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Record W2136481553 · doi:10.1093/ajae/aav051

The Natural Resource Curse and Fiscal Decentralization

2015· article· en· W2136481553 on OpenAlexaff
Fidel Pérez-Sebastián, Ohad Raveh

Bibliographic record

VenueAmerican Journal of Agricultural Economics · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsSocial Sciences and Humanities Research Council
FundersMinisterio de Ciencia y TecnologíaGeneralitat Valenciana
KeywordsDecentralizationResource curseNatural resourceBoomEconomicsCurseBlessingResource (disambiguation)PoliticsEconomic systemDevelopment economicsPublic economicsMacroeconomicsPolitical scienceEcologyMarket economyGeographyBiology

Abstract

fetched live from OpenAlex

Natural resource abundance is a blessing for some countries, but a curse for others. We show that differences across countries in the degree of fiscal decentralization can contribute to this divergent outcome. Using a large panel of countries covering several decades and various fiscal decentralization and natural resource measures, we provide empirical support for the novel hypothesis. We also study a model that combines political and market mechanisms under a unified framework to illustrate how natural resource booms may create negative effects in fiscally decentralized nations.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.199
Teacher spread0.184 · 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 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

Citations53
Published2015
Admission routes1
Has abstractyes

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