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Record W19354446 · doi:10.1186/s40173-015-0050-9

Minimum wages and employment in China

2015· article· en· W19354446 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

VenueIZA Journal of Labor Policy · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsUniversity of TorontoMemorial University of Newfoundland
FundersFundamental Research Funds for the Central UniversitiesSocial Sciences and Humanities Research Council of CanadaRenmin University of ChinaFudan UniversityInternational Development Research CentreNanyang Technological UniversityCentral University of Finance and EconomicsNational Natural Science Foundation of ChinaTulane University
KeywordsMinimum wageChinaEconomicsPanel dataWageDemographic economicsSocial policySurvey data collectionWage growthLabour economicsGeographyEconometrics

Abstract

fetched live from OpenAlex

Abstract Since China promulgated new minimum wage regulations in 2004, the frequency and magnitude of changes in minimum wages have been substantial. This paper uses county-level minimum wage data combined with urban household survey micro-dataset from 16 representative provinces as a merged county-level panel to estimate the employment effects of minimum wage changes in China over the 2002–2009 period. In contrast to the mixed results reported by previous studies using provincial-level data, we present evidence that minimum wage changes led to significant adverse effects on employment in the Eastern and Central regions of China, and resulted in disemployment for females, young adults, and low-skilled workers. JEL classifications: J38

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 score0.337
Threshold uncertainty score0.994

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 head0.020
GPT teacher head0.331
Teacher spread0.311 · 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