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OFFSHORING AND PRODUCTIVITY: A MICRO‐DATA ANALYSIS

2010· article· en· W2032613015 on OpenAlex
Jianmin Tang, Henrique Do Livramento

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueReview of Income and Wealth · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsOffshoringProductivityBusinessForeign direct investmentDimension (graph theory)Industrial organizationInternational tradeProduction (economics)International businessInvestment (military)Survey data collectionEconomicsOutsourcingMarketingMicroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

Offshoring has become increasingly important for businesses, especially manufacturing firms, to compete in domestic and international markets. This paper empirically studies the association between offshoring, productivity, and plant characteristics by focusing on the geographical dimension of plants' business activities. Using data from Statistics Canada's Survey of Innovation 2005 and Annual Surveys of Manufacturers, we show that material offshoring is strongly associated with firms' outward‐oriented business activities (including foreign operation, investing in foreign M&E, and exporting), even after controlling for geographic advantages and industry‐ and plant‐specific effects. For R&D offshoring, we find that it is mainly associated with investment in foreign M&E. In addition, this paper shows that material offshoring is positively associated with productivity and that the association is significantly larger for material offshoring to Asia Pacific countries than for material offshoring to the U.S. and other locations.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.060
GPT teacher head0.268
Teacher spread0.209 · 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