{"id":"W2082908681","doi":"10.1162/rest.88.4.671","title":"Growth and Convergence across the United States: Evidence from County-Level Data","year":2006,"lang":"en","type":"article","venue":"The Review of Economics and Statistics","topic":"Fiscal Policy and Economic Growth","field":"Economics, Econometrics and Finance","cited_by":201,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Ordinary least squares; Instrumental variable; Convergence (economics); Economics; Econometrics; Real estate; Least-squares function approximation; Sample (material); Demographic economics; Mathematics; Statistics; Economic growth; Finance; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004745179,0.0002687472,0.0005790946,0.004405027,0.0006169236,0.001532352,0.0004800629,0.0003177211,0.001474544],"category_scores_gemma":[0.02637151,0.0002890388,0.0003972159,0.00946412,0.0009008667,0.0009755814,0.002103252,0.0007244038,0.0003898088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006028417,"about_ca_system_score_gemma":0.0007768568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0632886,"about_ca_topic_score_gemma":0.0546164,"domain_scores_codex":[0.9969323,0.001492253,0.0002646142,0.000536387,0.0005177078,0.000256709],"domain_scores_gemma":[0.9607251,0.0180087,0.009943295,0.003238012,0.006785476,0.001299341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000577728,0.00002660306,0.9854553,0.00003723875,0.0001434445,0.00007127412,0.0005193925,0.001057143,0.00002065772,0.001158717,0.002676985,0.008775479],"study_design_scores_gemma":[0.00001319386,0.00003253442,0.989754,0.0001061056,0.00008312812,0.00008814998,0.001392279,0.002616479,0.000112027,0.001032997,0.004754666,0.00001453045],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852733,0.003149546,0.001328392,0.0007554441,0.00003277668,0.00001777575,0.003969337,0.00005734581,0.005416009],"genre_scores_gemma":[0.9912187,0.001523669,0.0005954329,0.00009269657,0.00002934066,0.00002242508,0.006137715,0.00002284432,0.000357256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0632886,"threshold_uncertainty_score":0.1258404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1179656446041985,"score_gpt":0.288395463596842,"score_spread":0.1704298189926436,"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."}}