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Record W3122689279

Gender wage-productivity differentials and global integration in China

2013· preprint· en· W3122689279 on OpenAlexfundno aff
Ana C. Dammert, Beyza Ural Marchand, Chi Wan

Bibliographic record

VenueEconstor (Econstor) · 2013
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaSoutheast University
KeywordsProductivityWageChinaLabour economicsEfficiency wageEconomicsLow wagePolitical scienceEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

In the absence of discrimination, there should be no wage-productivity differentials as relative wages should be equal to the relative marginal productivity levels of workers. This paper investigates the role of globalization on the structure and evolution of gender differentials in China by simultaneously estimating demand-side wage and productivity outcomes using nonlinear least squares. The analyses are based on a comprehensive population-wide panel survey of manufacturing firms between the years of 2004 and 2007, covering 94 percent of total industry output and providing an accurate representation of labor demand. The results suggest that more exposure to globalization through increased exports is associated with lower gender wage-productivity differentials, and more exposure through increased foreign investment leads to differentials in favor of female workers. On the other hand, gender discrimination is found to be prevalent among domestically owned and non-exporting firms.

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)0.001
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.228
Teacher spread0.182 · 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
Published2013
Admission routes1
Has abstractyes

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