Oil discovery, energy transition, and the decline in wholesale prices during the Great Depression
Bibliographic record
Abstract
Statistical tests are used to examine the role of prices for petroleum, coal, and farm products in the Great Depression. A new empirical mapping of relationships between monthly energy and farm product prices and key macroeconomic variables shows how biophysical factors intersected with the price system in the 1930s US economy. Deflation was a critical feature of the Depression, with the US aggregate wholesale price index falling by 37 percentage points between October 1929 and February 1933. Petroleum product prices and farm product prices can explain 89% of changes to the aggregate wholesale price index over the 1930s. Granger causality tests show that petroleum product prices led changes to money supply in the 1930s, by 8 months, while farm product and all-commodities prices Granger caused changes to industrial production. Changes in prices from October 1929 to February 1933 varied substantially between commodities, with prices of coal, metals, and building materials-the essential ingredients for capital formation-all increasing in real terms. Real bituminous coal prices are found to Granger cause changes to money supply, personal income, and industrial production over the 1930s. Overall, the results add further support to the hypothesis that the Great Depression was caused by an energy transition, following discovery of large quantities of petroleum in the US Southwest. Supplementary Information: The online version of this article (doi:10.1111/jiec.13602) contains supplementary material, which is available to authorized users.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".