Good versus Bad Deflation: Lessons from the Gold Standard Era
Bibliographic record
Abstract
INTRODUCTION Concerns expressed by the Federal Open Market Committee at its meeting in May 2003 that the “balance of risks in the U.S. had shifted in favor of deflation”; similar concerns raised by an International Monetary Fund (IMF) report on deflation (2002) over the risk of deflation in Europe, especially Germany and Switzerland; and the experience of declining price levels in China and Japan have sparked new interest in the subject of deflation. In this paper, we examine the issue from a historical perspective. We focus on the experience of deflation in the late nineteenth century, when most of the countries of the world adhered to the classical gold standard. The period 1880–1914 was characterized by two decades of secular deflation followed by two decades of secular inflation. The price level experience of the pre-1914 period has considerable resonance for recent concerns over the possibility of deflation's reemergence. Four elements of the earlier experience are relevant to today's environment: (1) Deflation was relatively low (1%–3% in most countries); (2) productivity advance was rapid; (3) the real economy was growing; and (4) the price level was anchored by a credible nominal anchor—adherence to gold convertibility. Deflation has had a bad rap. Possibly as a consequence of the combination of deflation and depression in the 1930s, deflation is associated with (for some, it connotes) depression. In contrast, a basic tenet of monetary theory—the Friedman rule—suggests that deflation (albeit perfectly anticipated) is an outcome of optimal monetary policy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| 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".