{"id":"W4282002757","doi":"10.1257/pandp.20221009","title":"Early Withdrawal of Pandemic Unemployment Insurance: Effects on Employment and Earnings","year":2022,"lang":"en","type":"article","venue":"AEA Papers and Proceedings","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Unemployment; Receipt; Earnings; Coronavirus disease 2019 (COVID-19); Margin (machine learning); Pandemic; Duration (music); Demographic economics; Economics; Entitlement (fair division); Medicine; Finance; Internal medicine; Accounting","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":[0.0004999663,0.0001802712,0.0003732656,0.0001812938,0.0002199226,0.00004164021,0.0001233659,0.0000588788,0.0000721628],"category_scores_gemma":[0.0001053901,0.0001945574,0.00005038221,0.0002171163,0.00007275197,0.0001225132,0.0001514216,0.000257534,0.000007169515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144646,"about_ca_system_score_gemma":0.00001840745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003559049,"about_ca_topic_score_gemma":0.000002017596,"domain_scores_codex":[0.9988322,0.000007310657,0.0003492606,0.0004287008,0.00009731961,0.0002851978],"domain_scores_gemma":[0.9993731,0.0001047044,0.0002965586,0.00009018683,0.00001782559,0.0001176478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001006561,0.00003703302,0.9865354,0.0001132521,0.00004257884,0.000001842564,0.003522857,0.000008316922,0.001234404,0.004801921,0.0002982544,0.003303505],"study_design_scores_gemma":[0.001514691,0.001284185,0.9626483,0.00004996083,0.00001081202,0.00001449367,0.0001884293,0.0000505768,0.0003216884,0.00157418,0.03206213,0.0002805957],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937801,0.0008801947,0.000001985309,0.0004984829,0.0001255286,0.0003109328,0.00002970156,0.00004009562,0.004333018],"genre_scores_gemma":[0.9979134,0.0003221947,0.00003439069,0.0009689789,0.00004086285,0.00007039986,0.000002491806,0.00002687369,0.0006204486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03176387,"threshold_uncertainty_score":0.7933819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630733522399162,"score_gpt":0.2247790024217604,"score_spread":0.2084716671977687,"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."}}