{"call_id":"call_14ce067d","run_id":"20261015T171142Z-suite-causcibench_real-naive-d6494d/causcibench_real.q144/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":"2026-10-15T17:11:46.206762Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"naive-ols","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\": \"goldstein_lobbyists_earmark\",\n \"rows\": 1262,\n \"description\": \"The dataset assembles city-level information on lobbying, federal resource allocation, connectivity to Washington, DC, and local demographic, fiscal, and political context for 1,262 U.S. cities with populations over 25,000 between 1999 and 2012. It links lobbying disclosures filed under the Lobbying Disclosure Act to Census/ACS demographics, Census of Governments fiscal data, federal earmark records, and airline route information to study how variation in public-goods provision and political environment relates to cities’ lobbying behavior and the earmarks they receive.\\n\\nVariables in the dataset\\nid: Internal numeric identifier for each city in the replication dataset released with the article.\\ncity: Name of the city (municipal government) included in the sample of U.S. cities with population above 25,000.\\nstate: U.S. state in which the city is located, used to link to state-level fiscal and political data.\\ndirect_flight_dc: Indicator for whether there is at least one direct commercial flight between the city’s local (or nearby) airport and any of the Washington, DC–area airports (BWI, DCA, or IAD) in the relevant period, capturing the convenience of travel to the capital and used as an instrument for city lobbying expenditures.\\nearmark: Total dollar amount of federal congressional earmarks awarded to the city in fiscal years 2008 and 2009, typically summed across those years and expressed in constant (2012) dollars.\\ncity_lobbying: Total federal lobbying expenditures by the city government, based on Lobbying Disclosure Act reports, aggregated over the analysis window (e.g., around 2007–2009) and inflation-adjusted, with this variable serving as the core measure of city lobbying effort (often log-transformed in analysis).\\nstate_lobbying: Total lobbying expenditures by the state government over a comparable period, providing a measure of higher-level governmental lobbying that may complement or compete with city efforts.\\ncounty_lobbying: Total lobbying expenditures by the county government in which the city is located, used to capture additional substate lobbying that can affect how much federal funding ultimately flows to the city (and is found to be negatively related to city earmarks and grants).\\npop_e: City population, drawn from the Decennial Census and ACS and extrapolated to match the analysis years, providing a size control for lobbying and earmark outcomes.\\nland_e: Land area of the city (in square miles), as reported by Census geography files and used as a basic geographic control.\\nwater_e: Water area of the city (in square miles), capturing the extent of water within city boundaries, again from Census geography data.\\nsenior_e: Share of the city population aged 65 or older, constructed from Census/ACS age distributions and extrapolated to the analysis years.\\nstudent_e: Share of residents enrolled in school, capturing educational-age composition and demand for education-related public goods.\\nethnic_e: Index of ethnic heterogeneity (ethnic fractionalization) at the city level, typically constructed from Census/ACS racial and ethnic composition using a fractionalization formula where higher values indicate more ethnically diverse cities, which the paper links to public-goods provision and lobbying.\\nmincome_e: Median household income in the city (often in thousands of dollars), measured using Census/ACS and extrapolated to the relevant period as an indicator of overall economic status.\\nunemp_e: City unemployment rate, capturing local labor-market conditions that may influence fiscal stress and demand for federal resources.\\npoverty_e: Share of households in poverty in the city, reflecting economic disadvantage and potential demand for redistributive public goods.\\ngini_e: Gini coefficient of income inequality at the city level, measuring the extent of income dispersion and used in the paper as a key determinant of lobbying behavior.\\ncity_taxshare_e: Share of total city revenue coming from own-source taxes (summed across tax instruments), capturing how reliant the city is on its own tax base rather than external transfers.\\ncity_propertytaxshare_e: Share of total city revenue specifically derived from property taxes, reflecting dependence on the property tax base and related fiscal capacity.\\ncity_intgovrevenueshare_e: Share of total city revenue coming from intergovernmental transfers (state and federal), measuring how much of the city’s budget is financed by higher-level governments rather than local sources.\\nhoudem_e: Indicator that the city’s U.S. House representative (in the district containing the city) is a Democrat, based on congressional delegation data matched via county–district crosswalks.\\nsendem_e: Indicator that at least one of the state’s U.S. Senators representing the city is a Democrat, capturing partisan alignment with the federal legislature.\\ngovdem_e: Indicator that the state’s governor is a Democrat, capturing partisan control at the state executive level, which is theorized to affect state support for local public goods.\\ndiverge1_e: First measure of the “public goods gap” between city and state, defined as the difference between the city’s direct expenditures per capita and the corresponding state government’s direct expenditures per capita, capturing how much more (or less) the city spends than the state overall.\\ndiverge2_e: Second measure of the “public goods gap,” defined as the difference between the city’s direct expenditures per capita and the per capita state transfers to that city, measuring the portion of city spending not covered by state government support.\\nst_airexp_e: Per-capita state government expenditure on air transportation and related functions (from the Census of Governments), used as a state-level control related to air infrastructure that may correlate with the presence of airports and airline routes.\\ncity_airexp_e: Per-capita city expenditure on air transportation (e.g., airports and related infrastructure), providing a city-level measure of investment in air facilities and helping to account for baseline air-service conditions when using direct flights as an instrument.\",\n \"columns\": {\n  \"id\": \"float64\",\n  \"city\": \"str\",\n  \"state\": \"str\",\n  \"direct_flight_dc\": \"int64\",\n  \"earmark\": \"float64\",\n  \"city_lobbying\": \"float64\",\n  \"state_lobbying\": \"float64\",\n  \"county_lobbying\": \"float64\",\n  \"pop_e\": \"float64\",\n  \"land_e\": \"float64\",\n  \"water_e\": \"float64\",\n  \"senior_e\": \"float64\",\n  \"student_e\": \"float64\",\n  \"ethnic_e\": \"float64\",\n  \"mincome_e\": \"float64\",\n  \"unemp_e\": \"float64\",\n  \"poverty_e\": \"float64\",\n  \"gini_e\": \"float64\",\n  \"city_taxshare_e\": \"float64\",\n  \"city_propertytaxshare_e\": \"float64\",\n  \"city_intgovrevenueshare_e\": \"float64\",\n  \"houdem_e\": \"float64\",\n  \"sendem_e\": \"float64\",\n  \"govdem_e\": \"float64\",\n  \"diverge1_e\": \"float64\",\n  \"diverge2_e\": \"float64\",\n  \"st_airexp_e\": \"float64\",\n  \"city_airexp_e\": \"float64\"\n }\n}\n\n# Question\nHow much does the money spent on lobbying increase the number of earmarks received?","response":"submit_answer({\"answer\": 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