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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 Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"ds_0010\",\n \"rows\": 1695,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| county_id | Numeric identifier for a North Carolina county (1–100). Stable across years. |\\n| year | Calendar year of the observation (1958–1993). Not all measures are available every year. |\\n| pop_t_i_ln | Natural logarithm of total county resident population. Population counts come from the U.S. Census via NHGIS and are linearly interpolated for non-decennial years. |\\n| pop_b_i_perc | Share of the county’s total population that is Black (0–1). Based on U.S. Census counts via NHGIS, linearly interpolated between decennial enumerations. |\\n| inc_hh_med_i | Median household income for the county, as reported in Census data via NHGIS. Values for non-decennial years are linearly interpolated; units reflect source-year dollars. |\\n| educ_25p_i | Median years of schooling among county residents age 25 and older. Derived from U.S. Census data via NHGIS and linearly interpolated for non-decennial years. |\\n| p_vreg_w | White voter registration as a share of the white voting-age population (0–1+, where values can exceed 1.0 due to unpurged names on voter rolls). Compiled from state and federal administrative sources. |\\n| p_vreg_bnw | Black voter registration as a share of the Black voting-age population (0–1+, where values can exceed 1.0 due to unpurged names on voter rolls). Compiled from state and federal administrative sources. |\\n| post_x_covered | Indicator equal to 1 for county-years from 1966 onward in counties subject to Section 5 preclearance of the Voting Rights Act; 0 otherwise. North Carolina had partial county coverage (40 of 100 counties), determined using a federal turnout benchmark from the 1964 election. |\\n| tag_vreg_bnw | Auxiliary tag associated with the Black registration series. Coding details are not specified in this file; treat as a metadata flag accompanying p_vreg_bnw. |\\n\\nDataset Context:\\nThis file is a county–year panel for North Carolina covering 1958–1993. It brings together historical measures of voter registration by race with core county demographics to document changes in political participation over a period spanning the passage and implementation of the Voting Rights Act of 1965 (VRA).\\n\\nRegistration by race prior to the mid-1960s is available for 1958 and 1960 from the U.S. Commission on Civil Rights. After the VRA, registration figures were compiled from the North Carolina State Board of Elections, Inter-university Consortium for Political and Social Research (ICPSR) series, and the Voter Education Project. Post-1965 registration is observed in 1966–1968, then generally every two years from 1970 through 1988, with additional coverage in 1971 and 1993. Registration measures use the voting-age population (VAP) of the corresponding racial group as the denominator. Because county rolls were not always purged of deceased or migrated individuals (“deadwood”), the registration share can occasionally exceed 100%.\\n\\nDemographic measures—total population (logged), racial composition, median household income, and median years of schooling among adults 25+—are drawn from the U.S. Census via NHGIS. To provide annual coverage, values are linearly interpolated between decennial census years. Units for income reflect source-year dollars, and education is recorded in years.\\n\\nThe period also spans a major policy environment: Section 5 of the VRA required certain jurisdictions to obtain federal approval (“preclearance”) for changes to voting practices. North Carolina was unique among the initially covered states in having partial county coverage—40 of its 100 counties—based on a federally applied turnout benchmark from the 1964 election. The dataset includes an indicator flagging county-years after 1965 in covered counties to reflect when preclearance was in effect. Overall, the panel structure allows users to observe evolving registration patterns by race alongside demographic shifts under this policy regime, with variable availability driven by the timing of administrative releases and census collections.\",\n \"columns\": {\n  \"county_id\": \"float64\",\n  \"year\": \"float64\",\n  \"pop_t_i_ln\": \"float64\",\n  \"pop_b_i_perc\": \"float64\",\n  \"inc_hh_med_i\": \"float64\",\n  \"educ_25p_i\": \"float64\",\n  \"p_vreg_w\": \"float64\",\n  \"p_vreg_bnw\": \"float64\",\n  \"post_x_covered\": \"float64\",\n  \"tag_vreg_bnw\": \"float64\"\n }\n}\n{\n \"id\": \"ref_cov\",\n \"rows\": 100,\n \"description\": \"Each county's coverage: the max of post_x_covered over all its rows\",\n \"columns\": {\n  \"county_id\": \"float64\",\n  \"covered\": \"float64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 200,\n \"description\": \"County rows of 1960 and 1970 with p_vreg_bnw, counties observed in both years\",\n \"columns\": {\n  \"county_id\": \"float64\",\n  \"year\": \"float64\",\n  \"post_x_covered\": \"float64\",\n  \"p_vreg_bnw\": \"float64\",\n  \"covered\": \"float64\",\n  \"nyears\": \"int64\",\n  \"post\": \"int64\",\n  \"did\": \"float64\"\n }\n}\n{\n \"id\": \"ref_years\",\n \"rows\": 100,\n \"description\": \"How many of 1960 and 1970 each county is observed in with p_vreg_bnw\",\n \"columns\": {\n  \"county_id\": \"float64\",\n  \"nyears\": \"int64\"\n }\n}\n\n# Question\nBy how many percentage points did Section 5 preclearance coverage change Black voter registration (share of the Black voting-age population) in covered North Carolina counties from 1960 to 1970 compared with uncovered counties?","response":"sm.OLS(p_vreg_bnw, add_constant([covered, post, did])).fit(cov_type='cluster', groups=county_id) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"p_vreg_bnw\", \"x\": [\"did\"], \"controls\": [\"covered\", \"post\"], \"cluster\": \"county_id\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_83e59ad4","text":"sm.OLS(p_vreg_bnw, add_constant([covered, post, did])).fit(cov_type='cluster', groups=county_id) (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":"p_vreg_bnw","x":["did"],"controls":["covered","post"],"cluster":"county_id"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"385f78755fb3e71de9ec5de621971a2a140683965feff47112eddf311f3ebdd4","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["c269c8d3775607be3c97d1c4676e5a439896414a436c3ad3a4a9599c65393aa0"],"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":0.63699996098876}}
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