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Record W286745821

Globalisation and Labour Productivity in the Malaysian Manufacturing Sector

2012· article· en· W286745821 on OpenAlexvenueno aff
Rahmah Ismail, Aliya Rosa, Noorasiah Sulaiman

Bibliographic record

VenueReview of Economics and Finance · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsGlobalizationProductivityOpenness to experienceForeign direct investmentManufacturingEconomicsPanel dataManufacturing sectorLabour economicsInvestment (military)BusinessEconomic growthMacroeconomicsMarket economyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Globalisation process has forced the Malaysian manufacturing sector to strengthen its ability to compete in the international market. Globalisation, coupled with advancement in information, communication and technology has increased the demand for quality labour, having knowledge and competing to maximise production. The objective of this paper is to analyse the depth of globalisation impact on labour productivity in the Malaysian manufacturing sector. The analysis has used data from the Manufacturing Industrial Survey, Department of Statistics Malaysia comprising 24 years, from 1985 to 2008 and selected six sub-industries. A multiple regression model using panel data is estimated to analyse the relationship between labour productivity using capital-labour ratio, number of labour, foreign direct investment (FDI), foreign labour, economic openness and technology. Findings of the study show that globalisation indicators like FDI and economic openness have negative and significant effect on labour productivity in the manufacturing sector. The dummy period after the year 1995 is positive and significant reflecting that the impact of globalisation on labour productivity in the Malaysian manufacturing sector is higher after the year 1995 as compared to the years prior to 1995.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.215
Teacher spread0.164 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2012
Admission routes1
Has abstractyes

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