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Record W2155363278

Economic Foundation of Dictatorship in Resource Exporting Economies

2010· preprint· en· W2155363278 on OpenAlexaff
Samer Atallah

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsMcGill University
Fundersnot available
KeywordsEliteNatural resourceDemocracyEconomicsEconomic rentResource (disambiguation)DemocratizationSubsidyInvestment (military)Market economyPolitical sciencePolitics
DOInot available

Abstract

fetched live from OpenAlex

This paper explains the lack of democratization in resource exporting countries using a two period resource extraction model. There are two classes of agents: elite who own capital and natural resources and citizens who own labor. The elite announce, in the rst period, their plans for resource extraction and investment in the economy. Citizens, in the second period, decide whether to conduct a revolution against elite to capture their share of rents from un-extracted resources. Government policies are designed to ensure that the elite remain in power and that citizens do not have the incentive to revolt. These policies subsidize extraction and investment during the rst period. The extraction subsidy reduces the benet of revolution while the investment subsidy increases its cost. On the other hand, policies in the democracy case are not constrained by the revolution threat and represent the median voter preferences. The resource is over extracted in the non-democratic case compared to the democratic case. Also, investment in the non-resource sector is lower. The important nding of the model is that extraction path goes against price signals; rst period extraction increases with the increase of the resource price in the second period. Non-Democratic institution is the rational choice of the elite even with the costly policies to prevent a revolution.

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 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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.291
Teacher spread0.248 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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