Economic policy uncertainty, R&D expenditures and innovation outputs
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
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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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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