Using Patents and Publications to Assess R&D Efficiency in the States of the USA
Bibliographic record
Abstract
Even with the USA spending the largest amounts in R&D, its share in total patent grants worldwide has been declining. This decline is also evident in its share of world scientific publications. These developments have been termed by some as the ‘‘American Paradox”. Extant research on R&D efficiency and technological innovation capability has considered the USA as a homogeneous entity and has not focused at the sub-national level. This paper analyses the R&D efficiency of 50 US states and the District of Columbia. R&D efficiency is calculated as the ratio of patents granted and scientific publications to R&D expenditures. Only 14 states out of the 51 regions are found to exhibit positive changes in R&D efficiency between 2004 and 2008. Comparing this performance with that of the BRICS nations over the same period we find that Brazil, India, China and South Korea show significant improvements in R&D efficiency with India taking the lead. This research identifies the states in the US with the highest R&D efficiency and presents benchmarks which can be followed by policy interventions. The paper highlights the importance of conducting analyses of R&D efficiency using patents and publications at the sub-national level for informed policy making.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".