{"call_id":"call_f52d2dbb","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q073/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","index":0,"op":"select","args":{"columns":["wm_turnout","direct","new","year","left","wm_voters","enep"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":2750,"rows_after":2750,"frame_sha256_before":"60daee4b992c2a8c0fc1040bc2e87793b668776b0f837d37ea027f6525e3bf51","frame_sha256_after":"e1346743ef28e35e122eedea0aadd9e6f931bb4ee0800251781bcb560a5f5a48"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.928575Z","duration_ms":0.5750830750912428,"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_503ef18b","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q073/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","index":1,"op":"dropna","args":{"columns":["wm_turnout","direct","new","year","left","wm_voters","enep"]}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":2750,"rows_after":2749,"frame_sha256_before":"e1346743ef28e35e122eedea0aadd9e6f931bb4ee0800251781bcb560a5f5a48","frame_sha256_after":"cbf3885c5f36e4556f432b25d1b7fed6baddab5bc88f620b2a0d9cb0705aff61"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.931693Z","duration_ms":0.6707089487463236,"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_7aef7dbb","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q073/e1a1","step":"data","kind":"note","name":"data.transform","version":"","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","index":2,"op":"dummies","args":{"column":"year","prefix":"year","drop_first":true}},"input_digest":"","dataset_digests":{},"result":null,"outputs":{"rows_before":2749,"rows_after":2749,"frame_sha256_before":"cbf3885c5f36e4556f432b25d1b7fed6baddab5bc88f620b2a0d9cb0705aff61","frame_sha256_after":"66f954b4feb6f56a868d2459329d40535d7c670409cf3314492f0cc3cba4e08f"},"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.933097Z","duration_ms":0.3656668122857809,"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_09d5cf98","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q073/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":"2026-10-15T17:00:54.944314Z","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_0043\",\n \"rows\": 2750,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| (Unnamed) | Unlabeled column from the original data export (often an empty field or row index placeholder). |\\n| direct | Municipality institutional type indicator for the given observation year: 1 = direct-democracy town meeting as the highest local decision-making body; 0 = representative municipal council. |\\n| area1918 | Municipality land area circa 1918 (units as recorded in historical registers). |\\n| nr_poor_relief1917 | Number of individuals receiving poor relief in 1917 in the municipality (count). |\\n| shareagri1917 | Share of local workforce/economic activity in agriculture in 1917 (proportion). |\\n| taxbase1918 | Municipal tax base in 1918 (monetary taxable-capacity measure as recorded in historical accounts; units as in source). |\\n| year | Observation year (parliamentary election year) for turnout and vote-based variables (e.g., 1921, 1924, 1928, …, 1944). |\\n| wm_voters | Number of eligible female voters in the municipality for the election year `year` (count). |\\n| m_turnout | Men’s turnout in parliamentary elections in `year` (percent, 0–100): (number of male votes cast ÷ number of eligible male voters) × 100. |\\n| wm_turnout | Women’s turnout in parliamentary elections in `year` (percent, 0–100): (number of female votes cast ÷ number of eligible female voters) × 100. |\\n| wv_share | Share of total votes cast that were cast by women in `year` (percent, 0–100): (female votes cast ÷ total votes cast) × 100. |\\n| left | Support for leftist parties in the municipality in `year` (vote share; coding as in source, typically a proportion). |\\n| enep | Effective number of electoral parties in the municipality in `year` (Laakso–Taagepera index computed from party vote shares). |\\n| municipality | Municipality identifier (name or code as provided in the source data). |\\n| county | County identifier (name or code as provided in the source data). |\\n| population | Municipality population size (number of inhabitants) measured in the year *prior* to the election/observation year: Population\\\\_{t−1} for election year t = `year`. This is the population figure used in the statutory 1,500-person benchmark in contemporaneous municipal governance rules. |\\n| over1500 | Indicator based on `population` (i.e., Population\\\\_{t−1} relative to 1,500): 1 if `population` > 1,500; 0 otherwise. |\\n| nr_organized | Number of organized local associations/organizations in the municipality (count from