{"id":"W3123368100","doi":"","title":"Minimum Wage Increases and Individual Employment Trajectories","year":2018,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Earnings; Counterfactual thinking; Minimum wage; Economics; Demographic economics; Labour economics; Wage; Hourly wage; Wage growth; Workforce; Baseline (sea); Margin (machine learning); Cohort; Quarter (Canadian coin); Matching (statistics); Medicine; Psychology; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009024777,0.0001112949,0.000180645,0.0008121023,0.0003700164,0.0006994545,0.0003828749,0.0003859534,0.001976446],"category_scores_gemma":[0.005672798,0.0002019728,0.0002608292,0.0009242275,0.0001468208,0.0004619025,0.0006225487,0.0006589207,0.0004353425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004201947,"about_ca_system_score_gemma":0.0003211878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01621652,"about_ca_topic_score_gemma":0.02335125,"domain_scores_codex":[0.999613,0.0001001265,0.00003214692,0.0001206971,0.00007030922,0.00006379098],"domain_scores_gemma":[0.9973567,0.0006762138,0.001056751,0.0002814763,0.0003184979,0.0003104489],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001550826,0.00009767549,0.9861072,0.00001376811,0.0000764744,0.00004197583,0.0004156748,0.00199045,0.0001974603,0.0006067347,0.0007343675,0.009563057],"study_design_scores_gemma":[0.000006175613,0.00006765706,0.9944738,0.00001815406,0.00001509092,0.00005061788,0.0003798813,0.00292711,0.000101291,0.0006138152,0.001337173,0.000009290155],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956114,0.0001033372,0.0005521515,0.0001478145,0.000005546695,0.00001085441,0.002672106,0.00001416925,0.0008826563],"genre_scores_gemma":[0.9961724,0.00007593642,0.0003909993,0.0000174938,0.000003879964,0.00001963745,0.002552144,0.000004740716,0.0007626981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01621652,"threshold_uncertainty_score":0.03224427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3669686520625505,"score_gpt":0.5086960938845214,"score_spread":0.1417274418219709,"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."}}