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Record W3124102416 · doi:10.1093/oep/gpi020

Natural-resource exploitation with costly enforcement of property rights

2005· preprint· en· W3124102416 on OpenAlexfundno aff
Louis Hotte

Bibliographic record

VenueOxford Economic Papers · 2005
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
FundersUniversité de MontréalUniversité Laval
KeywordsEnforcementProductivityProperty rightsWageEconomicsNatural resourceResource (disambiguation)Marginal productPoint (geometry)ExploitDistribution (mathematics)NormativeMarginal costLabour economicsProduction (economics)MicroeconomicsMacroeconomics

Abstract

fetched live from OpenAlex

A model of resource exploitation when private ownership requires costly enforcement is developed. Enforcement costs are endogenized as the outcome of a game between a resource owner and illegal extractors. I find that two instruments are used to deter illegal extraction: policing efforts and purposeful ‘overexploitation’ of the resource. The latter works by reducing the returns from illegal activities. Hence, even with private ownership, the marginal product of a resource worker may be below his marginal product in alternative employment. Conditions are found for which at low wage rates, further wage reductions lower profits. Those conditions are necessary and sufficient for the existence of a range of low wages characterized by a free-access equilibrium. This may explain the more frequent prevalence of free access in less-developed countries. I show that higher resource prices will not lead to more free-access, but may lead to ‘value destruction’.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0030.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.018
GPT teacher head0.204
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2005
Admission routes1
Has abstractyes

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