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 to 2004. Hedonic regressions indicate that prices are related to processor generation and clock speed, memory capacity, screen size and quality and the presence of a digital camera or wireless capability. A particularly salient feature of PDAs is portability, where we find: (i) purchasers value the energy density of the battery technology (e.g. lithium ion) rather than the battery life in hours; and (ii) the physical characteristics of the PDA (e.g. weight, volume) are nonlinearly related to price, suggesting that valuation of the physical form of PDAs does not bear a simple linear relationship to characteristics, either in absolute terms (‘smaller is better’) or vs. an ergonomic ‘sweet spot’. Rather, portability characteristics are correlated with other desirable attributes, making the relationship between price and portability difficult to disentangle. However, hedonic price indexes are robust across different measures of the portability of PDAs. Hedonic indexes using the dummy variable, characteristics prices, and imputation approaches decline on average between 19 and 26% per year. A matched model price index computed from a subset of observations declines at 19% per year, while a fixed-effects hedonic index declines at 14% per year.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".