{"attempt":1,"datasets":{"jtrain":{"description_shown":"Wooldridge's JTRAIN data (Holzer, Block, Cheatham and Knott's Michigan job-training survey; R package wooldridge): one row per firm and year, 157 firms over 1987-1989 (471 rows); scrap rates exist for a subset of firms. Columns: rownames, a row number; year; fcode, firm code; employ, employees; sales, annual sales (dollars); avgsal, average employee salary; scrap, scrap rate (per 100 items); rework, rework rate; tothrs, total hours of training; union, 1 if unionized; grant, 1 if the firm received a job-training grant in that year; d88 and d89, 1 for 1988 and 1989; totrain, total employees trained; hrsemp, training hours per employee; lscrap, log of scrap; lemploy, lsales, lrework, lhrsemp, logs of those columns; lscrap_1, lscrap lagged one year; grant_1, grant lagged one year; clscrap, cgrant, clemploy, clsales, lavgsal, clavgsal, cgrant_1, chrsemp, clhrsemp, first differences and logs of the named columns.","file_sha256":"2820079b04c953a3555a3722e4b14430bc9efedfd6870715a863717de8177383","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"","question_sha256":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85","seq":0,"skills":[],"task_id":"paper_repro.wooldridge_jtrain.ex14_1_grant.tool","task_sha256":"e0867e8d724019590bbbdd82f64a5b5a56761c5b4d80ba28d0de08c7348142e0","timestamp":"2026-10-15T16:59:23.549534Z"}
{"arguments":{"cluster":null,"controls":["d88","d89"],"dataset":"jtrain","fe":["fcode"],"intercept":true,"level":0.95,"se":"classical","subset":null,"weights":null,"x":["grant","grant_1"],"y":"lscrap"},"attempt":1,"cached":false,"call_id":"call_c9948620","card_version":"0.1.0","code_version":"62cdbe0860ad","dataset_digests":{"jtrain":"ce556c6a511400be9674bea971d3ba4cff3c7e1149b038c7ebbcf69c15dab73c"},"decided_by":"code","duration_ms":6.371082970872521,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"2a301add420256aea97d88a72ddcef3475327b754cec7ce3e67dde5593fd536c","llm_name":null,"output_digest":"ecd5a997c99a315b570f3b8ea54bff89d84ae9839d66820acc004be4ee22f745","rationale":"","replay_class":"replayed","result":{"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent and have the same variance for every observation"],"call_id":"call_c9948620","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":162},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":58},{"flag":false,"name":"r2","note":"","value":0.9275719385145765},{"flag":false,"name":"df_resid","note":"","value":104},{"flag":false,"name":"se_type","note":"classical (homoskedastic) standard errors","value":"classical"},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null},{"flag":false,"name":"fixed_effects","note":"absorbed as dummies; their coefficients are not tabulated","value":"fcode (54 levels)"}],"figures":[{"ci_high":0.04638807456669097,"ci_level":0.95,"ci_low":-0.5510178221962718,"derivation":{"note_pt":"","steps":[{"formula":"","result":-0.2523148738147904,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":162.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":58.0}],"text_pt":"Coeficiente de grant na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de lscrap, efeitos fixos de fcode.","unit":""},{"formula":"ep = \u221aV_jj","result":0.1506289943545013,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.022689093940248387}],"text_pt":"Erro-padr\u00e3o cl\u00e1ssico (homoced\u00e1stico): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":-0.2523148738147904},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9830375264837259},{"note":"","symbol":"ep","unit":"","value":0.1506289943545013}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 104 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.grant","label":"Coefficient on grant in the regression of lscrap","se":0.1506289943545013,"unit":"","value":-0.2523148738147904},{"ci_high":-0.004755091021796887,"ci_level":0.95,"ci_low":-0.8384239260366337,"derivation":{"note_pt":"","steps":[{"formula":"","result":-0.42158950852921534,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":162.