Technology Transfer and Early Industrial Development: Evidence From the Sino-Soviet Alliance
Bibliographic record
Abstract
This paper studies the long-term effects of technology and know-how transfers on structural transformations. In the 1950s, the Soviet Union supported the construction of 156 Projects, largescale capital-intensive industrial clusters in China, and sponsored a physical capital transfer providing state-of-the-art machinery and equipment; and a know-how transfer through training for engineers and production supervisors. We use newly-assembled data that follow these plants for over four decades, combined with natural variation in the transfers they eventually received. We find that know-how transfer had permanent effects on output quantity and quality, increased domestic technology development, and exports to the Western world when China engaged in international trade. By contrast, receiving only Soviet capital goods had smaller effects that faded out over time, especially after China's opening to trade. The intervention generated horizontal and vertical spillovers, as well as production reallocation from state-owned to privately owned companies since the late 1990s.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".