Oil Price Shocks and the Macroeconomy: What Has Been Learned Since 1996
Bibliographic record
Abstract
This paper reports on developments in theoretical and empirical understanding of the macroeconomic consequences of oil price shocks since 1996, when the U.S. Department of Energy sponsored a workshop summarizing the state of understanding of the subject. Four major insights stand out. First, theoretical and empirical analyses point to intra- and intersectoral reallocations in response to shocks, generating asymmetric impacts for oil price increases and decreases. Second, the division of responsibility for post-oil-price shock recessions between monetary policy and oil price shocks, has leaned heavily toward oil price shocks. Third, parametric statistical techniques have identified a stable, nonlinear, relationship between oil price shocks and GDP from the late 1940s through the third quarter of 2001. Fourth, the magnitude of effect of an oil price shock on GDP, derived from impulse response functions of oil price shocks in the GDP equation of a VAR, is around -0.05 and -0.06 as an elasticity, spread over two years, where the shock threshold is a price change exceeding a three-year high.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".