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Record W2489416979

The resilience of the Canadian textile industries and clusters to shocks, 2001-2013

2016· preprint· en· W2489416979 on OpenAlexaboutno aff
Kristian Behrens, Brahim Boualam, Julien Martin

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic geographyPsychological resilienceProductivityResilience (materials science)Cluster (spacecraft)BusinessCluster analysisShock (circulatory)RevenueNatural resource economicsEconomicsEconomic growthComputer science
DOInot available

Abstract

fetched live from OpenAlex

Understanding and assessing the role played by geographical clusters in the resilience of industries' and firms' to adverse economic shocks is important to inform policy and to devise regional development strategies. Yet, surprisingly little is known about that topic. This report aims to fill this gap. To this end, we first use recent microgeographic techniques to measure the degree of clustering in the Canadian textile and clothing (T&C) industry, and to detect geographical clusters of plants. We then dissect the changes in that industry (exit of plants, employment changes, productivity, industry switching, and geographical relocation) between 2001 and 2013. The T&C industry is geographically strongly clustered and subject to large industry-specific shocks (the end of the Multi Fibre Arrangement; mfa) during our study period, thus providing an ideal laboratory to examine the role of geographical clusters for resilience. We find a very limited impact of the initial level of clustering on subsequent changes in either industry-level employment, productivity, or revenue. Using detailed geocoded plant-level data, we further find that plants in clusters were more likely to exit than plants that were not part of a cluster and they downsized their employment more than non-clustered plants. These results suggest that clusters need not make industries or plants more resilient to adverse economic shocks. Furthermore, there is a composition effect of clusters. In the T&C industry, clusters contain larger plants that react to shocks by exiting or downsizing. In this respect, clusters were actually less resilient to shocks in the sense of providing local employment stability, which is usually the key concern for local policy makers. Plants in clusters were, however, more likely to switch into different industries following the end of the mfa. This suggests that being part of a cluster may help surviving plants to adapt in the event of a negative shock.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.045
GPT teacher head0.281
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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