Vintage Effects and the Diffusion of Time-Saving Technological Innovations
Bibliographic record
Abstract
Abstract An important aspect of the study of technological innovations is the explanation of the extent and pace of diffusion. We show that pooling data across vintages of a technology may result in misleading conclusions about the impact of key factors on the duration of time to adoption of the innovation. This is especially important for a technology that affects both product/service quality and a firm's costs of operation to different degrees as the technology evolves over time. Using data on the diffusion of point-of-sale optical scanners between 1974 and 1985, we find that factors such as the stock of prior adopters, household income, family size, the four-firm concentration ratio and item-pricing laws had predictably different effects on the diffusion rate depending on the vintage of the technology. These results are robust to controlling for unobserved heterogeneity among firms, inclusion of additional regressors and a change in functional form.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".