{"call_id":"call_4919515c","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q064/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","index":0,"op":"select","args":{"columns":["elecc_presid2013","ratio_treat","D2D30","D2D40","D2D50","dpto1","elecc_presid2008","db_elecc_presid2008","loc"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":4025,"rows_after":4025,"frame_sha256_before":"1fa9d0cb8000f3a56f374858661e8c1f89912a8c22eab4531ea27b3612539658","frame_sha256_after":"464602e4ad153c1684d1529bb3348daffca2cd36366ad00bca1669f9f694f776"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.455959Z","duration_ms":0.5362080410122871,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_fbe31d69","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q064/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","index":1,"op":"dropna","args":{"columns":["elecc_presid2013","ratio_treat","D2D30","D2D40","D2D50","dpto1","elecc_presid2008","db_elecc_presid2008","loc"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":4025,"rows_after":3350,"frame_sha256_before":"464602e4ad153c1684d1529bb3348daffca2cd36366ad00bca1669f9f694f776","frame_sha256_after":"859816ad70f8cb8dcb76f82222ed614b76809f81b968b0234c74cbb31a7141f3"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.494544Z","duration_ms":3.2572918571531773,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_5caa2cd0","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q064/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","index":2,"op":"dummies","args":{"column":"dpto1","prefix":"dpto1","drop_first":true}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":3350,"rows_after":3350,"frame_sha256_before":"859816ad70f8cb8dcb76f82222ed614b76809f81b968b0234c74cbb31a7141f3","frame_sha256_after":"3f5aa820095a6de8d532d9756f631b83c05fa51de40e41028ca3e91638bbd530"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.509171Z","duration_ms":0.3659999929368496,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{}}
{"call_id":"call_9092ab32","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q064/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":"2026-10-15T17:00:54.535736Z","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\": \"ds_0034\",\n \"rows\": 4025,\n \"description\": \"| Variable Name | Description |\\n|---------------|-------------|\\n| id_indiv | Unique individual respondent identifier. |\\n| loc | Locality identifier (cluster code used to group households). |\\n| department | Administrative department code (first-level region). |\\n| district | Administrative district identifier (subdivision of department). |\\n| locality | Locality name or numeric locality code. |\\n| treatment | Binary indicator of experimental group assignment (campaign-group flag). |\\n| treat2 | Secondary assignment or variant indicator (auxiliary assignment flag). |\\n| control | Binary indicator for membership in the comparison condition used in the study design. |\\n| D2D | Binary indicator for assignment to the door-to-door campaign arm. |\\n| massive | Binary indicator for assignment to the mass/public rally information arm. |\\n| D2DT | Individual-level indicator for households that were directly visited by a canvasser. |\\n| D2DC | Individual-level indicator for households in door-to-door localities that were not directly visited. |\\n| lineal | Binary indicator denoting a linear settlement layout (houses aligned along a road). |\\n| lineal_inv | Complementary indicator to lineal (inverse or non-linear locality flag). |\\n| dpto1 | Department dummy (coded indicator used in analyses to capture department membership). |\\n| age | Respondent age in years. |\\n| married | Binary indicator for marital status (1 = married). |\\n| children | Binary indicator for whether the respondent has any children. |\\n| num_children | Number of children reported by the respondent. |\\n| employed | Binary indicator for whether the respondent is formally employed. |\\n| languag | Language indicator (captures primary/only language spoken, e.g., Guaraní/Spanish). |\\n| yrseduc | Years of formal schooling completed by the respondent. |\\n| bornloc | Binary indicator for whether the respondent was born in the same locality. |\\n| hh_asset_index | Household asset index (composite