THE IMPACT OF TRADE POLICY REGIMES ON FIRMS' LEARNING FOR INNOVATION FROM SUPPLIERS
Bibliographic record
Abstract
Using data on the textile-based Mauritius export processing zone (MEPZ) collected just prior to the demise of the preferential trade agreement (PTA) with Europe, we compare the acquisition and absorption of innovative technological capabilities (ITCs) of domestic firms and Asian-owned subsidiaries through domestic, Asian and European-based supplier linkages. Our results show that there are significant differences in learning for innovation from suppliers by MEPZ domestic firms and Asian-owned subsidiaries. The study firstly reveals that domestic supplier firms in a developing sub-Saharan African country like Mauritius can be an important source of ITCs to both domestic firms and foreign subsidiaries. Secondly, despite the presence of appropriate policies and institutions, Asian-owned subsidiaries did not fully harness learning opportunities and absorb acquired ITCs through supplier linkages in order to create new technology locally. We conjecture that their learning strategy was dictated by their foreseen exit from the MEPZ due to the anticipated end of the PTA. Thirdly, domestic firms exhibited a higher commitment to the acquisition and absorption of ITCs through supplier linkages and to the development of local ITCs. We infer that their learning strategy is a consequence of their need to continue to thrive and expand post-PTA.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".