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Record W832564660

How Oil-Price Shocks Affect Producers and Consumers

2014· article· en· W832564660 on OpenAlexaboutno aff
Ryan Kellogg

Bibliographic record

VenueEconstor (Econstor) · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsBrent CrudeOil-storage tradeOil priceEconomicsBarrel (horology)West Texas IntermediateCrude oilVolatility (finance)RecessionPetroleumCrack spreadMonetary economicsFinancial economicsKeynesian economicsGeologyPetroleum engineeringEngineeringPaleontology
DOInot available

Abstract

fetched live from OpenAlex

Markets for crude oil have been characterized by multiple episodes of volatility over the past 20 years. The price of Brent crude oil, an international crude oil benchmark priced in the North Sea, varied from a low of about $10 per barrel (bbl) in 1999 to a peak of more than $ 140/bbl in 2008, before falling again during the Great Recession. While the Brent price stabilized around $110/bbl during 2010-13, it recently and suddenly collapsed to around $50/bbl. The majority of these oil price swings have been attributed to global demand shocks such as the Great Recession, though the price drop this past autumn has not yet been extensively studied. (1) The accompanying figure shows the price both of Brent light and West Texas Intermediate (WTI) crude oil, which is priced in Cushing, Oklahoma. Historically, the WTI and Brent crude oil prices tracked each other extremely closely. However, beginning in 2011 these two price series diverged substantially, with WTI sometimes falling more than $20/bbl below Brent. This gap has recently closed substantially, but not entirely. In a series of papers, my co-authors and I have studied how shocks to crude oil markets affect oil producers and consumers. We have addressed questions such as How do oil drilling and production respond to oil price shocks?, Is oil price volatility itself important?, and How do consumers forecast future price changes? This research summary briefly describes these papers and notes issues where future research is needed. The Cushing Oil Glut In a recent project, Severin Borenstein and I studied the divergence between WTI and Brent oil prices that began in 2011. (2) This divergence arose from the confluence of a dramatic increase in unconventional crude oil production in Alberta, North Dakota, and West Texas and a lack of sufficient pipeline capacity to transport this new crude oil to Gulf Coast refineries. These factors led to a of oil at Cushing, Oklahoma, depressing the WTI price relative to the price of international crude oil. Our paper focuses on whether this decrease in the WTI price passed through to regional gasoline and diesel prices. [GRAPHIC OMITTED] Using data from the Energy Information Administration (EIA) on wholesale refined product markets, we find that gasoline and diesel prices in the Midwest, including Oklahoma, did not decrease at all in response to the glut of crude oil at Cushing. This lack of response is explained by the fact that, even though crude oil pipeline capacity was constrained after 2011, refined-product pipeline capacity was not. Thus, the marginal barrels of gasoline and diesel in the Midwest were, and still are, imported from the Gulf Coast, where they are refined using high-cost internationally-procured crude oil. These results imply that increases in crude oil production in the central U.S. did not lead to benefits for local consumers in the form of lower gasoline prices. Instead, Midwest refiners profited from the large spread between Midwest prices for crude oil and refined products. Since the publication of our paper, a series of significant pipeline investments has substantially decreased the spread between WTI and Brent oil prices. As our paper predicted, the relative increase in the WTI price has not passed through to Midwest refined product prices. Still, the WTI-Brent price wedge has not completely closed, owing to the U.S. ban on crude oil exports and to the fact that shale oil from North Dakota and West Texas is relative to imported crude. Because most U.S. Gulf Coast refineries are designed to handle heavy crude oil, they only purchase light crude oil at a discount, creating a differential relative to the international price. This situation presents a clear need for research into the economics of lifting the U.S. crude oil export ban, including a detailed analysis of how changes in light vs. heavy crude oil use by refineries would affect U. …

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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)0.001
metaresearch head score (Gemma)0.003
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.204
Teacher spread0.190 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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