Capturing Economic Rents From Resources Through Royalties and Taxes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 it