furthermore, our results suggest that mobilizing commenters who have a shared interest in the form of alliances is a promising lobbying strategy. Overall, our findings highlight the importance of expertise and collective actions for lobbying success.
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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 | 0.025 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".