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Record W2923854832 · doi:10.34989/san-2016-11

Low for Longer? Why the Global Oil Market in 2014 Is Not Like 1986

2021· article· en· W2923854832 on OpenAlexaff
Bahattin Büyükşahin, Reinhard Ellwanger, Kun Mo, Konrad Zmitrowicz

Bibliographic record

VenueStaff Analytical Notes · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsBank of Canada
Fundersnot available
KeywordsOil priceFalling (accident)CrashEconomicsAgricultural economicsMonetary economicsPsychology

Abstract

fetched live from OpenAlex

In the second half of 2014, oil prices experienced a sharp decline, falling more than 50 per cent between June 2014 and January 2015. A cursory glance at this oil price crash suggests similarities to developments in 1986, when the price of oil declined by more than 50 per cent, initiating an episode of relatively low oil prices that lasted for more than a decade. This analytical note compares the 1986 price decline with the current episode more closely, and its key findings suggest important differences. While oil demand had been falling in the beginning of the 1980s, demand growth currently is being sustained by emerging economies and is projected to be more stable. Also, spare production capacity is significantly smaller today. Due to higher decline rates and shorter investment cycles of unconventional production, current supply is expected to adjust faster to low prices and reductions in investment spending. As long as oil demand from emerging economies remains robust, increases in production will require additional investment in high-cost production. The cost of this incremental production points to higher prices in the medium term than were observed in 2015, although the potential size of a price increase is limited because of ongoing cost-cutting initiatives and technological advances. Due to the fundamental changes in the oil market, it is unlikely that a decade of low oil prices—similar to the experience following the 1986 oil price crash—will repeat itself.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.025
GPT teacher head0.254
Teacher spread0.229 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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