{"call_id":"call_e7f7d7cd","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.thornton_2008.tab4_c1.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{"tools":["calc__annuity","calc__breakeven","calc__combine","calc__discount","calc__fiscal_impact","core__atkinson","core__equivalent_variation","core__fgt","core__gini","core__indirect_incidence","core__kakwani","core__mvpf","core__palma","core__quantile_shares","core__reynolds_smolensky","core__scale_effect","core__theil","core__welfare_change","data__cite","data__describe","data__siconfi","data__sidra","evidence__pool_dl","infer__ate","infer__density_test","infer__did_2x2","infer__did_staggered","infer__event_study","infer__fe","infer__fisher_exact","infer__iv_2sls","infer__linear_hypothesis","infer__mde","infer__neyman","infer__ols","infer__power","infer__randomization_inference","infer__rdd","infer__sample_size","infer__twfe","infer__wald_late","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:57.583563Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":"# Role\nYou are MAGI's economist. A municipal analyst in Brazil asks a question; you answer it by choosing registered tools and their arguments. The tools compute every number. Your job is to recognise how the data were generated, pick the tool whose design matches, read what it reports, and say what the result means for the analyst.\n\n# Rules\n1. You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic: for a ratio, difference, sum, product, percent change or share of figures already computed, call calc.combine; to restate a log-point coefficient as a percent or an effect in standard deviations, call core.scale_effect.\n2. Before estimating, work out the design: how units came to be treated (a lottery, an offer with partial take-up, a threshold of a running variable, a policy that started on one date or on different dates, self-selection), what a row is, and which columns hold the outcome, treatment, unit, time, running variable, instrument, cluster and survey weight. Call data.describe when the columns are unclear. Then choose the tool whose \"Use when\" matches and heed its \"Avoid when\".\n3. Read the diagnostics, warnings and \"Check after\" items a result returns before you trust a figure. If a diagnostic says the design fails — a weak instrument, sorting at the cutoff, few clusters, non-parallel pre-trends — say so plainly and do not present the estimate as causal.\n4. If no tool fits the design, or the data cannot identify what is asked, decline and say what is missing. Never invent a design, a column or a number.\n5. A tool that errors or declines returns its reason; fix the arguments and call again, or choose another tool.\n6. Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale.\n7. Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"thornton\",\n \"rows\": 4820,\n \"description\": \"Thornton's (2008) Malawi HIV-results experiment, as distributed with 'Causal Inference: The Mixtape' (thornton_hiv.dta): one row per respondent of the 2004 survey (4,820 rows, 133 columns; most are interactions not needed here). Columns used by the paper's main tables: villnum, village identifier (incentives were randomized to individuals within villages); site, district (1 Mchinji, 2 Balaka, 3 Rumphi); rumphi and balaka, 1 for respondents in those districts; got, 1 if the respondent went to the results centre and learned the HIV test result; any, 1 if the respondent was offered any nonzero monetary incentive; tinc, the value of the incentive offered; distvct, distance from home to the assigned results centre (km); under and over, 1 if that distance is under or over 1.5 km; simaverage, simulated average distance; hiv2004, 1 if HIV positive (0 negative; -1 or missing when not tested); test2004, 1 if tested in 2004; male, 1 for men; age, in years; age2, age squared; MainSample, 1 for the paper's main analysis sample (tested, with demographic data) and missing otherwise.