Estimating the influence of KIBS spillovers through the knowledge production function and the role of international trade
Bibliographic record
Abstract
A research gap exists regarding how research and development (R&D) expenditures contribute to the generation of innovative spillovers through knowledge-intensive business services (KIBS), particularly within the manufacturing and business services subsectors. These spillovers have a networking effect on innovation products in the manufacturing and services subsectors, as captured by the knowledge production function (KPF). The study proposes a two-stage model for analyzing the elasticities of production and economies of scale that arise from estimating singular knowledge production functions within a regional trade system. This model is tested for the case of the trade agreement of the United States, Mexico, and Canada (USMCA). The main results express that R&D expenditures embodied in KIBS spillovers have a significant effect on the generation of innovation outputs, in each of the USMCA countries and, reveal increasing returns to scale. Also, KPF elasticities, for the capital and the labor variables, proved to be inelastic and exhibited constant returns to scale. Additionally, the different estimates in each one of the USMCA countries expose diverse technologies of scale among the three countries.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".