{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001988668,0.0002344929,0.0004062399,0.0006448265,0.0004534891,0.0009400621,0.000589012,0.000783223,0.002576751],"category_scores_gemma":[0.006990599,0.0001603968,0.0007738976,0.0007546404,0.0005128693,0.0004898424,0.001327107,0.001739167,0.0004497053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007562869,"about_ca_system_score_gemma":0.0006802807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03781331,"about_ca_topic_score_gemma":0.04615188,"domain_scores_codex":[0.9989727,0.0003786654,0.00005585118,0.0001049471,0.0001593655,0.0003285723],"domain_scores_gemma":[0.9904469,0.003931667,0.003975391,0.0002212526,0.0004493881,0.0009753479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001178053,0.0003430992,0.985207,0.00004210084,0.0001431566,0.0003175304,0.0002096074,0.003789387,0.0005754278,0.0002453407,0.001335668,0.006613546],"study_design_scores_gemma":[0.00001716794,0.0002830586,0.9959156,0.00001184576,0.0000448884,0.00003753872,0.0004034737,0.002374741,0.0002805378,0.00008915,0.0005327595,0.00000925708],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970446,0.0001586579,0.00008637172,0.0005290576,0.00001606745,0.00001228563,0.001350176,0.000006100203,0.0007966955],"genre_scores_gemma":[0.9977891,0.00009102472,0.00005310469,0.0001083497,0.0000352617,0.0000114965,0.001342397,0.000001832764,0.0005675311],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03781331,"threshold_uncertainty_score":0.07518643,"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."}}