\",\n \"columns\": {\n  \"site\": \"float32\",\n  \"rumphi\": \"float32\",\n  \"balaka\": \"float32\",\n  \"villnum\": \"float64\",\n  \"m1out\": \"float32\",\n  \"m2out\": \"float32\",\n  \"survey2004\": \"float32\",\n  \"got\": \"float32\",\n  \"zone\": \"float32\",\n  \"distvct\": \"float32\",\n  \"tinc\": \"float32\",\n  \"Ti\": \"float32\",\n  \"any\": \"float32\",\n  \"under\": \"float32\",\n  \"over\": \"float32\",\n  \"simaverage\": \"float32\",\n  \"age\": \"float32\",\n  \"age2\": \"float32\",\n  \"male\": \"float32\",\n  \"mar\": \"float32\",\n  \"educ2004\": \"float32\",\n  \"timeshadsex_s\": \"float64\",\n  \"hadsex12\": \"float32\",\n  \"eversex\": \"float32\",\n  \"usecondom04\": \"float32\",\n  \"tb\": \"float32\",\n  \"thinktreat\": \"float32\",\n  \"a8\": \"float64\",\n  \"land2004\": \"float32\",\n  \"T_consentsti\": \"float64\",\n  \"T_consenthiv\": \"float64\",\n  \"T_final_trichresult\": \"float32\",\n  \"T_final_result_ct\": \"float32\",\n  \"T_final_result_gc\": \"float32\",\n  \"hiv2004\": \"float32\",\n  \"test2004\": \"float32\",\n  \"followup_tested\": \"float64\",\n  \"followupsurvey\": \"float32\",\n  \"havesex_fo\": \"float64\",\n  \"numsex_fo\": \"float64\",\n  \"likelihoodhiv_fo\": \"float64\",\n  \"numcond\": \"float32\",\n  \"anycond\": \"float32\",\n  \"bought\": \"float32\",\n  \"consentany2004\": \"float32\",\n  \"inter98\": \"float32\",\n  \"inter01\": \"float32\",\n  \"got_hadsex12\": \"float32\",\n  \"got_hiv_hadsex12\": \"float32\",\n  \"any_hiv_hadsex12\": \"float32\",\n  \"tinc_hiv_hadsex12\": \"float32\",\n  \"under_hiv_hadsex12\": \"float32\",\n  \"any_male_hadsex12\": \"float32\",\n  \"got_havesex_fo\": \"float32\",\n  \"got_hiv_havesex_fo\": \"float32\",\n  \"any_hiv_havesex_fo\": \"float32\",\n  \"tinc_hiv_havesex_fo\": \"float32\",\n  \"under_hiv_havesex_fo\": \"float32\",\n  \"any_male_havesex_fo\": \"float32\",\n  \"male_under\": \"float32\",\n  \"any_got\": \"float32\",\n  \"tinc_got\": \"float32\",\n  \"under_got\": \"float32\",\n  \"over_got\": \"float32\",\n  \"any_hiv2004\": \"float32\",\n  \"tinc_hiv2004\": \"float32\",\n  \"under_hiv2004\": \"float32\",\n  \"over_hiv2004\": \"float32\",\n  \"any_male\": \"float32\",\n  \"tinc_male\": \"float32\",\n  \"under_male\": \"float32\",\n  \"over_male\": \"float32\",\n  \"any_havesex_fo\": \"float32\",\n  \"tinc_havesex_fo\": \"float32\",\n  \"under_havesex_fo\": \"float32\",\n  \"over_havesex_fo\": \"float32\",\n  \"any_hadsex12\": \"float32\",\n  \"tinc_hadsex12\": \"float32\",\n  \"under_hadsex12\": \"float32\",\n  \"over_hadsex12\": \"float32\",\n  \"male_distvct\": \"float32\",\n  \"any_distvct_hiv\": \"float32\",\n  \"tinc_distvct_hiv\": \"float32\",\n  \"under_distvct_hiv\": \"float32\",\n  \"over_distvct_hiv\": \"float32\",\n  \"hiv_distvct\": \"float32\",\n  \"distvct_hiv\": \"float32\",\n  \"any_distvct_male\": \"float32\",\n  \"tinc_distvct_male\": \"float32\",\n  \"under_distvct_male\": \"float32\",\n  \"male_got\": \"float32\",\n  \"any_got_hiv\": \"float32\",\n  \"tinc_got_hiv\": \"float32\",\n  \"under_got_hiv\": \"float32\",\n  \"over_got_hiv\": \"float32\",\n  \"hiv_got\": \"float32\",\n  \"got_hiv\": \"float32\",\n  \"any_got_male\": \"float32\",\n  \"tinc_got_male\": \"float32\",\n  \"under_got_male\": \"float32\",\n  \"male_hadsex12\": \"float32\",\n  \"any_hadsex12_hiv\": \"float32\",\n  \"tinc_hadsex12_hiv\": \"float32\",\n  \"under_hadsex12_hiv\": \"float32\",\n  \"over_hadsex12_hiv\": \"float32\",\n  \"hiv_hadsex12\": \"float32\",\n  \"hadsex12_hiv\": \"float32\",\n  \"any_hadsex12_male\": \"float32\",\n  \"tinc_hadsex12_male\": \"float32\",\n  \"under_hadsex12_male\": \"float32\",\n  \"male_male\": \"float32\",\n  \"any_male_hiv\": \"float32\",\n  \"tinc_male_hiv\": \"float32\",\n  \"under_male_hiv\": \"float32\",\n  \"over_male_hiv\": \"float32\",\n  \"hiv_male\": \"float32\",\n  \"male_hiv\": \"float32\",\n  \"any_male_male\": \"float32\",\n  \"tinc_male_male\": \"float32\",\n  \"under_male_male\": \"float32\",\n  \"male_havesex_fo\": \"float32\",\n  \"any_havesex_fo_hiv\": \"float32\",\n  \"tinc_havesex_fo_hiv\": \"float32\",\n  \"under_havesex_fo_hiv\": \"float32\",\n  \"over_havesex_fo_hiv\": \"float32\",\n  \"hiv_havesex_fo\": \"float32\",\n  \"havesex_fo_hiv\": \"float32\",\n  \"any_havesex_fo_male\": \"float32\",\n  \"tinc_havesex_fo_male\": \"float32\",\n  \"under_havesex_fo_male\": \"float32\",\n  \"tinc_male_hadsex12\": \"float32\",\n  \"under_male_hadsex12\": \"float32\",\n  \"MainSample\": \"float32\"\n }\n}\n\n# Question\nThornton (2008), 'The Demand for, and Impact of, Learning HIV Status', Table 4, column (1): in the paper's main sample (respondents who were tested for HIV and have demographic data), regress attendance at the HIV results centre on the indicator for receiving any nonzero monetary incentive by ordinary least squares, controlling for being HIV positive, sex, age and age squared, and district fixed effects, with robust standard errors clustered by village. Report the coefficient on the any-incentive indicator.","response":"Table 4, column (1): OLS of attendance at the results centre on any incentive, HIV status, sex, age, age squared and district dummies, errors clustered by village, in the main sample (tested, with demographic data).\ninfer__ols({\"dataset\": \"thornton\", \"y\": \"got\", \"x\": [\"any\"], \"controls\": [\"hiv2004\", \"male\", \"age\", \"age2\", \"rumphi\", \"balaka\"], \"fe\": [], \"cluster\": \"villnum\", \"subset\": \"MainSample == 1\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_e7f7d7cd","text":"Table 4, column (1): OLS of attendance at the results centre on any incentive, HIV status, sex, age, age squared and district dummies, errors clustered by village, in the main sample (tested, with demographic data).","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"thornton","y":"got","x":["any"],"controls":["hiv2004","male","age","age2","rumphi","balaka"],"fe":[],"cluster":"villnum","subset":"MainSample == 1"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"56a7e1583701ed7f18e177cd1d9cc19cf5ba925904735eac800a986e5c939283","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["eacf25fe725954802e16dd08888adc6e9de0f5b84f9ad97921a3486b1141ec5b"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"reference","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.6258341018110514}}
{"call_id":"call_a7dd219b","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.thornton_2008.tab4_c1.agent/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"thornton","y":"got","x":["any"],"controls":["hiv2004","male","age","age2","rumphi","balaka"],"fe":[],"cluster":"villnum","se":"HC1","weights":null,"subset":"MainSample == 1","intercept":true,"level":0.95},"input_digest":"a46c4ba2482871d4285af411598e729792df492741d263058b10ce39a16a0ecf","dataset_digests":{"thornton":"dc13aacf7f58cce5e743c7dbaeafdfd387dc91212e8c2bbf48e7948d8ff682d7"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_a7dd219b","status":"ok","reason":"","figures":[{"key":"coef.any","value":0.43148448096904746,"unit":"","label":"Coefficient