Data and Code for: Product Innovation, Product Diversification, and Firm Growth: Evidence from Japan’s Early Industrialization
Bibliographic record
Abstract
We explore how firms grow by adding products. We leverage detailed data from Japan’s cotton spinning industry at the turn of the last century to do so. This setting allows us to fully characterize the type of differentiation (vertical or horizontal) of new product introductions as well as whether the product is within or outside of the firm’s prior technological capabilities. We find that trying to introduce innovative products beyond the firm’s previous technologically feasible set, even if such trials fail, is a key to firm growth. Indeed, it mostly facilitates growth through the firm’s later success in horizontal product diversification. In long-term outcomes, the right tail of the firm size distribution becomes dominated by firms first moved into technologically challenging products and then later applied their newly acquired technical competence to horizontal expansion of their product portfolios. Two mechanisms through which this knowledge transfer occurs are greater production system flexibility and higher product appeal to downstream buyers.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".