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DISCOUNT RATE DISTORTIONS AND THE RESOURCE CURSE

2013· article· en· W1545749199 on OpenAlex
Hu Bin, Ross McKitrick

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSouth African Journal of Economics · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEconomicsResource curseEndowmentDemocracyNatural resourceCurseIncentiveConsumption (sociology)PoliticsNon-renewable resourceGovernment (linguistics)MicroeconomicsEconomic systemDevelopment economicsPolitical scienceRenewable energy

Abstract

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Abstract Empirical evidence has suggested a “resource curse” exists, in which countries with abundant resources may have higher initial consumption but then grow more slowly. The effect appears to be dependent on a country's political structure. Theoretical models not typically accounted for historical exceptions, or have not shown the effect exists in a dynamic growth setting. We derive the resource curse effect in an optimal growth model augmented with a political process. The economy has a finite nonrenewable resource, and the government planner can choose to over‐extract natural resources relative to the efficient path by distorting the discount rate, but in so doing incurs political costs that depend on the presence of democracy. Government planners in non‐democratic countries usually have more autonomy in policymaking than those in democratic countries; therefore, the political cost is lower for non‐democratic countries. We show that the incentive for the planner to distort the extraction path is larger, the higher is the initial resource endowment. Consistent with empirical evidence, the distortion raises short‐term consumption but lowers the long‐term growth rate, and institutional differences create corner solutions that explain why some resource‐abundant countries avoid the curse. These results are robust to the inclusion of autonomous technological change.

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Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.173
Teacher spread0.157 · 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