Does Economic Policy Uncertainty Affect the Export Technological Sophistication of Manufacturing Industries
Bibliographic record
Abstract
Based on data from 19 major countries from 2000-2017, this paper examines the impact of economic policy uncertainty on the export technological sophistication of manufacturing industries. The research shows that in the sample period, the export technological sophistication of manufacturing industries varies among countries, with China and India slowly increasing, Germany and Japan still at a high level, and Canada and Greece in a downward trend. From the empirical results, the expected mechanism of economic policy uncertainty forces the domestic manufacturing industries industry to accelerate R&D innovation by restraining the "technological spillover" effect of imported intermediate goods and the "financing dependence" effect of domestic credit investment, thus promoting the increase of the export technological sophistication in various countries. For countries with high economic growth rate, high degree of development and high degree of economic freedom, the positive impact of economic policy uncertainty on the export technological sophistication of manufacturing industries is more significant. From the perspective of economic policy uncertainty, the paper examines its impact on the export technological sophistication of manufacturing industries with important policy implications. Strengthening bilateral and multilateral consultations among governments and accelerating R&D innovation of domestic enterprises are effective measures to enhance export competitiveness at present.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".