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Record W2599667604

Curse or Blessing? How Institutions Determine Success in Resource-Rich Economies

2017· article· en· W2599667604 on OpenAlexaboutno aff
Peter Kaznacheev

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsBlessingResource curseCurseEconomicsNatural resourceOil reservesDevelopment economicsProperty rightsResource (disambiguation)GeopoliticsEconomyMarket economyBusinessEconomic policyNatural resource economicsPoliticsPetroleumPolitical scienceGeographyLaw
DOInot available

Abstract

fetched live from OpenAlex

One of the main reasons for the drop in oil prices that began in 2014 was a rapid increase in U.S. oil production—it reached the level of the other two biggest producers, Russia and Saudi Arabia, that same year. That, in turn, decreased U.S. demand for imported petroleum and hence put downward pressure on the oil price worldwide. There is one aspect of the shale revolution that gets much less attention than geopolitical or environmental issues: What were the institutional conditions that allowed the technological innovation to happen? In essence, it was a combination of secure property rights, a favorable tax regime, minimal red tape, and a strong entrepreneurial culture (there were around 13,000 small U.S. oil companies fiercely competing with each other). This paper explains how the quality of institutions determines whether natural resource abundance is a blessing or a curse: Will it boost or stifle innovation and economic development? Institutional deficiency in resource economies perpetuates rent-seeking, autocracy, and slower economic growth as illustrated by multiple examples. One of the most alarming among them is Venezuela. While the country possesses the largest oil reserves in the world it is at a brink of economic collapse and is struggling with mass food shortages. Nonetheless, the evidence presented in this paper is at odds with the “resource curse” hypothesis that mineral-exporting countries are doomed to stagnation. A number of countries with high levels of economic freedom, such as Australia, Canada, Chile, and Norway, demonstrate that it is possible to build a prosperous and innovative economy with a significant share of income from the sale of minerals. Furthermore, sound institutions can help diversify the economy and weather the storm of low commodity prices. As exemplified by several countries in the 1980s and in later years, petroleum exporters with strong institutions can achieve positive growth even during oil price drops.

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)0.013
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
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.008
Scholarly communication0.0070.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.045
GPT teacher head0.262
Teacher spread0.217 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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