Holding Cash and Spontaneous Behavior: A Modification of the Baumol Equation
Bibliographic record
Abstract
During the decades following the presentation of the original Baumol equation (1952) the role of holding cash was significantly changed. The original Baumol equation considered the two elements of (i) the value of transactions, positively affecting cash holding; and (ii) the interest rate, negatively affecting cash holding. A third element that was not considered is the economic behavioral aspect of the availability of money that may lead to spontaneous purchasing. This element reduces the inclination of customers towards holding cash.The present paper develops various kinds of loss functions due to spontaneous purchasing behavior and presents several different modified Baumol equations that are more reliable and realistic than the original Baumol equation.An important implication of our paper relates to the ineffectiveness of monetary policy. When the interest rate is very low, in the original Baumol model we approach the liquidity trap range in which monetary policy is ineffective. However, according to our new model the monetary policy still remains effective, even at low or zero interest rates. This is the case even in an environment in which the monetary policy seems to be totally inefficient, as we recently find in several industrial countries throughout the world. In some sense, this reminds us of the idea of an automatic stabilizer that supports fiscal policies. The new modified Baumol equation in the current paper reveals an automatic stabilizer which accelerates the effectiveness of monetary policy, and avoids the phenomenon of the liquidity trap, even in cases of zero interest rates.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".