{"id":"W4318618489","doi":"10.1007/s41027-023-00428-7","title":"Effect of COVID-19 Pandemic on Employment and Earning in Urban India during the First Three Months of Pandemic Period: An Analysis with Unit-Level Data of Periodic Labour Force Survey","year":2023,"lang":"en","type":"article","venue":"Indian Journal of Labour Economics","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Unemployment; Pandemic; Livelihood; Urbanization; Earnings; Informal sector; Notice; Poverty; Government (linguistics); Coronavirus disease 2019 (COVID-19); Demographic economics; Recession; Pace; Economic growth; Socioeconomics; Economics; Geography; Business; Political science; Medicine; Agriculture; Finance","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.007979969,0.000310882,0.001449674,0.00185311,0.0001485233,0.00007405529,0.001075596,0.000188308,0.00006072072],"category_scores_gemma":[0.001462231,0.0002691324,0.0001591656,0.001247706,0.0002576408,0.0005042087,0.0002304403,0.0005387138,0.000003666094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004199305,"about_ca_system_score_gemma":0.0004563293,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003300729,"about_ca_topic_score_gemma":0.01792052,"domain_scores_codex":[0.9969886,0.0002331344,0.00176165,0.000461727,0.0001104999,0.0004444062],"domain_scores_gemma":[0.994575,0.001321811,0.002858398,0.0009023201,0.00006582852,0.0002766603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006724481,0.0000446561,0.9611958,0.0002696454,0.0006010248,0.0000262483,0.00530219,0.03131618,0.00001669933,0.0001829195,0.000003200852,0.0003690278],"study_design_scores_gemma":[0.002883079,0.0008866086,0.9928757,0.0001024435,0.00008515394,0.00005192446,0.0006095581,0.001729506,0.00005443542,0.0002520237,0.0002164474,0.0002531336],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958743,0.000447019,0.00007950576,0.0001526008,0.00009375157,0.0003345189,0.002996751,0.00001134303,0.00001024375],"genre_scores_gemma":[0.9988242,0.0008315613,0.00004164685,0.000104793,0.00005152581,0.000005596183,0.00004806732,0.00004551945,0.00004709025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03167992,"threshold_uncertainty_score":0.9999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07751406686666505,"score_gpt":0.2980843374515309,"score_spread":0.2205702705848658,"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."}}