Is the Discretionary Income Effect of Oil Price Shocks a Hoax?
Bibliographic record
Abstract
The transmission of oil price shocks has been a question of central interest in macroeconomics since the 1970s. There has been renewed interest in this question since the large and persistent fall in the real price of oil in 2014-16. In the context of this debate, Ramey (2017) makes the striking claim that the existing literature on the transmission of oil price shocks is fundamentally confused about the question of how to quantify the effect of oil price shocks. In particular, she asserts that the discretionary income effect on private consumption, which plays a central role in contemporary accounts of the transmission of oil price shocks to the U.S. economy, makes no economic sense and has no economic foundation. Ramey suggests that the literature has too often confused the terms-of-trade effect with this discretionary income effect, and she makes the case that the effects of the oil price decline of 2014-16 on private consumption are smaller for a multitude of reasons than suggested by empirical models of the discretionary income effect. We review the main arguments in Ramey (2017) and show that none of her claims hold up to scrutiny. Our analysis highlights the theoretical basis of the discretionary income effect. We also discuss improved regression-based estimates of this effect that allow for changes in the dependence on oil and gasoline imports, and we highlight the fact that alternative estimates used by policymakers involve strong simplifying assumptions.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".