KIBS spillovers in the knowledge production function of manufacturing and services sub-sectors at the USMCA countries
Bibliographic record
Abstract
In this paper, the knowledge production function is estimated by regressing R&D knowledge spillovers, capital investment and labor productivity on innovation generation, variable measured as filled patents, in the manufacturing and services sectors of each one of the three countries that integrate the USMCA Agreement (United States-Mexico-Canada Agreement). The results state that direct and indirect spillovers of KIBS R&D expenses, reinforced by capital investment and labor productivity measures, have a significative influence on the number of patents filled by each country over the years analyzed on those sub-sectors of the USMCA economy. That positive and causal effect generates knowledge production value chains from KIBS consultancies to the manufacturing and the services subsectors and backwards therefore reinforcing the value creation system of those services; also, by promoting the development of new goods and services by innovating in the production or administrative processes of the enterprises immerse on those subsectors (the embodied effect). The conclusions could serve as basis for the analysis of the sectorial policies that have to be stipulated by the conjunction of the politicians of the three countries, in the institutions already been established by the USMCA agreement in recent years. Keywords: KIBS, I-O direct and indirect impacts, USMCA, knowledge spillovers, manufacturing and service sectors, patents, innovation, knowledge production function.
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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 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".