Hedging against postage rate increases
Bibliographic record
Abstract
Canada Post issued the first permanent stamp on 16th November, 2006. The United States Postal Service soon followed by introducing the forever stamp on 12th April, 2007. The stamps were developed to ease the transition associated with increases in postal rates. The stamp price equals the one-ounce cost of first-class postage at the time of purchase. The stamp allows consumers to receive services equal to the value of a first-class postage stamp at the time of use. Permanent stamps are unique given their insensitivity to postal rate increases, thereby offering the potential to hedge against future postage rate increases. Moreover, postal services commonly pre-announce rate changes providing a several week notice before the change takes effect. The careful treasury manager conceivably can minimise mailing costs around postal increases by strategically purchasing permanent and forever stamps. Indeed, the enterprising entrepreneur might make a market by purchasing stamps prior to the rate change and selling them after for the new going rate. In this paper the authors develop a model to determine optimal stamp purchases around postal rate increases. The results indicate it is optimal for most individuals to purchase between a 90- and 730-day supply of permanent postage when a $0.01 increase in postage rates is imminent. From a policy perspective, the results imply that postal authorities should monitor permanent stamp purchase patterns to determine the optimal timing and magnitude of postal rate increases.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".