historical records; year as recorded in source). |\\n| zeropop | Distance of lagged population from the 1,500-person benchmark: `population` − 1500, where `population` is Population\\\\_{t−1} for election year t = `year`. |\\n| c_m | Unclear/undocumented field carried over from the source file (may be empty or auxiliary). |\\n| new | Indicator for being below the 1,500-person benchmark based on `population` (i.e., Population\\\\_{t−1}): 1 if `zeropop` < 0 (equivalently, `population` < 1,500); 0 otherwise. |\\n\\nDataset Context:\\nThis dataset is a municipality × election-year panel covering Swedish municipalities in the early decades after the introduction of universal suffrage, with election-year observations spanning 1921–1944. It combines historical municipal administrative and demographic measures (e.g., population, land area, tax base, poor relief counts, agriculture share, number of local organizations) with parliamentary electoral statistics disaggregated by gender (numbers of eligible voters and turnout measures for men and women, and women’s share of total votes), as well as local political context indicators (e.g., left-party support and party-system fragmentation).\\n\\nSeveral variables encode municipality population relative to a 1,500-person benchmark used in contemporaneous municipal governance statutes. In this panel setting, the population measure used for the benchmark is recorded as the population in the year preceding the election/observation year (Population\\\\_{t−1} relative to election year t), and the derived threshold/distance indicators are computed from that same lagged population. The data were assembled from Swedish national electoral statistics and historical municipal registers/census and fiscal records; year-suffixed variables denote the historical measurement year (e.g., area1918, taxbase1918).\\n\",\n \"columns\": {\n  \"direct\": \"float64\",\n  \"area1918\": \"float64\",\n  \"nr_poor_relief1917\": \"float64\",\n  \"shareagri1917\": \"float64\",\n  \"taxbase1918\": \"float64\",\n  \"year\": \"int64\",\n  \"wm_voters\": \"float64\",\n  \"m_turnout\": \"float64\",\n  \"wm_turnout\": \"float64\",\n  \"wv_share\": \"float64\",\n  \"left\": \"float64\",\n  \"enep\": \"float64\",\n  \"municipality\": \"float64\",\n  \"county\": \"float64\",\n  \"population\": \"float64\",\n  \"over1500\": \"float64\",\n  \"nr_organized\": \"float64\",\n  \"zeropop\": \"float64\",\n  \"c_m\": \"float64\",\n  \"new\": \"float64\"\n }\n}\n{\n \"id\": \"ref_iv\",\n \"rows\": 2749,\n \"description\": \"Complete rows of the script's columns, with election-year dummies\",\n \"columns\": {\n  \"wm_turnout\": \"float64\",\n  \"direct\": \"float64\",\n  \"new\": \"float64\",\n  \"year\": \"int64\",\n  \"left\": \"float64\",\n  \"wm_voters\": \"float64\",\n  \"enep\": \"float64\",\n  \"year_1924\": \"int64\",\n  \"year_1928\": \"int64\",\n  \"year_1932\": \"int64\",\n  \"year_1936\": \"int64\",\n  \"year_1940\": \"int64\",\n  \"year_1944\": \"int64\"\n }\n}\n\n# Question\nWhat is the causal effect of having municipal direct-democracy institutions (versus representative councils) on women's parliamentary turnout, estimated for municipalities whose institutional status was marginally determined by the 1918 municipal reform?","response":"IV2SLS.from_formula('wm_turnout ~ 1 + left + wm_voters + enep + C(year) + [direct ~ new]').fit(cov_type='robust', debiased=True)\ninfer__iv_2sls({\"dataset\": \"ref_iv\", \"y\": \"wm_turnout\", \"endog\": [\"direct\"], \"instruments\": [\"new\"], \"exog\": [\"left\", \"wm_voters\", \"enep\", \"year_1924\", \"year_1928\", \"year_1932\", \"year_1936\", \"year_1940\", \"year_1944\"], \"se\": \"HC1\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_09d5cf98","text":"IV2SLS.from_formula('wm_turnout ~ 1 + left + wm_voters + enep + C(year) + [direct ~ new]').fit(cov_type='robust', debiased=True)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"ref_iv","y":"wm_turnout","endog":["direct"],"instruments":["new"],"exog":["left","wm_voters","enep","year_1924","year_1928","year_1932","year_1936","year_1940","year_1944"],"se":"HC1"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b3aaf3c2cc39d53c95d841df4e34a978c065d4c123bc0e505d16a3b3f8a23363","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["e4e1652480bab1025657ac8481204a516ce851b296553d6077fae79c07d69ab7"],"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":0.7117500063031912}}
