Capturing Economic Rents From Resources Through Royalties and Taxes
Bibliographic record
Abstract
Oil price fluctuations, concerns over the division of resource revenues, and unconventional oil and gas developments are forcing governments to confront the same issue: how to design optimal royalty and corporate tax systems that bring in a publicly acceptable share of revenues without discouraging private investment. This paper surveys tax and royalty systems across six countries, as well as four US states and five Canadian provinces, offering concise analyses of their strengths and shortcomings to describe the best and simplest approaches to both. As in a public-private partnership, government owns the resources and allows private agents to maximize the rents resources generate. An optimal royalty system will thus be rent-based, ensuring that both owner and agent obtain maximally competitive returns so that each has incentives to continue the partnership. Such a system will also be simple, making compliance easy, manipulation difficult, and risks affordable. And it will be stable, instilling in the private sector the confidence needed to invest for the long term. As for corporate income taxes, they should be neutral across business activities, and applied at equal effective rates on economic income, to avoid distorting market forces through subsidies or needless complexity. A clean rent-based tax that allows all costs incurred by producers to be expensed or carried over, along with a corporate income tax system shorn of many of the preferences that negatively affect business activity, should be the way forward for any government looking to update their fiscal regimes for the 21st century.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".