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{"call_id":"call_8f34f83a","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q003/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":"2026-10-15T17:00:51.327912Z","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_0002\",\n \"rows\": 3950,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| year | Calendar year of the observation (1936–2014). Each row represents a state-year in the panel. |\\n| Policy | Continuous, standardized index summarizing the ideological orientation of a state’s policy outputs in that year. The score is estimated from nearly 150 statutory and regulatory indicators spanning domains such as social welfare, taxation, labor, civil rights, women’s rights, morality laws, family planning, environment, religion, criminal justice, and drug policy. Higher values indicate a more liberal policy orientation. The scale is standardized to be comparable across states and years (centered roughly at 0 with a typical standard deviation near 1). |\\n| sen_dem_control | Indicator (0/1) that the Democratic Party held a majority of seats in the state senate (upper chamber) in that year, based on official election returns and chamber composition. For Nebraska’s nonpartisan unicameral legislature, this variable is generally coded as missing. |\\n| hs_dem_control | Indicator (0/1) that the Democratic Party held a majority of seats in the state house/lower chamber in that year, based on official election returns and chamber composition. For Nebraska, typically coded as missing due to nonpartisan unicameral structure. |\\n| Policy_L1 | One-year lag of the policy index (value from t−1). Included to capture persistence and gradualism in state policy change over time. |\\n| Policy_L2 | Two-year lag of the policy index (value from t−2). Provides an additional look-back to characterize medium-run policy momentum. |\\n| South11 | Indicator (0/1) for membership in the traditional 11-state U.S. South: AL, AR, FL, GA, LA, MS, NC, SC, TN, TX, VA. Useful for distinguishing long-standing regional political and institutional contexts. |\\n| gov_dem | Indicator (0/1) that the sitting governor was affiliated with the Democratic Party for the relevant portion of the calendar year, as compiled from official administrative records and election data. |\\n| gov_dem_X_hs_dem | Product term (0/1) equal to 1 when both gov_dem = 1 and hs_dem_control = 1 in the same year; 0 otherwise. Encodes concurrent Democratic control of the governorship and the lower chamber. |\\n| gov_dem_X_sen_dem | Product term (0/1) equal to 1 when both gov_dem = 1 and sen_dem_control = 1 in the same year; 0 otherwise. Encodes concurrent Democratic control of the governorship and the upper chamber. |\\n| both_dem | Indicator (0/1) that Democrats controlled both legislative chambers in that year (hs_dem_control = 1 and sen_dem_control = 1). |\\n| full_dem | Indicator (0/1) that Democrats held a “trifecta” in that year: governorship plus majorities in both legislative chambers. |\\n| state | Numeric state identifier for the panel unit. Codes map to U.S. states included in the panel; Nebraska’s unicameral, nonpartisan legislature is handled specially for chamber-control variables. |\\n\\nDataset Context:\\nThis dataset is an annual, state-year panel that tracks the ideological orientation of U.S. state policies alongside the party composition of key state institutions from the mid-1930s through the early 2010s. The central measure is a dynamic policy index that pools information from nearly 150 codings of enacted state statutes and regulatory provisions. The underlying source material includes historical statutory compilations, administrative records, and archival policy datasets, covering areas such as safety-net programs, taxation structures, labor and employment regulations, civil and voting rights, women’s rights, morality and family policy, environmental regulation, religion in public institutions, criminal justice, and drug laws. Because most policy indicators are dichotomous (present/absent) and some are continuous (e.g., benefit levels), the index is estimated using a modeling approach that accommodates mixed data types and allows comparability over time. Scores are standardized so that higher values represent more liberal policy bundles, facilitating interpretation across decades when the national policy environment shifted.\\n\\nParty control variables are constructed from state election returns and official chamber rosters, identifying which party held the governor’s office and whether Democrats held majorities in the state house and senate in each year. Nebraska’s unique nonpartisan unicameral legislature is typically excluded from chamber-majority coding or marked as missing for those fields. The dataset also includes composite indicators for concurrent control across branches (e.g., both chambers, or governorship plus both chambers) and simple product terms that register simultaneous control by party across institutions.\\n\\nCoverage spans 1936–2014, a period that includes major institutional realignments, changes in party competition, and significant waves of policy innovation at the state level. The inclusion of lagged versions of the policy index reflects the empirical regularity that state policy changes are often incremental and path dependent. A regional flag for the traditional 11-state South is provided to facilitate analyses that account for historically distinct political and legal environments in that region. Together, these features make the file a versatile panel for describing long-run patterns in state policy content and the composition of state political institutions.\",\n \"columns\": {\n  \"year\": \"int64\",\n  \"Policy\": \"float64\",\n  \"sen_dem_control\": \"float64\",\n  \"hs_dem_control\": \"float64\",\n  \"Policy_L1\": \"float64\",\n  \"Policy_L2\": \"float64\",\n  \"South11\": \"int64\",\n  \"gov_dem\": \"float64\",\n  \"gov_dem_X_hs_dem\": \"float64\",\n  \"gov_dem_X_sen_dem\": \"float64\",\n  \"both_dem\": \"float64\",\n  \"full_dem\": \"float64\",\n  \"state\": \"float64\"\n }\n}\n{\n \"id\": \"ref_g0\",\n \"rows\": 50,\n \"description\": \"Each state's binarised gov_dem in 1970\",\n \"columns\": {\n  \"state\": \"float64\",\n  \"g0\": \"float64\"\n }\n}\n{\n \"id\": \"ref_g1\",\n \"rows\": 50,\n \"description\": \"Each state's binarised gov_dem in 1971\",\n \"columns\": {\n  \"state\": \"float64\",\n  \"g1\": \"float64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 64,\n \"description\": \"State rows of 1970 and 1971 for states with gov_dem 0 in 1970 (switchers to 1 and stayers at 0)\",\n \"columns\": {\n  \"year\": \"int64\",\n  \"state\": \"float64\",\n  \"Policy\": \"float64\",\n  \"gov_dem\": \"float64\",\n  \"Policy_L1\": \"float64\",\n  \"Policy_L2\": \"float64\",\n  \"g0\": \"float64\",\n  \"g1\": \"float64\",\n  \"Post\": \"int64\",\n  \"TreatGroup\": \"int64\",\n  \"TreatGroup_Post\": \"int64\"\n }\n}\n\n# Question\nHow much did state policy liberalism change, by the governor's first year in office, in states that elected a Democratic governor in 1971 relative to comparable states that remained under Republican governors?","response":"smf.ols('Policy ~ TreatGroup*Post + Policy_L1 + Policy_L2'), cluster by state\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"Policy\", \"x\": [\"TreatGroup_Post\"], \"controls\": [\"TreatGroup\", \"Post\", \"Policy_L1\", \"Policy_L2\"], \"cluster\": \"state\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_8f34f83a","text":"smf.ols('Policy ~ TreatGroup*Post + Policy_L1 + Policy_L2'), cluster by state","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"Policy","x":["TreatGroup_Post"],"controls":["TreatGroup","Post","Policy_L1","Policy_L2"],"cluster":"state"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"243a63852ab5ae8cb3025614069153c822ab0af7141e30553f17356ab1ea5414","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["168a924426c018a4edc2037c1a06b5a3fa75030ad9fe6bcc217b8f8215cce1dd"],"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":9.201833978295326}}
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