The resilience of the British and European goods industry: Challenge of Brexit
Bibliographic record
Abstract
Abstract This is a study of the volume flexibility of the British and European goods industry, and its relative ability to cope with exogenous shocks, using the case of the Brexit process in a comparative context. It is located within the literature on comparative capitalism, and what it tells us in terms of how different institutional orders may be equipped to deal with such events. Using data for goods firms across 27 EU countries and the UK, we find that the UK goods industry has coped poorly with the shocks related to the Brexit process: its volume flexibility has declined. Brexit also has had an, albeit lesser, impact on the volume flexibility of their European firms counterparts. In particular, smaller firms in the EU coped better, a possible reflection of stronger institutional supports. However, firms that investing more in R&D, provide training to improve management efficiency, and apply innovation to improve asset efficiency, seem to be coping better. This study illustrates how the withdrawal of Britain from supra-national European institutions seems to have accentuated any negative effects of domestic institutions on firms, and, indeed, has had even worse consequences than the 2008 economic crisis for the British goods industry. The latter would suggest it is ill equipped to cope with further shocks, such as the 2020 pandemic. We draw out the implications for theorizing, policy and future research.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".