wealth proxy based on owned assets). |\\n| ratio_treat | Proportion of households in the locality that were actually visited (0–1 scale). |\\n| fecha_registro | Date code or numeric value associated with voter registration (registration date field). |\\n| registro | Individual-level indicator for whether the respondent was registered to vote (registration status). |\\n| elecc_muni2010 | Indicator for whether the respondent voted in the 2010 municipal election. |\\n| elecc_presid2013 | Indicator for whether the respondent voted in the April 2013 presidential election. |\\n| elecc_presid2008 | Indicator for whether the respondent voted in the 2008 presidential election. |\\n| log_pop | Natural log of locality population (log-population). |\\n| mujeres_perc | Percentage of locality population that is female. |\\n| mujeres_PEA | Percentage of economically active population that is female (locality-level). |\\n| pob_0_14_perc | Percentage of locality population aged 0–14. |\\n| pob_15_64_perc | Percentage of locality population aged 15–64. |\\n| pob_65mas_perc | Percentage of locality population aged 65 and over. |\\n| analfabetos_perc | Percentage of illiterate residents in the locality. |\\n| asiste_escuela_perc | Percentage of school-age population attending school (locality-level). |\\n| TASA_women | Female labor/activity rate at the locality level (labor-market indicator). |\\n| TASA_men | Male labor/activity rate at the locality level (labor-market indicator). |\\n| electricidad_perc | Percentage of dwellings with access to electricity in the locality. |\\n| agua_perc | Percentage of dwellings with running water within the household in the locality. |\\n| basura_perc | Percentage of dwellings with trash collection service in the locality. |\\n| fono_fijo_perc | Percentage of dwellings with a fixed (landline) telephone in the locality. |\\n| fono_cel_perc | Percentage of dwellings with a mobile phone in the locality. |\\n| ocupantes | Number of occupants in the dwelling (household size measure). |\\n| Rural | Indicator for rural locality status (1 = rural). |\\n| distancia2_final | Distance measure to the nearest voting/registration center (locality-level distance). |\\n| cedula_perc | Percentage of residents with a national ID card (cédula) in the locality. |\\n| registrado_perc | Percentage of residents registered to vote in the locality. |\\n| extranjero | Percentage or indicator of foreign-born/non-native residents in the locality. |\\n| log_viv_ocupadas | Natural log of occupied dwellings in the locality. |\\n| desague_perc | Percentage of dwellings with sewage connection in the locality. |\\n| dens_real | Empirical density measure for the locality (population per area unit). |\\n| dens_censo | Census-based population density metric for the locality. |\\n| log_hectareas | Natural log of locality area in hectares. |\\n| attendance_rallies | Self-reported attendance at public rallies (individual survey response). |\\n| interes | Self-reported interest in politics or political participation (survey item). |\\n| izquierda | Self-placement or indicator for left political orientation (survey response). |\\n| derecha | Self-placement or indicator for right political orientation (survey response). |\\n| centro | Self-placement or indicator for centrist political orientation (survey response). |\\n| D2D30 | Assignment indicator for the allocated 30% door-to-door intensity category (planned assignment). |\\n| D2D40 | Assignment indicator for the allocated 40% door-to-door intensity category (planned assignment). |\\n| D2D50 | Assignment indicator for the allocated 50% door-to-door intensity category (planned assignment). |\\n| db_age | Baseline or database value of respondent age (value from prior census/baseline round). |\\n| db_married | Baseline marital-status value (from prior census/baseline round). |\\n| db_children | Baseline indicator of whether respondent had children (baseline measure). |\\n| db_num_children | Baseline number of children (from prior round). |\\n| db_employed | Baseline employment indicator (from prior census/baseline round). |\\n| db_languag | Baseline language indicator (value from prior round). |\\n| db_yrseduc | Baseline years of education (from prior round). |\\n| db_bornloc | Baseline