Far from the Tree? Do Private Entrepreneurs Agglomerate Around Public Sector Incumbents During Economic Transition?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While it is well known that state enterprises in transition economies were displaced by private enterprises at a macro level, little is known about whether private entrepreneurs emerged in a way that helped preserve or shift preexisting agglomerations of industrial activity at a microgeographic level. To address this question, we integrate competing perspectives on the role of large, bureaucratic incumbents in spawning entrepreneurs. We conceptualize a trade-off between two countervailing effects of large incumbents on potential entrepreneurs: bureaucratic socialization and exposure to capabilities. This yields novel predictions about how different kinds of startups agglomerate around different kinds of incumbents. We test these predictions using fine-grained geographic data on founding rates by private entrepreneurs in China’s bicycle manufacturing industry. Consistent with our theorized trade-off, we find evidence of a nonmonotonic effect of incumbent size on local founding rates by private entrepreneurs. Additional moderating effects are consistent with boundary conditions on the hypothesized mechanisms. Our results provide the first empirical investigation of the extent to which entrepreneurial activity agglomerated around public sector incumbents during economic transition. We discuss how these insights add to the understanding of economic transition as well as how the context of economic transition adds to the understanding of entrepreneurial spawning.
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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 | 0.000 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it