DISCRIMINATING BETWEEN TARIFF BILL-BASED THEORIES OF THE STOCK MARKET CRASH OF 1929 USING EVENT STUDY DATA
Bibliographic record
Abstract
Jude Wanniski (1978) argued that the Smoot-Hawley Tariff Bill was a key factor in the Stock Market Crash of October 1929 and the Great Depression. The specter of higher tariffs and lower foreign trade, he argued, depressed share prices, leading ultimately to the Stock Market Crash. Bernard Beaudreau (1996, 2005), on the other hand, made the reverse argument, namely that the specter of higher tariffs from November 1928 to October 1929 fueled the Stock Market Boom as investors anticipated higher revenues and profits from the anticipated increase in sales and revenues. The Stock Market Crash, he argued, came on the heels of the defeat of the Thomas Recommittal Plan which foretold of lower, not higher as Wanniski contended, tariffs on manufactures. Using Event Study data from January 14, 1929 to October 29, 1929, this paper attempts to discriminate between these two hypotheses. The results show that “good” tariff bill news as reported in the New York Times contributed to stock price appreciation, and vice-versa, supporting the latter theory.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".