{"id":"W2041103157","doi":"10.1111/j.1468-0084.2004.00086.x","title":"Calculating a Standard Error for the Gini Coefficient: Some Further Results*","year":2004,"lang":"en","type":"article","venue":"Oxford Bulletin of Economics and Statistics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Jackknife resampling; Standard error; Gini coefficient; Statistics; Mathematics; Regression; Econometrics; Ordinary least squares; Coefficient of determination; Inequality; Economic inequality; Mathematical analysis","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":[0.0008318227,0.0002052747,0.0005292689,0.00009129161,0.0002186491,0.00009024291,0.000207785,0.00009401217,0.0001582805],"category_scores_gemma":[0.0002595664,0.0001960913,0.0001147985,0.0000317877,0.0001863217,0.00005201123,0.00006243774,0.0001123816,0.00003700464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008915474,"about_ca_system_score_gemma":0.0000306757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006470979,"about_ca_topic_score_gemma":0.00007353787,"domain_scores_codex":[0.9981815,0.000007362396,0.00103552,0.0003786195,0.00001605004,0.0003809804],"domain_scores_gemma":[0.9984725,0.0004020989,0.0006634484,0.0003385674,0.00002214023,0.0001012818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002561993,0.00004270875,0.0003302049,0.00005142759,0.0001272521,8.274606e-7,0.0007587068,0.08461649,5.990526e-7,0.9072576,0.003281762,0.003276222],"study_design_scores_gemma":[0.003088479,0.0003643782,0.0009419729,0.00001874004,0.00002762673,0.000006598192,0.0002250291,0.08003941,0.00002395878,0.1532599,0.7616531,0.0003507605],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5820473,0.005455234,0.287315,0.03697265,0.00200677,0.002448199,0.067565,0.00006907357,0.0161208],"genre_scores_gemma":[0.9488967,0.002722162,0.04589735,0.001157561,0.0002560399,0.00003820214,0.0001151608,0.00005806876,0.0008587255],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7583714,"threshold_uncertainty_score":0.799637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05284684744851625,"score_gpt":0.240469288725196,"score_spread":0.1876224412766798,"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."}}