Tax Loss Utilization and Corporate Groups: A Policy Conundrum
Bibliographic record
Abstract
There are both theoretical and practical tax policy considerations that favour a broad recognition for the value of corporate income tax losses-- including for businesses operated within corporate groups. Ideally, an equitable and economically efficient tax system could obviate the need for loss netting against income by providing for the tax value of losses from business to be refundable by tax authorities in cash to owners. This approach, however, involves many serious difficulties, including revenue cost to governments and potential for abuse by both domestic and foreign businesses. Accordingly, loss refundability tends to be provided for only sparingly, if at all; while many corporate income tax systems—such as in the U.S. the U.K., Japan and many other OECD countries--deal with loss netting within corporate groups through a formal system of tax loss transfer or tax consolidation. While Canadian policymakers have considered introduction of such a system over a long period of time, they have yet to come up with a satisfactory formal system for Canada. So, corporate groups in Canada have been left to make do with an informal self-help loss trading system that presents a number of problems compared to formal systems. As a federal country with substantial corporate taxation levied at the provincial level, Canada appears unusually constrained in what it can do to bring greater equity and efficiency to corporate group tax loss utilization. Moreover, the inefficiencies in the current system are small in aggregate terms, and the informal self-help system has a relatively generous threshold for access. Accordingly, while Canada’s current informal self-help corporate group loss system is far from ideal, it appears to remain as a workable approach. Alternatives to the status quo should be considered cautiously, as they have the potential to do more harm than good.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".