Measurement and Difference Analysis of Total Factor Productivity of Strategic Emerging Enterprises in China
Bibliographic record
Abstract
Improving the total factor productivity of strategic emerging enterprises is of great significance for promoting the optimization and upgrading of the industrial structure and achieving high-quality economic development. Based on the data of 2,760 strategic emerging enterprises of China a-share listed companies from 2014 to 2016, this study uses Levinsohn and Pertrin(LP) method to measure the total factor productivity of China's strategic emerging enterprises, and analyzes regional differences in total factor productivity of strategic emerging enterprises. The results showed that during the sample study period, the total factor productivity of China's strategic emerging enterprises decreases first and then increases. From the perspective of different regions, the total factor productivity of strategic emerging enterprises in the four regions showed a "stepwise distribution", the total factor productivity of strategic emerging enterprises in the East, Central and Western regions showed a downward trend, and the total factor productivity of strategic emerging enterprises in the central region showed a downward trend and then an upward trend. From the perspective of regional heterogeneity, Beijing, Shanghai, Guangdong, Shandong, Jiangsu and other provinces have higher total factor productivity of strategic emerging enterprises. However, the total factor productivity of strategic emerging enterprises in Xinjiang, Gansu, Guangxi, Yunnan, Shaanxi and other provinces and cities is low. In order to promote the total factor productivity of strategic emerging enterprises, we can increase R&D investment, link financing constraint and pay attention to regional development differences.
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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 | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".