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{"call_id":"call_f5ce3309","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q027/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":"2026-10-15T17:00:52.509680Z","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_0014\",\n \"rows\": 7007,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| cycle | Even-numbered year of the U.S. House general-election cycle (range: 1980–2012). Serves as the time index for the panel. |\\n| district_final | Congressional district identifier (state postal abbreviation + district number, e.g., AK1 for at‑large). District codes are reindexed after each decennial redistricting to reflect new boundaries. |\\n| general_sharetotal_A_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Asian. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. |\\n| general_sharetotal_B_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Black. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. |\\n| general_sharetotal_H_all | Proportion (0–1) of all itemized individual dollars contributed to candidates in the district’s general-election contest that are attributed to donors classified as Latino/Hispanic. Donor ethnoracial identity is inferred with a surname–geography Bayesian method and aggregated to the district–cycle level. |\\n| cand_A_all | Indicator (1/0) that at least one major‑party nominee (Democratic or Republican) in the district’s general election is identified as Asian, based on verified public sources and candidate records. |\\n| cand_B_all | Indicator (1/0) that at least one major‑party nominee in the district’s general election is identified as Black, based on verified public sources and candidate records. |\\n| cand_H_all | Indicator (1/0) that at least one major‑party nominee in the district’s general election is identified as Latino/Hispanic, based on verified public sources and candidate records. |\\n| dfe | Constant column equal to 1 for all rows, included for convenience in some analysis workflows; it carries no substantive information about districts or elections. |\\n\\nDataset Context:\\nThis panel assembles district–election year information for U.S. House general elections from 1980 through 2012. Monetary figures come from the DIME compilation of Federal Election Commission disclosures and related sources, limited to itemized individual contributions reported under federal law (generally contributions exceeding the cycle-level reporting threshold). Dollars are aggregated to the general-election period for candidates competing in a given district and cycle.\\n\\nTo characterize the ethnoracial composition of contributors, the data employ a widely used Bayesian surname–geography approach (implemented via the wru methodology). This procedure combines U.S. Census surname frequencies with donors’ geocoded locations (Census tract or, if unavailable, state) to estimate the probability that a donor belongs to an ethnoracial group; donors are then assigned to the most probable category, and contributions are summed to compute district-level shares for Asian, Black, and Latino/Hispanic contributors. These shares are fractions of total itemized individual dollars given to general-election candidates in the district; the remainder of contributions reflects donors in other categories.\\n\\nCandidate ethnoracial identity is coded from verified public records, including congressional caucus memberships and other documentation, and linked to each district’s major‑party general-election nominees. District identifiers reflect changes from decennial redistricting (e.g., around 1982, 1992, 2002, and 2012), so the same geographic area may appear under different labels across redistricting periods. Because federal disclosures only require itemization above the reporting threshold, very small donations are not included; all shares and totals therefore describe the universe of reported itemized individual contributions.\",\n \"columns\": {\n  \"cycle\": \"float64\",\n  \"district_final\": \"str\",\n  \"general_sharetotal_A_all\": \"float64\",\n  \"general_sharetotal_B_all\": \"float64\",\n  \"general_sharetotal_H_all\": \"float64\",\n  \"cand_A_all\": \"float64\",\n  \"cand_B_all\": \"float64\",\n  \"cand_H_all\": \"float64\",\n  \"dfe\": \"float64\"\n }\n}\n{\n \"id\": \"ref_complete\",\n \"rows\": 408,\n \"description\": \"How many of 2006 and 2008 each district has the outcome in\",\n \"columns\": {\n  \"district_final\": \"str\",\n  \"ncycles\": \"int64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 768,\n \"description\": \"District rows of 2006 and 2008: no Asian nominee then one (treated) or in neither (control), outcome observed in both\",\n \"columns\": {\n  \"cycle\": \"float64\",\n  \"district_final\": \"str\",\n  \"cand_A_all\": \"float64\",\n  \"cand_B_all\": \"float64\",\n  \"cand_H_all\": \"float64\",\n  \"general_sharetotal_A_all\": \"float64\",\n  \"a_pre\": \"float64\",\n  \"a_post\": \"float64\",\n  \"ncycles\": \"int64\",\n  \"Treated\": \"int64\",\n  \"Post\": \"int64\",\n  \"Treated_Post\": \"int64\"\n }\n}\n{\n \"id\": \"ref_post\",\n \"rows\": 401,\n \"description\": \"Each district's cand_A_all in 2008 (a_post)\",\n \"columns\": {\n  \"district_final\": \"str\",\n  \"a_post\": \"float64\"\n }\n}\n{\n \"id\": \"ref_pre\",\n \"rows\": 402,\n \"description\": \"Each district's cand_A_all in 2006 (a_pre)\",\n \"columns\": {\n  \"district_final\": \"str\",\n  \"a_pre\": \"float64\"\n }\n}\n\n# Question\nAmong districts observed in 2006 and 2008, how did the share of general-election contributions from Asian donors change when a district went from having no Asian major-party nominee to having one, relative to districts with no Asian nominee in either election?","response":"smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"general_sharetotal_A_all\", \"x\": [\"Treated_Post\"], \"controls\": [\"Treated\", \"Post\", \"cand_B_all\", \"cand_H_all\"], \"cluster\": \"district_final\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_f5ce3309","text":"smf.ols('general_sharetotal_A_all ~ Treated + Post + Treated:Post + cand_B_all + cand_H_all').fit(cov_type='cluster', groups=district_final) (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":"general_sharetotal_A_all","x":["Treated_Post"],"controls":["Treated","Post","cand_B_all","cand_H_all"],"cluster":"district_final"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"0e3f1c70f71132d3f54fad52d400a07aab0d93ca6a40b9b80c7d9a620da2c5e4","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["a3660da4e6ad8b587d8dabe2d7f091ba60cb166b2e8deef26f53635136f9b947"],"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":0.5319998599588871}}
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