A Modified Baumol Approach-Optimal Withdrawal and Holding of Cash Liquid Assets
Bibliographic record
Abstract
<p>Baumol developed an equation for the transaction demand for money. It is affected positively by cost per withdrawal and transaction value, and negatively by the interest loss from holding cash.</p><p>Our objective is to modify the Baumol equation by including another factor. The demand for money is also affected by the customer concern that holding a more available liquid asset encourages more spontaneous purchases with resulting losses in their real value. We develop a new theoretical model by adding to the original Baumol cash demand equation another demand for a deposit which has positive yield and is less liquid. Holding this deposit restrains some of the spontaneous purchases. This modified Baumol equation leads to the following new results: Customers withdraw cash more often; maintain, on average, a smaller cash balance and larger amount of less liquid assets; and reduce their spontaneous and “nonrational” purchases.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".