The Modified Baumol Equation: Theory and Evidence
Bibliographic record
Abstract
Baumol developed an equation of demand for money for the transaction motive. It is affected positively by cost per withdrawal and negatively by the interest loss resulting from holding cash. The present paper suggests modifying the basic and simplified Baumol approach by adding another element to the transaction equation. Availability of cash encourages spontaneous purchases resulting in customer losses. Through cost minimization with respect to three elements instead of two as in the original Baumol equation, a new modified Baumol equation was created. It is examined by using an empirical data set and the results support the modified version of the Baumol equation. Customers respond positively to cash availability when they spend more on luxury goods. This is prominent especially among unmarried and most likely young customers. Due to high-income elasticity, spontaneous purchasing is higher among wealthier customers and full-time workers who maintain a steady and secure employment position. Since such customers have a weakness for spontaneously and sometimes even carelessly buying luxury items, from their point of view they create a good and efficient buffer by decreasing the available cash in hand and thereby reducing or possibly even preventing their wasteful behavior. The new version is robust and statistically more significant than the original equation presented in 1952.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".