The Role of Returnee-Entrepreneurs in Cluster Emergence: The Case of Shanghai’s Semiconductor-Design Industry
Bibliographic record
Abstract
While industry clusters are an important part of the global economy, our understanding of the factors that lead to their emergence is still lacking. A recent stream of research suggests that returnee-entrepreneurs can dramatically accelerate the emergence of high-tech clusters in developing economies (Hsu 1997; Saxenian and Hsu 2001; Saxenian 2002, 2006). But that is only one factor important to cluster emergence; this stream of research has not examined how another factor — competition with MNCs — might alter the effects returnee-entrepreneurs have on cluster emergence. To fill this literature gap, I focus on the competition for experienced engineers between returnee firms and MNCs in one newly emergent cluster. Drawing on 50 interviews in Shanghai’s semiconductor-design industry, I found that returnee-entrepreneurs played an important role in stimulating cluster emergence by transferring knowledge to local engineers. I also discovered that, as the cluster develops, the en masse entry of MNCs increases competition for experienced engineers at the expense of returnee firms. The results of this study have important implications for our understanding of high-tech cluster emergence and of the role of returnee firms in fostering indigenous firms’ technological upgrading.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".