On ‘Rusting’ Money Silvio Gesell’s Schwundgeld Reconsidered. Part II: The Long Run
Bibliographic record
Abstract
Silvio Gesell argued that ‘rusting’ money is economically and socially beneficial; that claim has often been contended. In Part II of the paper, I concentrate on the long-run implications of his ideas. I show that introducing money depreciation in isolation may be economically non-beneficial in typical long-run equilibrium. But money depreciation, when coupled with expansionary monetary policy, is a necessary condition for a positive Mundell-Tobin effect on long-run real variables and so creates wealth in the model. It is found that this also holds in the transition to the long-run equilibrium. Hence, the spirit of Gesell’s hypotheses can be verified for a plausible, long-run environment as well, and may, thus, be relevant for long-run economic policy problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".