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Record W3090599213 · doi:10.1111/caje.12655

Globalization, recruitments, and job mobility

2023· article· en· W3090599213 on OpenAlexvenueno aff
Carl Davidson, Fredrik Heyman, Steven J. Matusz, Fredrik Sjöholm, Susan Chun Zhu

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersMarcus och Amalia Wallenbergs minnesfondJohan och Jakob Söderbergs stiftelseTorsten Söderbergs Stiftelse
KeywordsOpenness to experienceGlobalizationBusinessLabour economicsJob marketEconomicsMarket economyWork (physics)Psychology

Abstract

fetched live from OpenAlex

Abstract Previous research indicates that firms pay a premium to poach workers from exporting firms if experience working for an internationally engaged firm reduces trade costs. Because international experience is less valuable to non‐exporters, we would expect to see differences in recruitments between firms that are internationally engaged and those that serve only the domestic market. Moreover, increased openness might lead to higher job‐to‐job mobility if more globalization raises both the share of exporters and the number of workers with skills that make them attractive for other exporters. Using linked Swedish employer–employee data for the period 1997 to 2013, we find systematic differences between the way exporters and non‐exporters recruit workers: exporters have a relatively high share of recruitments from other exporters as hypothesized. We also find some suggestive evidence that increased openness correlates positively with upward mobility for occupations that play a major role in international commerce, such as professionals and managers.

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)0.007
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 score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.305
GPT teacher head0.200
Teacher spread0.105 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

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