indicator for born-in-locality status (prior-round value). |\\n| db_hh_asset_index | Baseline household asset index (from prior census or baseline survey). |\\n| db_fecha_registro | Baseline registration date field (prior-round value). |\\n| db_elecc_muni2010 | Baseline indicator of voting in the 2010 municipal election (prior-round or administrative match). |\\n| db_elecc_presid2008 | Baseline indicator of voting in the 2008 presidential election (administrative baseline). |\\n| db_derecha | Baseline indicator for right political orientation (prior-round survey value). |\\n| db_izquierda | Baseline indicator for left political orientation (prior-round survey value). |\\n| db_centro | Baseline indicator for centrist political orientation (prior-round survey value). |\\n| db_interes | Baseline self-reported interest in politics (prior-round value). |\\n| db_log_pop | Baseline log-population for the locality (from earlier census/benchmark). |\\n| db_mujeres_perc | Baseline percentage female (locality-level baseline). |\\n| db_mujeres_PEA | Baseline percent of economically active population female (baseline). |\\n| db_pob_0_14_perc | Baseline percent aged 0–14 (locality-level baseline). |\\n| db_pob_15_64_perc | Baseline percent aged 15–64 (locality-level baseline). |\\n| db_pob_65mas_perc | Baseline percent aged 65+ (locality-level baseline). |\\n| db_analfabetos_perc | Baseline percent illiterate (locality-level baseline). |\\n| db_asiste_escuela_perc | Baseline percent attending school (locality baseline). |\\n| db_TASA_women | Baseline female labor/activity rate (locality-level baseline). |\\n| db_TASA_men | Baseline male labor/activity rate (locality-level baseline). |\\n| db_electricidad_perc | Baseline percentage of dwellings with electricity (locality-level baseline). |\\n| db_agua_perc | Baseline percentage of dwellings with running water (locality-level baseline). |\\n| db_basura_perc | Baseline percentage of dwellings with trash collection (locality-level baseline). |\\n| db_fono_fijo_perc | Baseline percentage with fixed (landline) phones (locality-level baseline). |\\n| db_fono_cel_perc | Baseline percentage with mobile phones (locality-level baseline). |\\n| db_ocupantes | Baseline average number of occupants per household (baseline household-size measure). |\\n| db_Rural | Baseline rural-status indicator (locality-level baseline). |\\n| db_distancia2_final | Baseline distance to voting/registration center (baseline locality distance). |\\n| db_cedula_perc | Baseline percentage with national ID card (locality-level baseline). |\\n| db_registrado_perc | Baseline percentage registered to vote (locality-level baseline). |\\n| db_extranjero | Baseline percentage of foreign-born residents (locality-level baseline). |\\n| db_log_viv_ocupadas | Baseline log of occupied dwellings (locality-level baseline). |\\n| db_desague_perc | Baseline percentage of dwellings with sewage (locality-level baseline). |\\n| db_dens_real | Baseline empirical density measure (locality-level baseline). |\\n| db_dens_censo | Baseline census-based density (locality-level baseline). |\\n| db_log_hectareas | Baseline log of locality area in hectares (baseline). |\\n| db_attendance_rallies | Baseline self-reported attendance at rallies (prior-round survey value). |\\n| db_ratio_treat | Baseline or planned proportion of households slated for visits (assignment-intensity plan). |\\n\\nData Description: This dataset originates from field data collection in rural Paraguay conducted around the 2013 presidential election and brings together individual-level household survey responses (adult women), matched administrative electoral records, locality-level socio-demographic indicators from census sources, and baseline/census measures. Survey teams visited sampled rural localities in two departments, recorded demographic and attitudinal information, and merged those records with locality characteristics (including satellite-derived settlement layout) and administrative voting/registration information; a set of assignment/assignment-intensity flags and baseline (\\\"db_\\\") variables are included to document planned allocation and pre-intervention/locality benchmarks.