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Record W2791383886

Multinationals and Reallocation: Productivity Growth in the Canadian Manufacturing Sector

2017· article· en· W2791383886 on OpenAlexaboutno aff
Wulong Gu, Jiang Li

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsManufacturing sectorProductivityFellFinancial crisisEconomicsTotal factor productivityLabour economicsMonetary economicsBusinessInternational economicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Output growth in Canadian manufacturing was slower in the 2000s than in the 1990s. The sector?s real output declined, in contrast to an overall increase in output in the business sector (Clarke and Couture 2017). It fell rapidly during the 2007-to-2009 financial crisis, and returned to its pre-crisis level only in 2016. The market share of foreign-controlled firms also declined after 2000 (Baldwin and Li 2017). This paper examines the role of multinationals and reallocation in productivity growth in the Canadian manufacturing sector for the period from 2001 to 2010, a period of significant change in this sector. It contributes to the literature on several fronts. First, it complements the literature by examining productivity growth at the firm level. This paper also seeks to examine whether the decline that started around 2006 was associated with changes in the effect of reallocation and the role of foreign multinationals in aggregate productivity growth.

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 score0.051
Threshold uncertainty score0.369

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.059
GPT teacher head0.210
Teacher spread0.151 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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