How Does Protectionism Impact Multinational Firm Reshoring? Evidence from the UK
Bibliographic record
Abstract
Abstract There is growing interest in the nature and possible extent of de-globalization. This paper explores the impact that protectionist measures have on multinational enterprise (MNE) reshoring back to the UK. Besides taking into account the global trends indicating a return to protectionism, the existing literature highlights various firm-level and structural country-level determinants of reshoring decisions. We test a conceptual model with parent-subsidiary firm-level data for the period 2009 to 2017. We conclude that firms that are more sensitive to wage costs in their overseas subsidiaries were more likely to reshore. We did not find that more capital-intensive firms had a higher propensity to reshore. We find that our results are mostly driven from UK MNEs with subsidiaries in EU. This result has clear implications for a potential Brexit effect. Theoretically, we base our findings in transactional cost economics to help explain why different types of firms behave in the way they do, and why different types of firms may respond in quite different ways to the same mix of institutions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".