\",\n \"columns\": {\n  \"id_indiv\": \"float64\",\n  \"loc\": \"float64\",\n  \"department\": \"float64\",\n  \"district\": \"float64\",\n  \"locality\": \"float64\",\n  \"treatment\": \"float64\",\n  \"treat2\": \"float64\",\n  \"control\": \"float64\",\n  \"D2D\": \"float64\",\n  \"massive\": \"float64\",\n  \"D2DT\": \"float64\",\n  \"D2DC\": \"float64\",\n  \"lineal\": \"float64\",\n  \"lineal_inv\": \"float64\",\n  \"dpto1\": \"float64\",\n  \"age\": \"float64\",\n  \"married\": \"float64\",\n  \"children\": \"float64\",\n  \"num_children\": \"float64\",\n  \"employed\": \"float64\",\n  \"languag\": \"float64\",\n  \"yrseduc\": \"float64\",\n  \"bornloc\": \"float64\",\n  \"hh_asset_index\": \"float64\",\n  \"ratio_treat\": \"float64\",\n  \"fecha_registro\": \"float64\",\n  \"registro\": \"float64\",\n  \"elecc_muni2010\": \"float64\",\n  \"elecc_presid2013\": \"float64\",\n  \"elecc_presid2008\": \"float64\",\n  \"log_pop\": \"float64\",\n  \"mujeres_perc\": \"float64\",\n  \"mujeres_PEA\": \"float64\",\n  \"pob_0_14_perc\": \"float64\",\n  \"pob_15_64_perc\": \"float64\",\n  \"pob_65mas_perc\": \"float64\",\n  \"analfabetos_perc\": \"float64\",\n  \"asiste_escuela_perc\": \"float64\",\n  \"TASA_women\": \"float64\",\n  \"TASA_men\": \"float64\",\n  \"electricidad_perc\": \"float64\",\n  \"agua_perc\": \"float64\",\n  \"basura_perc\": \"float64\",\n  \"fono_fijo_perc\": \"float64\",\n  \"fono_cel_perc\": \"float64\",\n  \"ocupantes\": \"float64\",\n  \"Rural\": \"float64\",\n  \"distancia2_final\": \"float64\",\n  \"cedula_perc\": \"float64\",\n  \"registrado_perc\": \"float64\",\n  \"extranjero\": \"float64\",\n  \"log_viv_ocupadas\": \"float64\",\n  \"desague_perc\": \"float64\",\n  \"dens_real\": \"float64\",\n  \"dens_censo\": \"float64\",\n  \"log_hectareas\": \"float64\",\n  \"attendance_rallies\": \"float64\",\n  \"interes\": \"float64\",\n  \"izquierda\": \"float64\",\n  \"derecha\": \"float64\",\n  \"centro\": \"float64\",\n  \"D2D30\": \"float64\",\n  \"D2D40\": \"float64\",\n  \"D2D50\": \"float64\",\n  \"db_age\": \"float64\",\n  \"db_married\": \"float64\",\n  \"db_children\": \"float64\",\n  \"db_num_children\": \"float64\",\n  \"db_employed\": \"float64\",\n  \"db_languag\": \"float64\",\n  \"db_yrseduc\": \"float64\",\n  \"db_bornloc\": \"float64\",\n  \"db_hh_asset_index\": \"float64\",\n  \"db_fecha_registro\": \"float64\",\n  \"db_elecc_muni2010\": \"float64\",\n  \"db_elecc_presid2008\": \"float64\",\n  \"db_derecha\": \"float64\",\n  \"db_izquierda\": \"float64\",\n  \"db_centro\": \"float64\",\n  \"db_interes\": \"float64\",\n  \"db_log_pop\": \"float64\",\n  \"db_mujeres_perc\": \"float64\",\n  \"db_mujeres_PEA\": \"float64\",\n  \"db_pob_0_14_perc\": \"float64\",\n  \"db_pob_15_64_perc\": \"float64\",\n  \"db_pob_65mas_perc\": \"float64\",\n  \"db_analfabetos_perc\": \"float64\",\n  \"db_asiste_escuela_perc\": \"float64\",\n  \"db_TASA_women\": \"float64\",\n  \"db_TASA_men\": \"float64\",\n  \"db_electricidad_perc\": \"float64\",\n  \"db_agua_perc\": \"float64\",\n  \"db_basura_perc\": \"float64\",\n  \"db_fono_fijo_perc\": \"float64\",\n  \"db_fono_cel_perc\": \"float64\",\n  \"db_ocupantes\": \"float64\",\n  \"db_Rural\": \"float64\",\n  \"db_distancia2_final\": \"float64\",\n  \"db_cedula_perc\": \"float64\",\n  \"db_registrado_perc\": \"float64\",\n  \"db_extranjero\": \"float64\",\n  \"db_log_viv_ocupadas\": \"float64\",\n  \"db_desague_perc\": \"float64\",\n  \"db_dens_real\": \"float64\",\n  \"db_dens_censo\": \"float64\",\n  \"db_log_hectareas\": \"float64\",\n  \"db_attendance_rallies\": \"float64\",\n  \"db_ratio_treat\": \"float64\"\n }\n}\n{\n \"id\": \"ref_iv\",\n \"rows\": 3350,\n \"description\": \"Complete rows of the script's columns, with the dpto1 dummy\",\n \"columns\": {\n  \"elecc_presid2013\": \"float64\",\n  \"ratio_treat\": \"float64\",\n  \"D2D30\": \"float64\",\n  \"D2D40\": \"float64\",\n  \"D2D50\": \"float64\",\n  \"dpto1\": \"float64\",\n  \"elecc_presid2008\": \"float64\",\n  \"db_elecc_presid2008\": \"float64\",\n  \"loc\": \"float64\",\n  \"dpto1_1.0\": \"int64\"\n }\n}\n\n# Question\nWhat is the causal effect of a marginal increase in the locality-level share of households actually visited by canvassers on the probability that a woman who was targeted (contacted) votes in the 2013 presidential election? Report the