{"call_id":"call_c356e6bc","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q112/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":"2026-10-15T17:11:02.158860Z","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\": \"dwiputri_the_corruption\",\n \"rows\": 945,\n \"description\": \"The existence of ambiguity in the study of the impact of corruption on economic growth, namely the grease the wheel hypothesis and sand the wheel hypothesis, triggered this research to look at the impact of corruption through another perspective, i.e. income inequality. This study seeks to understand the mutual influence between corruption and income inequality in Asian countries. The data is collected from a variety of sources, including the World Bank, Transparency International, and The Institute Worldwide Governance Index.\\n\\nDataset variables: \\n\\n- trade: Merchandise trade (% of GDP)\\n- id: Unique country identifier\\n- gini: GINI index (World Bank estimate)\\n- corr: corruption indicator\\n- country (str): Country name\\n- year: Year collected\\n- popgrow: Population growth (annual %)\\n- unemptot: Unemployment, total (% of total labor force) (national estimate)\\n- healthtot: Health expenditure, total (% of GDP)\\n- educsec: Gross enrolment ratio, secondary, both sexes (%)\\n- educprim: Gross enrollment ratio, primary, both sexes (%)\\n- gdpcapgrow: GDP per capita growth (annual %)\\n- gdpcapcons: GDP per capita (constant 2010 US$)\\n- cap: Gross fixed capital formation (% of GDP)\\n- capgrow: Gross fixed capital formation (annual % growth)\\n- expense: Expense (% of GDP)\\n- taxrev: Tax revenue (% of GDP)\\n- fdi: Foreign direct investment, net inflows (% of GDP)\\n- al_ethnic: Ethnic Fractionalization\\n- al_language: Language Fractionalization\\n- al_religion: Religion Fractionalization\\n- p_democ: Institutionalized Democracy\\n- ln_gdpcapcons: natural log of gdp per capita growth\\n- gdpcapcor: interaction between corruption and gdp per capita growth\\n- gov: Governance indicator\\\"\",\n \"columns\": {\n  \"trade\": \"float64\",\n  \"id\": \"float64\",\n  \"corr\": \"float64\",\n  \"gini\": \"float64\",\n  \"country\": \"str\",\n  \"year\": \"float64\",\n  \"popgrow\": \"float64\",\n  \"unemptot\": \"float64\",\n  \"healthtot\": \"float64\",\n  \"educsec\": \"float64\",\n  \"educprim\": \"float64\",\n  \"gdpcapgrow\": \"float64\",\n  \"gdpcapcons\": \"float64\",\n  \"cap\": \"float64\",\n  \"capgrow\": \"float64\",\n  \"expense\": \"float64\",\n  \"taxrev\": \"float64\",\n  \"fdi\": \"float64\",\n  \"al_ethnic\": \"float64\",\n  \"al_language\": \"float64\",\n  \"al_religion\": \"float64\",\n  \"p_democ\": \"float64\",\n  \"gov\": \"float64\",\n  \"ln_gdpcapcons\": \"float64\",\n  \"gdpcapcorr\": \"float64\"\n }\n}\n\n# Question\nDoes an increase in corruption lead to higher income inequality?","response":"causcibench_metadata_rule/1: iv\ninfer__iv_2sls({\"dataset\": \"dwiputri_the_corruption\", \"y\": \"gini\", \"endog\": [\"corr\"], \"instruments\": [\"al_ethnic\", \"al_language\", \"al_religion\"], \"exog\": [\"unemptot\", \"ln_gdpcapcons\", \"educprim\", \"educsec\", \"taxrev\", \"p_democ\", \"healthtot\", \"fdi\", \"gov\", \"cap\", \"popgrow\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_c356e6bc","text":"causcibench_metadata_rule/1: iv","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"dwiputri_the_corruption","y":"gini","endog":["corr"],"instruments":["al_ethnic","al_language","al_religion"],"exog":["unemptot","ln_gdpcapcons","educprim","educsec","taxrev","p_democ","healthtot","fdi","gov","cap","popgrow"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"bd0b9fa63eb984a11b150579a1d82e65af79487544dd97d0b062bf8e06089019","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["9c09ccd8107a695381ee9c9b163b4e757ca3570022def90ff15d1a7ddc755101"],"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":0.6255828775465488}}
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resíduos com os valores observados de corr.","formula":"","terms":[{"symbol":"n","value":83.0,"unit":"observações","note":""},{"symbol":"instrumentos","value":3.0,"unit":"","note":""}],"result":-14.961169281273891,"unit":""},{"text_pt":"Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":32.76110159981889,"unit":"","note":"diagonal da matriz de covariância"}],"result":5.72373144022489,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 70 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-14.961169281273891,"unit":"","note":""},{"symbol":"c","value":1.994437111771186,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":5.72373144022489,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.corr","value":4.0158866968054046,"flag":true,"note":"Wald F of the excluded instruments in the first stage for corr (heteroskedasticity-robust standard errors (HC1)); 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