{"id":"W2982486692","doi":"10.1108/ijm-10-2018-0361","title":"Minimum wage impacts on wages, employment and hours in China","year":2019,"lang":"en","type":"article","venue":"International Journal of Manpower","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Earnings; Wage; Propensity score matching; Economics; Demographic economics; Labour economics; Population; China; Demography; Medicine; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008489673,0.00009928431,0.0002431258,0.0003359401,0.000009890273,0.00007760293,0.0002577888,0.00005218126,0.0004032808],"category_scores_gemma":[0.0001030031,0.00009180511,0.00008149543,0.00006143907,0.00001673322,0.0002142603,0.00005675669,0.0001958241,0.00006420446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520408,"about_ca_system_score_gemma":0.00001657335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009650089,"about_ca_topic_score_gemma":0.00001980637,"domain_scores_codex":[0.9990039,0.00001748994,0.0005815411,0.0001499274,0.0001090562,0.0001380947],"domain_scores_gemma":[0.9992636,0.00005787898,0.0004394111,0.0001158256,0.00005788879,0.00006535575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003260098,0.0003257758,0.8823189,0.000008630956,0.000164397,0.0001568108,0.000546905,0.0001929047,0.00009282041,0.1130943,0.0005888208,0.002183788],"study_design_scores_gemma":[0.001614626,0.000218015,0.9583596,0.00009613115,0.000002287269,0.00003130505,0.00003593427,0.000270845,0.00003468334,0.02974659,0.009435618,0.0001543509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848111,0.0003750817,0.00006023453,0.002175227,0.001532349,0.00006711577,0.00005702506,0.000003111127,0.01091876],"genre_scores_gemma":[0.9983881,0.0003628252,0.00009933187,0.0004751189,0.0001151393,7.455931e-7,0.000003543947,0.00001202859,0.0005431527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08334768,"threshold_uncertainty_score":0.4415642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01257115974932383,"score_gpt":0.2454180405722944,"score_spread":0.2328468808229706,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}