local average treatment effect for the subgroup of women whose exposure responds to the experimentally assigned canvassing intensity.","response":"IV2SLS.from_formula('elecc_presid2013 ~ 1 + C(dpto1) + controls + [ratio_treat ~ D2D30 + D2D40 + D2D50]'), clustered by loc, debiased\ninfer__iv_2sls({\"dataset\": \"ref_iv\", \"y\": \"elecc_presid2013\", \"endog\": [\"ratio_treat\"], \"instruments\": [\"D2D30\", \"D2D40\", \"D2D50\"], \"exog\": [\"elecc_presid2008\", \"db_elecc_presid2008\", \"dpto1_1.0\"], \"cluster\": \"loc\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9092ab32","text":"IV2SLS.from_formula('elecc_presid2013 ~ 1 + C(dpto1) + controls + [ratio_treat ~ D2D30 + D2D40 + D2D50]'), clustered by loc, debiased","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"ref_iv","y":"elecc_presid2013","endog":["ratio_treat"],"instruments":["D2D30","D2D40","D2D50"],"exog":["elecc_presid2008","db_elecc_presid2008","dpto1_1.0"],"cluster":"loc"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"da65feaac353e8804ccbf4707466fff8de404aaac2220cf11e4a7d9c2cb16d7b","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["769bf542010a79555416819640607100a271453254e7ce5a1b0d4375625d1e99"],"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":0.7501670625060797}}
{"call_id":"call_27c4b42f","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q064/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","y":"elecc_presid2013","endog":["ratio_treat"],"instruments":["D2D30","D2D40","D2D50"],"exog":["elecc_presid2008","db_elecc_presid2008","dpto1_1.0"],"cluster":"loc","se":"HC1","subset":null,"level":0.95},"input_digest":"8e1d4f5bee5a485e46def16e0dfcca5c035b26d9be89f0c48ddfdbd64d526cdf","dataset_digests":{"ref_iv":"3f5aa820095a6de8d532d9756f631b83c05fa51de40e41028ca3e91638bbd530"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_27c4b42f","status":"ok","reason":"","figures":[{"key":"coef.ratio_treat","value":0.12222625458373189,"unit":"","label":"2SLS effect of ratio_treat on elecc_presid2013","se":0.054945514449942626,"ci_low":0.014069191182986981,"ci_high":0.2303833179844768,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de ratio_treat por mínimos quadrados em dois estágios: elecc_presid2013 sobre as projeções dos regressores endógenos nos instrumentos ['D2D30', 'D2D40', 'D2D50'] e os controles; resíduos com os valores observados de ratio_treat.","formula":"","terms":[{"symbol":"n","value":3350.0,"unit":"observações","note":""},{"symbol":"instrumentos","value":3.0,"unit":"","note":""}],"result":0.12222625458373189,"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":0.003019009558168854,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.054945514449942626,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 281 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.12222625458373189,"unit":"","note":""},{"symbol":"c","value":1.9684420918340833,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.054945514449942626,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.ratio_treat","value":31.546698921680957,"flag":false,"note":"Wald F of the excluded instruments in the first stage for ratio_treat (cluster-robust standard errors (CR1) by 'loc')"},{"name":"partial_r2.ratio_treat","value":0.5058889232757094,"flag":false,"note":"share of the variation the instruments explain beyond the controls"},{"name":"weak_instrument","value":false,"flag":false,"note":"every first-stage F is at least 10"},{"name":"overid_p","value":0.7034932770440423,"flag":false,"note":"Sargan test of the overidentifying restrictions (assumes homoskedastic errors)"},{"name":"sargan","value":0.7033939178279001,"flag":false,"note":"n·R² of the structural residuals on the instruments"},{"name":"sargan_df","value":2,"flag":false,"note":""},{"name":"n_obs","value":3350,"flag":false,"note":""},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'loc'"},{"name":"n_clusters","value":282,"flag":false,"note":"282 clusters of 