on any in the regression of got","se":0.022903775310708474,"ci_low":0.38612877031766796,"ci_high":0.47684019162042696,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de any na regressão por mínimos quadrados ordinários de got.","formula":"","terms":[{"symbol":"n","value":2812.0,"unit":"observações","note":""},{"symbol":"k","value":8.0,"unit":"parâmetros","note":""}],"result":0.43148448096904746,"unit":""},{"text_pt":"Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.0005245829234834191,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.022903775310708474,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 118 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.43148448096904746,"unit":"","note":""},{"symbol":"c","value":1.980272249272974,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.022903775310708474,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":2812,"flag":false,"note":"observations used"},{"name":"k","value":8,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.1813597706906881,"flag":false,"note":""},{"name":"df_resid","value":2804,"flag":false,"note":""},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'villnum'"},{"name":"n_clusters","value":119,"flag":false,"note":"119 clusters of 'villnum'"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.3459532807161491,"se":0.059187136420660445,"t":5.845075495076448,"p":4.5941886333014205e-8,"ci_low":0.2287466369483815,"ci_high":0.46315992448391674},{"term":"any","coef":0.43148448096904746,"se":0.022903775310708474,"t":18.839011259742424,"p":2.2667877103064597e-37,"ci_low":0.38612877031766796,"ci_high":0.47684019162042696},{"term":"hiv2004","coef":-0.05455339544033501,"se":0.03125820681070506,"t":-1.745250320042415,"p":0.08354447647717793,"ci_low":-0.11645315494960973,"ci_high":0.007346364068939702},{"term":"male","coef":-0.009752267006381356,"se":0.01853882046209857,"t":-0.5260457118250452,"p":0.5998437286886793,"ci_low":-0.04646417870172913,"ci_high":0.026959644688966417},{"term":"age","coef":0.004712882882559126,"se":0.003077147532971754,"t":1.5315752111526681,"p":0.12830390998885716,"ci_low":-0.001380706983903632,"ci_high":0.010806472749021884},{"term":"age2","coef":-0.00003730645931702345,"se":0.00004132298729666214,"t":-0.9028016065052703,"p":0.3684704324710839,"ci_low":-0.00011913722431766311,"ci_high":0.00004452430568361621},{"term":"rumphi","coef":-0.1371169428813025,"se":0.029222891040900964,"t":-4.692107385589974,"p":7.340978823823163e-6,"ci_low":-0.1949862230531265,"ci_high":-0.0792476627094785},{"term":"balaka","coef":-0.11788369602824725,"se":0.023270933534883274,"t":-5.065705501308697,"p":1.5197747132819793e-6,"ci_low":-0.16396647992205243,"ci_high":-0.07180091213444206}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across clusters of 'villnum' and may be correlated within them"],"notes":""},"outputs":null,"output_digest":"65ad886eba64e2abe66b87eaec7e5d2618c84ceb6f3aa55f56fbedee2dbf2be5","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:57.586414Z","duration_ms":12.571540893986821,"error":null,"decided_by":"model","rationale":"Table 4, column (1): OLS of attendance at the results centre on any incentive, HIV status, sex, age, age squared and district dummies, errors clustered by village, in the main sample (tested, with demographic data).","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_e7f7d7cd","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"fadd82fe2056f8727d86fd7fc527a82bd0ddd5b2873fbbb44cad3eaa25e384f9"}}
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