MétaCan
Menu
Back to cohort
Record W2921495958 · doi:10.1111/twec.12793

Access to imported intermediates and intra‐firm wage inequality

2019· article· en· W2921495958 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWorld Economy · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsProductivityWage inequalityWageEconomicsInequalityLabour economicsWage dispersionInvestment (military)Control (management)Monetary economicsEfficiency wageMacroeconomics

Abstract

fetched live from OpenAlex

Abstract We use Chinese firm‐level data from the World Bank Investment Climate Survey to examine the link between importing intermediates and intra‐firm wage inequality. Our results show that intermediate input importers not only have a significant wage premium but also have a greater intra‐firm wage dispersion than non‐importing firms. This pattern is robust when we control for productivity and use trade costs as the instruments. We further investigate the mechanism of how importing intermediates might contribute to both inter‐firm and intra‐firm wage inequality. Our evidence is consistent with three important channels. First, imported intermediate inputs complement skilled labour. Second, intermediates importers are more likely to use performance pay. Third, imported inputs complement innovation and employee training.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.465
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.004

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 head0.053
GPT teacher head0.243
Teacher spread0.189 · 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