{"id":"W2959598611","doi":"10.1111/rode.12607","title":"The interaction effect of gender and ethnicity in loan approval: A Bayesian estimation with data from a laboratory field experiment","year":2019,"lang":"en","type":"article","venue":"Review of Development Economics","topic":"Microfinance and Financial Inclusion","field":"Economics, Econometrics and Finance","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department for International Development; Department for International Development, UK Government; International Development Research Centre; Government of Canada","keywords":"Microfinance; Loan; Ethnic group; Empowerment; Indigenous; Financial inclusion; Demographic economics; Economics; Population; Financial services; Economic growth; Finance; Political science; Sociology; Demography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.04912787,0.0008178452,0.002106271,0.0006208157,0.001186828,0.001489676,0.001890951,0.001648091,0.00664936],"category_scores_gemma":[0.07091987,0.0006746417,0.002466124,0.0006451649,0.002144417,0.001375974,0.001082085,0.00227274,0.0007263903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001389072,"about_ca_system_score_gemma":0.00121336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02248187,"about_ca_topic_score_gemma":0.0124098,"domain_scores_codex":[0.9791799,0.01777017,0.0005108803,0.001510104,0.0005334711,0.0004954805],"domain_scores_gemma":[0.7048667,0.2708708,0.01078615,0.00958156,0.003213252,0.0006815311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02556375,0.01771884,0.559942,0.002112271,0.005968699,0.001406425,0.008323655,0.132328,0.01172001,0.05159565,0.007257667,0.1760631],"study_design_scores_gemma":[0.005050595,0.01667446,0.3759282,0.0004127498,0.006115106,0.0004185645,0.003310011,0.5333061,0.006137878,0.04320345,0.009010389,0.0004325841],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.944175,0.0003507881,0.05149464,0.000292623,0.0000432323,0.0008976566,0.0006942056,0.00007215804,0.001979581],"genre_scores_gemma":[0.9688455,0.0001593971,0.0262149,0.0001931179,0.00002765642,0.00147122,0.0008220182,0.00002292762,0.00224338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04912787,"threshold_uncertainty_score":0.2598161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02949016855909804,"score_gpt":0.2701034259645514,"score_spread":0.2406132574054534,"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."}}