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{"call_id":"call_50f77c2d","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q032/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"2925aeab2262","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":"2026-10-15T17:09:27.926640Z","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_0017\",\n \"rows\": 899,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| (index) | Row index from the CSV export; carries no substantive information. |\\n| state | Two-letter U.S. state postal abbreviation identifying the state associated with the observation. |\\n| year | Calendar year in which the state held the referenced general election. Most elections occurred in November of even-numbered years, though some states held major general elections in other months or in odd-numbered years; the value here reflects the actual calendar year of that election. |\\n| ln_x_cand_all | Log-transformed average number of newspaper advertisements per day placed by or on behalf of individual candidates in the seven days preceding the general election; constructed as ln(ads per newspaper-day + 1). Source counts come from hand-coding full newspaper issues, then averaging ad counts to the newspaper-year and aggregating to a state-year mean. Included offices typically comprise statewide executive posts, state legislatures, U.S. House, judicial, and many local offices; advertisements focused solely on parties are tracked separately, and candidate ads for U.S. Senate and the presidency are excluded from this summary measure. |\\n| prim_sw | Indicator for whether, in that year, the state used state-administered direct primary elections to nominate candidates for statewide offices (1 = direct primaries in use; 0 = nomination by caucus/convention or other non-primary procedures). Reflects the institutional status in each state-year during the late 19th and early 20th centuries. |\\n\\nDataset Context:\\nThis state-year panel assembles historical evidence on campaign communications during an era when newspapers were the dominant mass medium. Researchers reviewed issues from 90 local newspapers across 33 mostly non-Southern states between 1880 and 1930, focusing on the week immediately preceding each state’s general election. Political advertisements were hand-coded at the ad level, distinguishing candidate-focused content from party-only notices. To make newspapers comparable despite different publication schedules and occasional missing issues, counts were normalized to advertisements per newspaper-day, averaged to the newspaper-year, and then aggregated to a state-year mean.\\n\\nThe offices captured in the primary advertising measure are those commonly elected across states throughout the period: statewide executives (e.g., governor, attorney general), state legislatures, the U.S. House, judicial posts, and many local offices. Candidate advertising for U.S. Senate and for the presidency was excluded from that main summary to keep the measure focused on offices most consistently affected by state-level electoral rules of the time. The log transformation adds one before taking logs so that years with zero observed advertising are retained on a continuous scale.\\n\\nThe panel spans a period of substantial institutional change often associated with Progressive Era reforms. States transitioned at different times to practices such as direct primary elections for nominating statewide candidates, and the dataset records whether a state employed direct primaries in each year. Because election timing varied across states—some held major contests in odd-numbered years or in months other than November—the year variable records the actual calendar year of the pertinent general election. Overall, the dataset provides a harmonized view of newspaper-based campaign activity across places and time, facilitating comparisons of advertising intensity during a formative phase in U.S. electoral development.\",\n \"columns\": {\n  \"state\": \"str\",\n  \"year\": \"int64\",\n  \"ln_x_cand_all\": \"float64\",\n  \"prim_sw\": \"int64\"\n }\n}\n{\n \"id\": \"ref_adopt\",\n \"rows\": 30,\n \"description\": \"Each state's first year with prim_sw == 1, among rows with the outcome\",\n \"columns\": {\n  \"state\": \"str\",\n  \"adopt_year\": \"int64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 58,\n \"description\": \"State rows of 1906 and 1908: 1908 adopters (0 then 1) and states not yet adopting by 1908 (0 and 0)\",\n \"columns\": {\n  \"state\": \"str\",\n  \"year\": \"int64\",\n  \"ln_x_cand_all\": \"float64\",\n  \"prim_sw\": \"int64\",\n  \"adopt_year\": \"float64\",\n  \"nyears\": \"int64\",\n  \"p_pre\": \"int64\",\n  \"p_post\": \"int64\",\n  \"Treated\": \"int64\",\n  \"Post\": \"int64\",\n  \"Treated_Post\": \"int64\"\n }\n}\n{\n \"id\": \"ref_post\",\n \"rows\": 33,\n \"description\": \"Each state's prim_sw in 1908 (p_post)\",\n \"columns\": {\n  \"state\": \"str\",\n  \"p_post\": \"int64\"\n }\n}\n{\n \"id\": \"ref_pre\",\n \"rows\": 31,\n \"description\": \"Each state's prim_sw in 1906 (p_pre)\",\n \"columns\": {\n  \"state\": \"str\",\n  \"p_pre\": \"int64\"\n }\n}\n{\n \"id\": \"ref_years\",\n \"rows\": 33,\n \"description\": \"How many of 1906 and 1908 each state is observed in\",\n \"columns\": {\n  \"state\": \"str\",\n  \"nyears\": \"int64\"\n }\n}\n\n# Question\nDid states that adopted direct primaries in 1908 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1906 to 1908 relative to states that had not yet adopted?","response":"smf.ols('Y ~ Treated + Post + Treated:Post').fit(cov_type='cluster', groups=state, use_t=True) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"ln_x_cand_all\", \"x\": [\"Treated_Post\"], \"controls\": [\"Treated\", \"Post\"], \"cluster\": \"state\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_50f77c2d","text":"smf.ols('Y ~ Treated + Post + Treated:Post').fit(cov_type='cluster', groups=state, use_t=True) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"ln_x_cand_all","x":["Treated_Post"],"controls":["Treated","Post"],"cluster":"state"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"0e5f00c1ebc76fa4ba14c5208097b1364cc417a8ae80e6563d1c2e57a0706ba6","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["811dd421a4ddc67aac093751a1d8f1ed81696f8f11370aca23f23d3c87c797b1"],"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":0.6947079673409462}}
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