Rexamining the Hotelling Valuation Principle: Empirical Evidence from Canadian Oil and Gas Royalty Trust
Bibliographic record
Abstract
The Hotelling Valuation Principle (HVP) implies that the in situ value of a unit of a non-renewable resource is equal to current price less the cost of extraction. The required assumptions for this principle are strongly violated in the oil and gas industry, but despite this, results from previous research are mixed, with studies based on market data supporting the principle, and those based on basin-aggregate data rejecting the principle. To address problems with the data choice in previous studies, we test the HVP using market data on Canadian oil and gas royalty trusts. Unlike previous studies using market data for conventional oil and gas companies, our results tend to reject the HVP and we generally find market value to be significantly less than that predicted by the principle. The reduced value is explained by a significantly negative response to a real option to expand (proxied by a call option on oil and gas prices). These findings are consistent with the argument that in the period of rising oil prices that we consider, (2000–06), the net extraction price is high relative to its expected future growth, but production constraints prevent firms from fully exploiting the high price.
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.
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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".