{"call_id":"call_6108d7b1","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q073/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_iv","y":"wm_turnout","endog":["direct"],"instruments":["new"],"exog":["left","wm_voters","enep","year_1924","year_1928","year_1932","year_1936","year_1940","year_1944"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"f291956bab5ece665e8f6c56a93d2df287d830de888c496dc8406cdff4c702d3","dataset_digests":{"ref_iv":"66f954b4feb6f56a868d2459329d40535d7c670409cf3314492f0cc3cba4e08f"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_6108d7b1","status":"ok","reason":"","figures":[{"key":"coef.direct","value":3.928662717450435,"unit":"","label":"2SLS effect of direct on wm_turnout","se":1.0855141453627477,"ci_low":1.80015316257064,"ci_high":6.057172272330231,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de new na forma reduzida (wm_turnout sobre new e os controles) e no primeiro estágio (direct sobre new e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":1.9490019637531393,"unit":"","note":"forma reduzida"},{"symbol":"π","value":0.49609806285889896,"unit":"","note":"primeiro estágio"}],"result":3.928662717450435,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":1.1783409597826167,"unit":"","note":"diagonal da matriz de covariância"}],"result":1.0855141453627477,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 2738 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":3.928662717450435,"unit":"","note":""},{"symbol":"c","value":1.9608307860129344,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":1.0855141453627477,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.direct","value":914.6461459884626,"flag":false,"note":"Wald F of the excluded instruments in the first stage for direct (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.direct","value":0.26895787769146506,"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":"n_obs","value":2749,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":41.54698122504422,"se":2.549966728886183,"t":16.29314639849901,"p":4.88797192616156e-57,"ci_low":36.5469279597355,"ci_high":46.54703449035294},{"term":"left","coef":15.788432279060522,"se":1.2870566972073059,"t":12.26708373711798,"p":1.027276058000391e-33,"ci_low":13.26473188383231,"ci_high":18.312132674288733},{"term":"wm_voters","coef":0.012313657399039217,"se":0.0040600330927011655,"t":3.0328958207694963,"p":0.00244491763825996,"ci_low":0.004352619518639465,"ci_high":0.020274695279438967},{"term":"enep","coef":-3.122819001113924,"se":0.35208959484638114,"t":-8.869387357148197,"p":1.2986367198713285e-18,"ci_low":-3.813207118123529,"ci_high":-2.4324308841043187},{"term":"year_1924","coef":-1.6277274560829595,"se":0.8075531574809651,"t":-2.015628867281504,"p":0.04393632222186286,"ci_low":-3.2112025486135876,"ci_high":-0.04425236355233175},{"term":"year_1928","coef":15.106428756271326,"se":0.7970579855750294,"t":18.95273496993189,"p":2.2808379948900315e-75,"ci_low":13.543532919918356,"ci_high":16.669324592624296},{"term":"year_1932","coef":14.858819725613127,"se":0.8260876164420777,"t":17.986977930512257,"p":1.845644472754995e-68,"ci_low":13.239001695349456,"ci_high":16.478637755876797},{"term":"year_1936","coef":22.957167861458224,"se":0.7545020205997941,"t":30.426913692303092,"p":2.0216490573896478e-175,"ci_low":21.477717071357183,"ci_high":24.436618651559264},{"term":"year_1940","coef":17.524866827193676,"se":0.788515434415825,"t":22.22514114790543,"p":9.511085110480157e-101,"ci_low":15.978721488144764,"ci_high":19.07101216624259},{"term":"year_1944","coef":19.67056865682346,"se":0.7826387830111657,"t":25.13364924383363,"p":1.3181416849010784e-125,"ci_low":18.13594643676747,"ci_high":21.20519087687945},{"term":"direct","coef":3.928662717450435,"se":1.0855141453627477,"t":3.619172291981132,"p":0.0003008986817029755,"ci_low":1.80015316257064,"ci_high":6.057172272330231}],"first_stage":[{"endog":"direct","instrument":"new","coef":0.49609806285889896,"se":0.016403668226538316,"t":30.243117332518196,"p":1.3181530570393921e-173,"ci_low":0.46393324519676044,"ci_high":0.5282628805210374}],"reduced_form":[{"instrument":"new","coef":1.9490019637531393,"se":0.515972635497163,"t":3.7773359082796856,"p":0.0001618895049192856,"ci_low":0.937266935330072,"ci_high":2.960736992176207}]},"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 observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"554dcec9efb9059bd7071fbdb521939d3e203fc478988d4b91b721bae0b24ac0","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:54.946779Z","duration_ms":7.660125149413943,"error":null,"decided_by":"model","rationale":"IV2SLS.from_formula('wm_turnout ~ 1 + left + wm_voters + enep + C(year) + [direct ~ new]').fit(cov_type='robust', debiased=True)","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_09d5cf98","llm_name":"infer__iv_2sls","vendor_id":"script_1","result_for_model_ref":"818c63621374bb703f3edee1a1e87f20b632a40a47c1f581d4de8121658f9afc"}}
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