“The client can get caught out”: Tax structure maintainability and the intricacies of tax planning aggressiveness
Bibliographic record
Abstract
Abstract In this field study, we examine tax advisors' decision‐making process when developing tax planning arrangements. Through interviews with 40 tax advisors, our analysis indicates that tax savings may come at a price in practice by unveiling adverse post‐implementation experiences shared by tax partners. Partners find themselves in a tricky position at the time they form their recommendation as they cannot be certain that their client will be able to “live with their tax structure”—that is, maintain it and cope with the inherent risks once implemented. Their main concern is that their client may “get caught out” by a structure too aggressive or complicated for them, having no control over the client's behavior once the plan is implemented. This has significant implications in the tax planning decision‐making process as these concerns shape how partners adapt their work to their client's perceived competency and possibly restrain corporate firms' tax aggressiveness. Following Feller and Schanz (2017, Contemporary Accounting Research, 34(1), 494–524), we conceive of this as the fourth hurdle of tax planning— whether a tax structure is maintainable, as perceived by tax advisors—and unpack how it operates. Interestingly, restraining the client's tax planning aggressiveness (and the corresponding potential tax savings) is not necessarily perceived by partners as detrimental to the client relationship. Our findings contribute to a better understanding of tax planning in action, highlighting how tax partners seek to influence their client's tax planning aggressiveness.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".