How Industrial Design Matters for Firm Growth at Different Stages of Development: Evidence from Korea, 1970s to 2010s
Bibliographic record
Abstract
The traditional literature on the role of intellectual property rights (IPR) in innovation highlights the strength of IPR protection in the context of the tradeoff between innovation and diffusion. More recent literature analyzes the role of diverse forms of IPR in promoting innovation and growth and delves into not only regular patents but also utility models (or petite patents) and trademarks. Using firm‐level IPR (patents, designs and trademarks) data from Korea, we further extend this new strand of literature to explore the role of designs at different stages of development. The data spans five decades and can be divided into three subperiods that represent different stages of economic development. We find that design‐intensive sectors tend to be more export oriented. Further, firms’ sales growth is significantly associated with the design intensity of firms. Such association is found only during the later stages of economic development in Korea. Taken together with earlier studies, our findings imply that different forms of IPR, in particular designs, matter differently for innovation and firm performance at different stages of development. Designs are not that important in the early stages of development when economic growth relies on the mass production of low‐cost goods by low‐wage workers. The importance of design rises with economic development at later stages when product differentiation becomes critical. A unique and smart appearance increases value in the eye of the customer value and, thus, could help firms’ sales performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".