{"id":"W3123789533","doi":"","title":"Accounting for Wealth Concentration in the US","year":2020,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Earnings; Net worth; Economics; Net income; Distribution (mathematics); Capital income; Investment (military); National wealth; Labour economics; Capital (architecture); Rate of return; Earnings response coefficient; Price–earnings ratio; Monetary economics; Demographic economics; Earnings per share; Finance","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0008145095,0.0002854534,0.0003414537,0.002854248,0.0003495141,0.001347206,0.0002892378,0.0002453178,0.003144613],"category_scores_gemma":[0.006158511,0.0001746441,0.0002560872,0.004836536,0.0003018588,0.001157134,0.001145066,0.0006377627,0.0003357876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008848242,"about_ca_system_score_gemma":0.0006082591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04242425,"about_ca_topic_score_gemma":0.03168922,"domain_scores_codex":[0.9996164,0.0000920649,0.00003418178,0.00007992613,0.00009937582,0.00007803422],"domain_scores_gemma":[0.9971782,0.0006392883,0.00119171,0.000289127,0.0004555178,0.0002462162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005233643,0.00003926017,0.9259642,0.00003456249,0.0002520125,0.0002983569,0.0003076951,0.005139704,0.0002121405,0.01588302,0.008587726,0.0432289],"study_design_scores_gemma":[0.0000132754,0.00004511685,0.919796,0.0001426199,0.0001766632,0.0002981816,0.0006799946,0.0171915,0.0007054826,0.04024277,0.02067685,0.00003155972],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9538727,0.004901395,0.004387345,0.002681354,0.00007945694,0.00003597325,0.006758257,0.0001155711,0.02716792],"genre_scores_gemma":[0.9943025,0.001139656,0.0005924965,0.0001154214,0.00006907531,0.00001105668,0.002270576,0.00001086553,0.001488318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04242425,"threshold_uncertainty_score":0.08435464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07360169770245381,"score_gpt":0.3155897661485986,"score_spread":0.2419880684461448,"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."}}