{"id":"W2951193963","doi":"10.48550/arxiv.1510.02754","title":"How universal is the law of income distribution? Cross country comparison","year":2015,"lang":"en","type":"preprint","venue":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Distribution (mathematics); Per capita income; Economics; Demographic economics; Per capita; Population; Personal income; Income distribution; Econometrics; Order (exchange); Geography; Mathematics; Demography; Economic growth; Sociology; Mathematical analysis; Finance","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":[0.001996285,0.0001232824,0.0004669956,0.00127246,0.0003074006,0.001598277,0.0005213984,0.0003668957,0.002140862],"category_scores_gemma":[0.009160244,0.00009448775,0.0003454287,0.001319868,0.001448566,0.001772705,0.0006342832,0.0005791744,0.0002179165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072983,"about_ca_system_score_gemma":0.0003802331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01896558,"about_ca_topic_score_gemma":0.007875212,"domain_scores_codex":[0.9993154,0.0002284549,0.00002613618,0.0002189046,0.00009122727,0.0001199368],"domain_scores_gemma":[0.996333,0.001760093,0.0005700226,0.000765646,0.0004298587,0.0001413616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003122132,0.0001150934,0.7219336,0.0001691096,0.0008502505,0.0009681755,0.003137517,0.03216577,0.001319684,0.1560405,0.003504796,0.07948326],"study_design_scores_gemma":[0.00001928249,0.0001033016,0.8719508,0.00009820214,0.0001519046,0.0004481191,0.004121217,0.03870907,0.0009107692,0.07271918,0.01069957,0.00006866371],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767646,0.0008602855,0.007946324,0.0009839297,0.00002920498,0.00001708746,0.0005487045,0.00005245223,0.0127973],"genre_scores_gemma":[0.9987987,0.0002456481,0.0004528745,0.00004860148,0.000007104918,0.000004197826,0.0001752231,0.000009142247,0.0002584468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01896558,"threshold_uncertainty_score":0.03771037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03557520779546515,"score_gpt":0.2257235483516552,"score_spread":0.1901483405561901,"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."}}