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{"call_id":"call_9f6754bb","run_id":"20261015T170925Z-suite-crb_papers-scripted-2dbcf5/crb_papers.q058/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":"2026-10-15T17:09:29.421791Z","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_0030\",\n \"rows\": 902,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| party | Member’s party identification using standard congressional numeric codes (e.g., 100 = Democrat, 200 = Republican; other parties/independents use their conventional codes). Party is measured for the member serving in that Congress (two-year House term). |\\n| correct | Civil-rights roll-call support score for the member in that Congress (two-year House term). Constructed as the proportion (0–1) of included civil-rights–related House roll calls on which the member voted in the direction coded as expanding (rather than restricting) civil rights; abstentions are treated as votes to restrict civil rights. The set of roll calls varies by Congress. |\\n| dpres | District presidential partisanship: Democratic presidential vote share for the most recent presidential election prior to (or associated with) the Congress observation. Higher values indicate greater support for the Democratic presidential candidate (scale follows the replication file’s coding/transformation of election returns). |\\n| percentblack | Share of the congressional district population that is Black (0–1), aligned to district boundaries within redistricting cycles using decennial census data and standard crosswalk/harmonization to congressional districts. |\\n| competitive | Electoral competitiveness measure based on the margin of victory in the most recent presidential election in the district, multiplied by −1 so that larger values correspond to closer (more competitive) contests. Scale reflects the transformation used in the replication files. |\\n| covered_new2 | Indicator (1/0) that the congressional district contained any geographic overlap with a jurisdiction subject to Section 5 preclearance during that Congress. Coverage is based on the list of covered states/counties/townships and subsequent changes due to statutory amendments and court actions (including “bail-in” and “bail-out”). Time-varying at the district–Congress level. |\\n| black_legis | Indicator (1/0) that the district’s U.S. House member was Black in that Congress, based on congressional rosters/biographical sources. |\\n| redistrict_decade | District identifier that is stable within a redistricting cycle (roughly a decade). This ID is intended to allow districts to be tracked consistently within-cycle despite renumbering or boundary changes across cycles. |\\n| year3 | Ordered analysis-period index taking values 1 through 5. Use it with redistrict_decade to order observations within a district-cycle. The extract does not retain calendar years, Congress numbers, or a separate redistricting-cycle label; an index value must not be interpreted as a unique calendar Congress across the whole study. Periods named in the questions refer to these recorded index values. |\\n| treatment | Inverse-coded coverage indicator: 1 means not covered by Section 5 preclearance and 0 means covered. In every row, treatment = 1 - covered_new2. Either existing column determines coverage, provided its coding is respected; gaining coverage means covered_new2 changes from 0 to 1, or treatment changes from 1 to 0. Neither column is an ever-covered flag. |\\n\\nDataset Context:\\nThis file contains 902 district-period observations for 261 district-cycle identifiers, with at most five ordered analysis periods per identifier. It is an unbalanced extract: some units appear in only one period. The source analysis for this extract concerns Texas and the Deep South states of Alabama, Georgia, Louisiana, Mississippi, and South Carolina. It is not the full national panel. The parent collection covers House terms from 1959–60 through 1997–98, but this extract does not preserve the calendar Congress labels needed to date each row. Questions therefore specify windows using year3 rather than asserting calendar dates.\\n\\nThe observational unit is a congressional district in a House term, with districts tracked inside redistricting cycles using redistrict_decade. The parent collection includes representatives who served the full Congress in single-member districts. Do not join different redistrict_decade identifiers as if their district boundaries were unchanged. Missing periods are absent observations, not zeros or evidence that a district was uncovered.