Economic policy uncertainty, R&D expenditures and innovation outputs
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the relationship between the news-based economic policy uncertainty (EPU), research and development (R&D) expenditures per capita and innovation outputs. Design/methodology/approach Data from 1996 to 2015 for 19 countries (Australia, Brazil, Canada, Chile, China, France, Germany, India, Ireland, Italy, Japan, Netherlands, Russia, Singapore, South Korea, Spain, Sweden, the United Kingdom and the United States) are used. The authors apply country and year fixed-effects models for the estimations. Findings The study findings show that higher levels of EPU are positively associated with higher R&D expenditures per capita as well as innovation outputs (patent applications, patent grants and trademark applications). Practical implications This study deepens our understanding on the policy uncertainty–economic activities nexus and expands the literature on uncertainty, which is still at an initial phase of development, leading to generate a variety of open research questions for further investigation and study (Bloom, 2014). Originality/value There has not been an empirical investigation on the links between EPU and R&D expenditures and innovation outputs across several countries. The authors address this gap in the literature.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".