Faster, Smaller, Cheaper: An Hedonic Price Analysis of PDAs
Bibliographic record
Abstract
We compute quality-adjusted price indexes for Personal Digital Assistants (PDAs) for the period 1999-2004, using data on prices and characteristics of 203 models sold by 12 manufacturers.The PDA market is growing in size, it is technologically dynamic with very substantial changes in measured characteristics over time, and it has experienced rapid rates of product introduction.Hedonic regressions consistently show prices to be positively related to processor performance, RAM memory, permanent storage capacity, and battery life, as well as several measures of screen size and quality.Features such as networking, biometric identification, camera, and cellphone capability are also positively associated with price.Hedonic price indexes implied by these regressions decline at an AAGR of 21.1% to 25.6% per year during this period.A matched model price index computed from a subset of observations declines at 18.75% per year.Though these PDA rates of price decline are lower than have been estimated for desktop and laptop PCs, consumers in this "ultra-portable" segment of the computer market appear to have enjoyed substantial welfare gains over the past five years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".