{"id":"W3199115473","doi":"10.3982/ecta14603","title":"What Do Data on Millions of U.S. Workers Reveal About Lifecycle Earnings Dynamics?","year":2021,"lang":"en","type":"article","venue":"Econometrica","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":278,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Kurtosis; Earnings; Skewness; Economics; Econometrics; Percentile; Nonparametric statistics; Panel data; Distribution (mathematics); Statistics; Mathematics; 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":[0.001202791,0.0002024255,0.00037952,0.001198616,0.0002687223,0.000914716,0.0004078547,0.0007697366,0.001369813],"category_scores_gemma":[0.0111974,0.0002484925,0.0002746753,0.002186632,0.0003153843,0.001619263,0.0004830636,0.0006957103,0.0005844652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003115328,"about_ca_system_score_gemma":0.000290457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009885202,"about_ca_topic_score_gemma":0.01609843,"domain_scores_codex":[0.9996752,0.00008618311,0.00002913807,0.00007283952,0.00007772353,0.00005889376],"domain_scores_gemma":[0.9910416,0.003436874,0.003947355,0.0006995416,0.000557593,0.0003169866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007008263,0.00007290152,0.9651163,0.00005034613,0.000106649,0.00006827009,0.0003139784,0.003191598,0.000260885,0.001912813,0.003145961,0.02569024],"study_design_scores_gemma":[0.00001240958,0.00007178366,0.9736396,0.00007188429,0.00005330191,0.000116136,0.0006764452,0.01254832,0.0003652553,0.004471027,0.007939818,0.00003409312],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797279,0.002027007,0.002883165,0.002431268,0.00003749548,0.00001420704,0.01065669,0.00003216608,0.00219003],"genre_scores_gemma":[0.98708,0.001744796,0.001156665,0.0002861412,0.0001121653,0.00001784311,0.008715233,0.00000848148,0.0008787617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009885202,"threshold_uncertainty_score":0.01965529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04133422580063242,"score_gpt":0.2437742070355905,"score_spread":0.2024399812349581,"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."}}