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Record W2606345845 · doi:10.5539/ijef.v9n4p95

Crude Oil and the Libyan Economy

2017· article· en· W2606345845 on OpenAlexvenueno aff
Abdulrazag Mohamed Etelawi, Keith A. Blatner, Jill J. McCluskey

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsMulticollinearityEconomicsGross domestic productEconometricsStatisticConsumption (sociology)Inflation (cosmology)EconomyVariance inflation factorLinear regressionMathematicsStatisticsMacroeconomics

Abstract

fetched live from OpenAlex

Oil is the primary source of income in the Libyan economy; hence, it is important to more fully understand the economic factors associated with this sector of the economy. We applied a recent growth theory model to develop a better understanding of the relationship among capital, labor, domestic consumption of oil, oil exports and gross domestic product (GDP) in the Libyan economy. A log linear model was estimated using annual data for the period 1980 to 2012. All of the coefficients were significant at the 0.05 level except the log of labor, which was significant at the 0.0613 level. The signs associated with the variables were consistent with economic theory. The adjusted R square was 0.912 indicating that approximately 91 percent of variation in GDP was explained by the independent variables. There was only limited multicollinearity in the model as all Variance Inflation Factors (VIF) values were less than 10. Breusch Pagan and Anderson-Darling test results indicated a constant variance and that the errors were normally distributed, respectively. Similarly, the Durbin-Watson statistic indicated an absence of autocorrelation at the 0.05 level. The resulting elasticities were positive and strongly inelastic, indicating that large changes in each of the variables would be required to dramatically increase GDP. Nevertheless, it is clear that oil will continue to play a leading future economic growth and development.

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)0.001
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 score0.595
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.018
GPT teacher head0.207
Teacher spread0.189 · 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.

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

Citations8
Published2017
Admission routes1
Has abstractyes

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