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Record W1592410172 · doi:10.1108/ijm-03-2013-0047

Occupational segregation and the gender earnings gap in China: devils in the details

2015· article· en· W1592410172 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.

Bibliographic record

VenueInternational Journal of Manpower · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOccupational segregationEarningsWageChinaDistribution (mathematics)Demographic economicsCensusOccupational prestigeEconomicsPsychologyLabour economicsDemographyGeographySocioeconomic statusSociologyPopulation

Abstract

fetched live from OpenAlex

Purpose – The purpose of this paper is to analyze the gender earnings gap in China with a focus on the role of differences in the occupational distribution of males and females. Design/methodology/approach – The authors use a procedure to model occupational attainments and decompose differences in earnings into an inter-occupational portion due to differences in the occupational distribution between males and females, and an intra-occupational portion due to differences in pay. The analysis is based on Chinese census data. Findings – The authors find that the male-female pay gap is virtually completely explained by wage discrimination defined as females being paid less than males within the occupation groups based on six broad occupations. Occupational segregation explains virtually none of the overall male-female pay gap, and in fact the “segregation” slightly favors women. However, the picture changes substantially when the analysis is conducted at the more disaggregate sub-occupation level within each of the six broad groups. Wage discrimination remains the prominent contributor to the pay gap across the disaggregated sub-occupations in each of the broad occupations. But there is considerable heterogeneity in the effect of occupational discrimination within the sub-occupations within the different broad occupational groups. Social implications – When females have the same occupation-determining characteristics as men, they are in lower paying sub-occupations within the professional group and to a lesser extent within manufacturing and operations jobs. There is considerable heterogeneity in the effect of occupational discrimination within the sub-occupations in the different broad occupational groups. Originality/value – The paper systematically examines the degree to which the gender earnings gap in China is due to the differences in occupational distributions of males and females, highlighting that the conventional Blinder-Oaxaca decompositions can under- or over- estimate the unexplained portion of the gender pay gap by controlling or not controlling for differences in the occupational distribution of males and females. The paper also shows that previous studies that have examined occupational segregation across aggregate occupational groups can mask important differences in the effect of occupational discrimination within the sub-occupations in the different broad occupational groups.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.172
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
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 head0.052
GPT teacher head0.280
Teacher spread0.228 · 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