Destination-based cash-flow taxation: A critical appraisal
Bibliographic record
Abstract
This article offers the first comprehensive scholarly response to proposals for destination-based, cash-flow taxation (DCFT). DCFT proposals have attracted heightened public attention in 2016 because of the incorporation of one version into the US House Republican blueprint for tax reform and Donald Trump’s subsequent election to the White House. They also continue to fascinate tax specialists by suggesting that corporate profit can be taxed not only in countries of ‘source’ or ‘residence’ but also (or even exclusively) in the countries where sales to final consumers occur. This article clarifies the logical structure of DCFT proposals and exposes substantial gaps between their rhetoric and technical details. I argue first that it is crucial to distinguish between two versions of the DCFT. The first version resembles proposals for taxing corporate income by sales-factor-only formulary apportionment. The second version, which is what the US House Republican blueprint proposes, resembles a destination-based value-added tax with deductions for labour costs and refundable losses. I argue that the latter version of the DCFT introduces no fundamental new option into international tax design. Instead, it may create substantial trade distortions (and its loss refund feature is also unlikely to be administrable). The first version of the DCFT does present a new option for taxing corporate profit but is un-implementable. I also highlight ways in which DCFT proposals make ad hoc normative and behavioral assumptions. Finally, the article offers a novel explanation of why it is difficult to incorporate information about consumer location into international tax design and argues that ‘residence’ is more promising than ‘destination’ (when both are understood as capturing information about natural persons) for dealing with problems arising from capital mobility.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.015 |
| 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".