{"id":"W2940685465","doi":"10.17848/wp19-301","title":"Local Job Multipliers in the United States: Variation with Local Characteristics and with High-Tech Shocks","year":2019,"lang":"en","type":"report","venue":"","topic":"Regional Economics and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pew Charitable Trusts","keywords":"High tech; Quarter (Canadian coin); Economics; Shock (circulatory); Job creation; Demand shock; Econometrics; Population; Lagrange multiplier; Microeconomics; Labour economics; Mathematics; Geography; Mathematical optimization","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":[0.0009293978,0.0002146668,0.0002800801,0.001148605,0.0003344702,0.0008809575,0.000272376,0.0001889048,0.002534214],"category_scores_gemma":[0.002806194,0.0001868634,0.0002823532,0.00239205,0.0002813935,0.0004176168,0.0008344396,0.0003880032,0.000488419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006885926,"about_ca_system_score_gemma":0.0003406909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03774976,"about_ca_topic_score_gemma":0.0631962,"domain_scores_codex":[0.9996294,0.0001348371,0.000023396,0.00007649409,0.00006415491,0.00007175149],"domain_scores_gemma":[0.9971725,0.0009298893,0.0008983401,0.000183609,0.0004416885,0.0003740033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009587492,0.00005084493,0.9874134,0.00002604852,0.0001276064,0.00007606822,0.0002841013,0.004350306,0.0001541815,0.0004030154,0.001980034,0.005038579],"study_design_scores_gemma":[0.000003842924,0.0000175071,0.9966834,0.000008687279,0.0000164244,0.0000261064,0.0005331146,0.001464825,0.00008161375,0.0001722732,0.000984064,0.000008267299],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944252,0.0001607671,0.0004836613,0.00008227123,0.000006946208,0.000008798777,0.002589785,0.00002167222,0.002220899],"genre_scores_gemma":[0.9958619,0.0001036734,0.0001775552,0.00001547093,0.000006324485,0.00001276009,0.003000795,0.000009506779,0.0008120228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03774976,"threshold_uncertainty_score":0.07506007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0225430384181649,"score_gpt":0.2059198534787226,"score_spread":0.1833768150605577,"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."}}