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Record W2592897075 · doi:10.3138/cpp.2016-070

Multinationals and Reallocation: Productivity Growth in the Canadian Manufacturing Sector, 2001–2010

2017· article· en· W2592897075 on OpenAlexaffvenueabout
Wulong Gu, Jiang Li

Bibliographic record

VenueCanadian Public Policy · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsGlobal Affairs CanadaStatistics Canada
Fundersnot available
KeywordsProductivityRestructuringLabour economicsManufacturing sectorBusinessCapital (architecture)Foreign capitalForeign direct investmentEconomicsManufacturingEconomic growthMacroeconomics

Abstract

fetched live from OpenAlex

The article examines the role of multinationals and reallocation in productivity growth in the Canadian manufacturing sector in 2001–2010. It finds that foreign-controlled enterprises were more important than domestically controlled enterprises in overall labour productivity growth for 2001–2010, but the contribution of foreign-controlled enterprises declined after 2006 as a result of an increase in the exits of large and productive foreign-controlled firms during that period. Restructuring in the manufacturing sector intensified after 2006. During 2006–2010, there was an increase in reallocation to enterprises that are more productive and an increase in reallocation of labour to industries that are more capital and intermediate input intensive. The effect of new enterprises displacing exitors also increased after 2006, mostly because of the increased effect of domestic entrants displacing exitors while the effect of foreign entry and exit declined. Offsetting those positive effects of reallocation on labour productivity growth is the negative effect of reallocation of labour to the firms with lower relative capital and intermediate intensities within the same industries. Finally, the article finds that the decline in labour productivity growth after 2006 was partly due to a decline in the productivity contribution of foreign-controlled enterprises as a result of an increase in the exits of large and productive foreign-controlled firms during that period.

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)0.004
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 score0.069
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.009
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.107
GPT teacher head0.240
Teacher spread0.133 · 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

Citations3
Published2017
Admission routes3
Has abstractyes

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