Rates of Return on Flow-Through Shares: Investors and Governments Beware
Bibliographic record
Abstract
Canada’s tax code allows the use of flow-through shares for mining and oil and gas companies on the assumption that they are a good way to spur new productive exploration and are also beneficial to investors. In reality, it appears that flow-through shares are lousy for both. Flow-through shares are designed for corporations that cannot make good use of expense deductions from their taxes and so, through the use of these special type of shares, can pass along their expenses for shareholders to deduct from their own income taxes. This tax break is not insignificant: The amount of revenue foregone by the federal government due to flow-through shares and the related Mineral Exploration Tax Credit averaged $440 million every year between 2007 and 2012. But the steepest price has arguably been borne by investors, with returns on flow-through shares performing extraordinarily poorly. For small companies that issued these shares, the annualized absolute return was a nearly 100 per cent loss. For larger companies, the returns were not as bad — negative 14 per cent — but still a loss. And if adjusted for corresponding benchmarks, the returns were even worse. From the $2.5 billion raised from Canadians using flow-through shares, investors have lost $1.2 billion. Certainly these results would indicate that flow-through shares are hardly helping Canadian explorers strike lucrative new discoveries (it is impossible to say whether the limited success some larger companies had in locating productive assets, using flow-through shares, would not have occurred anyway). Meanwhile, these share issues, bearing the imprimatur of a special government right and the incentive of an investor tax benefit, have likely led to market distortions, luring capital that might have otherwise gone to more productive and rewarding investments. Compounding matters is the very real possibility that those projects that were funded by flow-through shares, but would have been better not begun at all, added competition for inputs and labour, increasing their prices — and lowering returns — for other mining and oil and gas projects with better prospects. In sum, the legacy of flow-through shares is effectively a list of everything that would indicate the policy’s failure. They have hurt investors. They have hurt economic efficiency. They have distorted market competition. And all at a cost to the federal government of nearly half-a-billion dollars a year. Furthermore, the incentives created by flow-through shares can only run counter to any desires among federal and provincial governments to diversify their economies and reduce dependency on mineral and fossil fuel resources. And even where it remains a goal to increase investment in such resources — or any other government-favoured sector, for that matter — it is clear that flow-through shares or tax incentives similar to this policy mechanism are an extremely poor way to achieve it.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".