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Record W2287881070 · doi:10.55016/ojs/sppp.v8i1.42548

A Comparative Anatomy of Oil Price Routs: A Review of Four Price Routs between 1985 and 2014

2015· review· en· W2287881070 on OpenAlexaffabout
Robert Skinner

Bibliographic record

VenueThe School of Public Policy Publications · 2015
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOil priceEconomicsMonetary economics

Abstract

fetched live from OpenAlex

With layoffs and cutbacks in the oilpatch and the ripple effects spreading out through Canada’s economy, it may seem as though the latest drop in oil prices could stall the economic engine in Alberta for a long time. This paper argues, however, that the current rout is unlike the other three major ones (when the price of oil dropped 60 per cent or more) experienced in the last 30 years. The factors affecting the oil price drop and its potential for rebound differ significantly. The routs of 2008 and 1997 were mostly demand-driven, triggered by credit crises in key markets. Both 2014 and 1985 started as demand-driven, too, but significantly, they changed to supply-driven crashes. There, however, the similarities end between today and 30 years ago. Fears that we may be in for a long-term rout, similar to that of 1985, which lasted more than a decade, may be allayed by examining the very different circumstances surrounding the contemporary situation. Given an uninspiring macroeconomic outlook, a supply-side solution will be imperative. A critical difference lies in the security of spare productive capacity. In 1985, OPEC’s spare capacity approached twenty per cent of world demand. Today, it is closer to two per cent. Moreover, today’s producer of the disruptive non-OPEC barrels in the shale oil industry in particular is far more reliant on external financing than were companies developing, for example, the North Sea 30 years ago. Then, with much higher prime rates, firms largely self-generated their financing. In addition, there was considerable room for cost-cutting, enabling increased production and therefore prolongation of the crash. If any silver lining can be found in the roiling storm clouds of the current rout, it is that the pendulum could swing sharply back by decade’s end. Natural production declines in old fields and investment cutbacks and cancellations around the world will eventually register in market balances once record inventories are drawn down. However we are likely to see a bumpy rise in prices rather than a steady recovery such as after 2009. The effects in Canada of the current rout are not Alberta’s alone to suffer, largely owing to the dominance of the resource, labour and technology-intensive oil sands business. Unlike 30 years ago, the oil sands industry is far more enmeshed with a broad spectrum of ancillary industries and activities across the country. Back then, the oil sands made up just 17 per cent of Canada’s total production of crude oil; today, that figure is over 62 per cent. The impacts of the oil sands industry thus are pan-Canadian. The lesson for Alberta and Canada is to rely less on oil-price projections from the seers in the U.S. financial business, who are transfixed by the shale oil industry. Shale oil’s already weak financial viability is even more precarious as the U.S. Federal Reserve ponders a rate hike. This crash was triggered by the over-supply from U.S. shale and Canadian oil sands and the response by Saudi Arabia to hold its market share and to not cut its production. It behoves Canada and Alberta to be more pro-active and pay closer and critical attention to OPEC and in particular to signals from the Gulf producers.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.071
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0110.022
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.160
GPT teacher head0.380
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2015
Admission routes2
Has abstractyes

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