{"id":"W1544043993","doi":"10.3386/w14017","title":"Understanding International Price Differences Using Barcode Data","year":2008,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Chicago; National Science Foundation","keywords":"Stylized fact; Purchasing power parity; Barcode; Replicate; Law of one price; Economics; Econometrics; Market segmentation; Price level; Price setting; Database transaction; Financial economics; Microeconomics; Business; Monetary economics; Mid price; Computer science; Macroeconomics; Marketing; Statistics; Database; Mathematics","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":[0.002460526,0.0002546366,0.0004033158,0.006261141,0.0008308764,0.00301462,0.0007762678,0.000732768,0.003783634],"category_scores_gemma":[0.03443572,0.0002065877,0.0003203838,0.01477942,0.000957902,0.003360831,0.001306519,0.001227717,0.0009450985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002604851,"about_ca_system_score_gemma":0.001488765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1779801,"about_ca_topic_score_gemma":0.1195572,"domain_scores_codex":[0.9971836,0.0004437583,0.0001755099,0.0004061183,0.001451824,0.0003392432],"domain_scores_gemma":[0.9803956,0.006161059,0.00587609,0.002159774,0.004924748,0.000482727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002764291,0.0002013999,0.6396325,0.0003203323,0.0001781607,0.0004639654,0.006196621,0.01281126,0.002142435,0.1919975,0.02155105,0.1242284],"study_design_scores_gemma":[0.000037588,0.0000938931,0.8030897,0.0002270583,0.00007567698,0.000228473,0.004350623,0.04096589,0.002937623,0.07692429,0.07092679,0.0001423311],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8633775,0.001269495,0.04645981,0.001755838,0.0001196942,0.0001228216,0.01820074,0.000426409,0.06826771],"genre_scores_gemma":[0.9807447,0.0004412856,0.009044555,0.0001579815,0.00004705969,0.00003954057,0.008204624,0.00004753918,0.001272607],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1779801,"threshold_uncertainty_score":0.3538883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9177518783124495,"score_gpt":0.508567577587602,"score_spread":0.4091843007248476,"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."}}