\\n\\nLegislative behavior is summarized with a civil-rights support score. To build it, House roll-call votes were identified from policy agenda categories focused on civil rights, minority issues, and voting rights, and votes not directly pertinent to these topics were excluded. For each Congress-member pair, the score is the proportion of included roll calls on which the member voted in the direction coded as expanding civil rights, yielding a value between 0 and 1.\\n\\nJurisdictional coverage under the Voting Rights Act’s Section 5 preclearance provision is mapped to congressional districts by geographic overlap. Coverage operated at the state, county, or township level and changed over time due to statutory amendments and court actions, including “bail-in” (placing additional jurisdictions under review) and “bail-out” (removing jurisdictions meeting statutory criteria). A district is flagged as covered in a given Congress if any part of it overlapped a covered jurisdiction during that period.\\n\\nDistrict context draws on administrative sources. Demographic composition, including the share of the population that is Black, comes from the U.S. Census, harmonized to congressional boundaries within each redistricting cycle. Presidential election returns are used to compute district-level Democratic vote strength and a competitiveness proxy based on recent presidential margins. Member characteristics, including party codes and whether the representative is Black, come from standard congressional rosters and roll-call archives.\\n\\nThe observed coverage histories include districts covered throughout a window, districts moving into coverage, and districts moving out of coverage. Six district-cycle units move from uncovered in period 1 to covered in period 2 and remain covered in period 3. There are no units observed in both periods 1 and 2 that remain uncovered in both. The questions identify their respective observation windows and use districts covered throughout those windows as the comparison population. These recorded histories describe the available data; they do not by themselves establish that the groups would have followed the same outcome trends.\\n\",\n \"columns\": {\n  \"party\": \"int64\",\n  \"correct\": \"float64\",\n  \"dpres\": \"float64\",\n  \"percentblack\": \"float64\",\n  \"competitive\": \"float64\",\n  \"covered_new2\": \"float64\",\n  \"black_legis\": \"float64\",\n  \"redistrict_decade\": \"float64\",\n  \"year3\": \"float64\",\n  \"treatment\": \"float64\"\n }\n}\n{\n \"id\": \"ref_c1\",\n \"rows\": 217,\n \"description\": \"covered_new2 of each district-cycle in analysis period 1, as c1\",\n \"columns\": {\n  \"redistrict_decade\": \"float64\",\n  \"c1\": \"float64\"\n }\n}\n{\n \"id\": \"ref_c2\",\n \"rows\": 211,\n \"description\": \"covered_new2 of each district-cycle in analysis period 2, as c2\",\n \"columns\": {\n  \"redistrict_decade\": \"float64\",\n  \"c2\": \"float64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 392,\n \"description\": \"Rows of periods 1 and 2 of district-cycles gaining coverage after period 1 or covered throughout the window, observed in both\",\n \"columns\": {\n  \"redistrict_decade\": \"float64\",\n  \"year3\": \"float64\",\n  \"correct\": \"float64\",\n  \"covered_new2\": \"float64\",\n  \"c1\": \"float64\",\n  \"c2\": \"float64\",\n  \"nyears\": \"int64\",\n  \"gained_coverage\": \"int64\",\n  \"post\": \"int64\",\n  \"gained_post\": \"int64\"\n }\n}\n{\n \"id\": \"ref_years\",\n \"rows\": 228,\n \"description\": \"How many of periods 1 and 2 each district-cycle has a non-missing correct in\",\n \"columns\": {\n  \"redistrict_decade\": \"float64\",\n  \"nyears\": \"int64\"\n }\n}\n\n# Question\nFor districts that moved from no Section 5 preclearance coverage in analysis period 1 to coverage in period 2, how did gaining coverage affect the representative's civil-rights support score between periods 1 and 2, relative to districts covered in both periods?","response":"smf.ols('correct ~ gained_coverage * post').fit(cov_type='cluster', groups=redistrict_decade)\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"correct\", \"x\": [\"gained_post\"], \"controls\": [\"gained_coverage\", \"post\"], \"cluster\": \"redistrict_decade\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9f6754bb","text":"smf.ols('correct ~ gained_coverage * post').fit(cov_type='cluster', groups=redistrict_decade)","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"correct","x":["gained_post"],"controls":["gained_coverage","post"],"cluster":"redistrict_decade"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"b4a8030c51680995626b743e2aaf787af776d5055d7246af82e76ae2777d8021","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["85f02bfddc6fa913f464f05fb1db16d99762893d06b9fab50711bf2ed1b5c3aa"],"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":0.7091250736266375}}
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