'loc'"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.5607708104948047,"se":0.015816252026013576,"t":35.45535374452077,"p":9.846489096114015e-106,"ci_low":0.5296374342717435,"ci_high":0.5919041867178659},{"term":"elecc_presid2008","coef":0.37415375588722816,"se":0.016068630363443435,"t":23.284732265572426,"p":1.5254354615366937e-67,"ci_low":0.34252358752170287,"ci_high":0.40578392425275345},{"term":"db_elecc_presid2008","coef":0.1145314316551564,"se":0.1607950907948696,"t":0.7122818929917897,"p":0.4768812137467282,"ci_low":-0.20198439322574807,"ci_high":0.43104725653606085},{"term":"dpto1_1.0","coef":-0.037642193397211,"se":0.015901911108927573,"t":-2.3671490262625166,"p":0.01860347134290003,"ci_low":-0.06894418456462803,"ci_high":-0.006340202229793958},{"term":"ratio_treat","coef":0.12222625458373189,"se":0.054945514449942626,"t":2.224499229961428,"p":0.026909264043325335,"ci_low":0.014069191182986981,"ci_high":0.2303833179844768}],"first_stage":[{"endog":"ratio_treat","instrument":"D2D30","coef":0.29658862505393885,"se":0.04546692073663891,"t":6.523173776642782,"p":3.1933026178806075e-10,"ci_low":0.2070896244898549,"ci_high":0.3860876256180228},{"endog":"ratio_treat","instrument":"D2D40","coef":0.39278264962053744,"se":0.07872634457693856,"t":4.989214877577282,"p":1.0640045831431322e-6,"ci_low":0.23781439921905767,"ci_high":0.5477509000220172},{"endog":"ratio_treat","instrument":"D2D50","coef":0.24826510681444106,"se":0.04713564245900836,"t":5.26703559902351,"p":2.7617198273300586e-7,"ci_low":0.15548132417248722,"ci_high":0.3410488894563949}],"reduced_form":[{"instrument":"D2D30","coef":0.030486210007206235,"se":0.034287513001150426,"t":0.8891344789627452,"p":0.3746913253919222,"ci_low":-0.03700677380856664,"ci_high":0.09797919382297911},{"instrument":"D2D40","coef":0.06019885991124399,"se":0.026122208257676263,"t":2.304508842339314,"p":0.021923318077279545,"ci_low":0.008778805645178162,"ci_high":0.11161891417730982},{"instrument":"D2D50","coef":0.011522540929111591,"se":0.0262922590994222,"t":0.4382484169785475,"p":0.6615429270858703,"ci_low":-0.04023224857159876,"ci_high":0.06327733042982195}]},"datasets":{},"warnings":[],"assumptions":["the instruments affect the outcome only through the endogenous regressors (exclusion restriction)","the instruments are as good as randomly assigned given the controls","errors are independent across clusters of 'loc' and may be correlated within them"],"notes":""},"outputs":null,"output_digest":"9800d53959b60d54e329df1b8caff23097820cd11eae88edb10a0125020f3934","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.538279Z","duration_ms":6.766750011593103,"error":null,"decided_by":"model","rationale":"IV2SLS.from_formula('elecc_presid2013 ~ 1 + C(dpto1) + controls + [ratio_treat ~ D2D30 + D2D40 + D2D50]'), clustered by loc, debiased","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_9092ab32","llm_name":"infer__iv_2sls","vendor_id":"script_1","result_for_model_ref":"5ada47c8f4a1c4f20e92cb2216d8dd55c1ef481eb68224fc431c26dde53b0316"}}
{"call_id":"call_8243696b","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q064/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.545353Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.iv_2sls): coef.ratio_treat = 0,1222. Reference path transcribed from the item's own estimation script.\", \"figure_refs\": [\"call_27c4b42f:coef.ratio_treat\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_8243696b","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.iv_2sls): coef.ratio_treat = 0,1222. Reference path transcribed from the item's own estimation script.","figure_refs":["call_27c4b42f:coef.ratio_treat"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"da65feaac353e8804ccbf4707466fff8de404aaac2220cf11e4a7d9c2cb16d7b","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["769bf542010a79555416819640607100a271453254e7ce5a1b0d4375625d1e99","0f183c5c3806c529f996a9f6a9d6009c6a1971de8108880894fba08702ea61ba","0fc2c9d337b6ac7a481dc37854975ece18eb564aa7d024e53bc0a0a7bdf9422b"],"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":0.32479106448590755}}
{"call_id":"call_e92c55ba","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q064/e1a1","step":"analysis","kind":"note","name":"analyst.guard","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.547299Z","duration_ms":0.0,"error":null,"decided_by":"code","rationale":"","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_8243696b","numbers_found":["0,1222"],"unbacked":[],"uncited":[],"invalid_refs":[],"empty":false,"verdict":"accepted","retries_left":1,"vendor_id":"script_2"}}