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":58.0}],"text_pt":"Coeficiente de grant_1 na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de lscrap, efeitos fixos de fcode.","unit":""},{"formula":"ep = \u221aV_jj","result":0.21019996441849445,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.04418402504153633}],"text_pt":"Erro-padr\u00e3o cl\u00e1ssico (homoced\u00e1stico): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":-0.42158950852921534},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9830375264837259},{"note":"","symbol":"ep","unit":"","value":0.21019996441849445}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 104 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.grant_1","label":"Coefficient on grant_1 in the regression of lscrap","se":0.21019996441849445,"unit":"","value":-0.42158950852921534}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":-2.2384743154419002,"ci_low":-3.4131636211165306,"coef":-2.8258189682792154,"p":7.148679426290452e-16,"se":0.29618433589543847,"t":-9.540744144136003,"term":"Intercept"},{"ci_high":0.04638807456669097,"ci_low":-0.5510178221962718,"coef":-0.2523148738147904,"p":0.09692391893977843,"se":0.1506289943545013,"t":-1.6750750736672522,"term":"grant"},{"ci_high":-0.004755091021796887,"ci_low":-0.8384239260366337,"coef":-0.42158950852921534,"p":0.04748974265972461,"se":0.21019996441849445,"t":-2.00565927637294,"term":"grant_1"},{"ci_high":0.1368776115185822,"ci_low":-0.29730896145103164,"coef":-0.08021567496622473,"p":0.46537157858748945,"se":0.1094751277197217,"t":-0.732729676932581,"term":"d88"},{"ci_high":0.016974077007711197,"ci_low":-0.5113796650806335,"coef":-0.24720279403646112,"p":0.06633915572683147,"se":0.1332182913918953,"t":-1.8556220129655585,"term":"d89"}]},"tool":"infer.ols","version":"0.1.0","warnings":["309 rows were dropped because one of 'lscrap', 'grant', 'grant_1', 'd88', 'd89', 'fcode' was missing"]},"result_for_model_ref":null,"result_sha256":"a05f790ef5753d55eba98696c3ae9c71f75a0c775222fd950ff70ff1e5608c75","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85","seq":1,"synthetic":false,"task_id":"paper_repro.wooldridge_jtrain.ex14_1_grant.tool","timestamp":"2026-10-15T16:59:23.549534Z","tool_id":"infer.ols","turn_id":null}
{"attempt":1,"check":{"check_index":0,"detail":"-0.2523148738 vs -0.25231487381477113","expected":-0.25231487381477113,"failure_class":null,"kind":"figure_close","observed":-0.2523148738147904,"passed":true,"required":true,"source_id":"wooldridge_jtrain.ex14_1_grant.software","tolerance":{"abs_tol":1.5062899435450078e-07,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-06,"rule":null},"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85","seq":2,"task_id":"paper_repro.wooldridge_jtrain.ex14_1_grant.tool","timestamp":"2026-10-15T16:59:23.556074Z"}
{"attempt":1,"check":{"check_index":1,"detail":"0.1506289944 vs 0.15062899435450078","expected":0.15062899435450078,"failure_class":null,"kind":"figure_close","observed":0.1506289943545013,"passed":true,"required":true,"source_id":"wooldridge_jtrain.ex14_1_grant.software","tolerance":{"abs_tol":1.5062899435450078e-07,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":1e-06,"rule":null},"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85","seq":3,"task_id":"paper_repro.wooldridge_jtrain.ex14_1_grant.tool","timestamp":"2026-10-15T16:59:23.556074Z"}
{"attempt":1,"check":{"check_index":2,"detail":"-0.2523148738 vs -0.252","expected":-0.252,"failure_class":null,"kind":"figure_close","observed":-0.2523148738147904,"passed":true,"required":false,"source_id":"wooldridge_jtrain.ex14_1_grant.paper","tolerance":{"abs_tol":0.000500001,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":null,"rule":"published_cell d=3"},"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85","seq":4,"task_id":"paper_repro.wooldridge_jtrain.ex14_1_grant.tool","timestamp":"2026-10-15T16:59:23.556074Z"}
{"attempt":1,"check":{"check_index":3,"detail":"0.1506289944 vs 0.151","expected":0.151,"failure_class":null,"kind":"figure_close","observed":0.1506289943545013,"passed":true,"required":false,"source_id":"wooldridge_jtrain.ex14_1_grant.paper","tolerance":{"abs_tol":0.000500001,"interval":null,"min_lre":null,"open_interval":false,"rel_tol":null,"rule":"published_cell d=3 (the SE as printed)"},"trace_call_id":null,"weight":1.0},"epoch":1,"event":"score","run_id":"20261015T165911Z-suite-paper_repro-baseline-8b4f85","seq":5,"task_id":"paper_repro.wooldridge_jtrain.ex14_1_grant.tool","timestamp":"2026-10-15T16:59:23.556074Z"}