{"call_id":"call_7dd90dba","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q070/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":"2026-10-15T17:10:29.284114Z","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\": \"ds_0040\",\n \"rows\": 1289,\n \"description\": \"| Variable Name | Description |\\n|---------------|-------------|\\n| (Unnamed) | Leading CSV observation index / row identifier imported with the file (simple sequential identifier for each row). |\\n| statea | Numeric country identifier used to label each country observation in the panel (author/internal country code). |\\n| log_HRVOTE | Natural logarithm of HRVOTE, a constructed measure of foreign-policy voting similarity/convergence (higher values indicate greater alignment as operationalized in the study). |\\n| log_tot_trade | Natural logarithm of total bilateral trade (exports + imports) between China and the country in a given year (trade volume). |\\n| statea_trade_gdp100 | Bilateral trade with China expressed as a percentage of the country’s GDP (scaled by 100). |\\n| Joint_Dem_Dum | Binary indicator (0/1) denoting whether the dyad is jointly classified as democracies in that year (1 = both democracies, 0 = otherwise). |\\n| log_cinc | Natural logarithm of the country’s Composite Index of National Capability (CINC) — a standard measure of material national capabilities. |\\n| log_tot_ustrade | Natural logarithm of total bilateral trade between the country and the United States for the given year. |\\n| us_aid100 | Amount of U.S. bilateral aid received by the country in that year, scaled by a factor of 100 (as provided in the replication dataset). |\\n| dummy2004 | Indicator variable equal to 1 for observations from the year 2004 and 0 otherwise (year-specific flag). |\\n| pts_score | Numeric PTS score included by the authors to capture a policy/preference-related index (operationalization and scale are described in the paper). |\\n| lag_log_energ_prod | One-period lag of the natural logarithm of the country’s energy production (e.g., national oil/energy output in the prior year). |\\n\\nData Description: This is a country-year panel assembled for the study period referenced in the paper (1992–2006) that combines bilateral economic statistics and country-level indicators for countries in Africa and Latin America. The dataset merges logged trade flows with measures derived from diplomatic behavior, state capability indices, trade shares relative to GDP, regime-type indicators, U.S. trade and aid flows, and lagged energy-production figures; many variables are log-transformed or scaled for comparability. Source material derives from standard international datasets and author-constructed series (trade statistics, capability indexes, voting/preference measures, aid records and national energy statistics), and the provided file has been pre-processed to a country-year format with the identifiers and derived variables noted above.\\n\",\n \"columns\": {\n  \"statea\": \"float64\",\n  \"log_HRVOTE\": \"float64\",\n  \"log_tot_trade\": \"float64\",\n  \"statea_trade_gdp100\": \"float64\",\n  \"Joint_Dem_Dum\": \"float64\",\n  \"log_cinc\": \"float64\",\n  \"log_tot_ustrade\": \"float64\",\n  \"us_aid100\": \"float64\",\n  \"dummy2004\": \"float64\",\n  \"pts_score\": \"float64\",\n  \"lag_log_energ_prod\": \"float64\"\n }\n}\n\n# Question\nWhat is the causal effect of changes in bilateral trade volume with China (log_tot_trade) on a country's foreign-policy voting alignment (log_HRVOTE) at the country-year level?","response":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.\ninfer__ols({\"dataset\": \"ds_0040\", \"y\": \"log_HRVOTE\", \"x\": [\"log_tot_trade\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_7dd90dba","text":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ds_0040","y":"log_HRVOTE","x":["log_tot_trade"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"e63cd30eac6db9b9417c0a9ab7e98972c6a6995c770f52182378d1c9b076244c","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["4c8855efbc7c2d057b62db6ce048bef8323d5878ff130809a23c5ab23cb8e581"],"config":{}},"attempts":[],"response_id":null,"finish_reason":"tool_use","model_reported":"naive-ols","usage":{"input":0,"output":0,"thoughts":0,"cached":0},"thoughts":null,"vendor":false,"wall_ms":0.5560838617384434}}
{"call_id":"call_1c6f1f4a","run_id":"20261015T171027Z-suite-crb_papers-naive-54c3f2/crb_papers.q070/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"ds_0040","y":"log_HRVOTE","x":["log_tot_trade"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"886041bdca6bcd796aeefc9ea68a60c4b440343cb6e34f72840b0f2007ffd86f","dataset_digests":{"ds_0040":"17c907548f4d1971c15de5e30889498802b105a8ffcf771becbd2a44fc0c05ba"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_1c6f1f4a","status":"ok","reason":"","figures":[{"key":"coef.log_tot_trade","value":-0.0030928450771122583,"unit":"","label":"Coefficient on log_tot_trade in the regression of log_HRVOTE","se":0.002913298261388725,"ci_low":-0.008809909041123585,"ci_high":0.002624218886899068,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de log_tot_trade na regressão por mínimos quadrados ordinários de log_HRVOTE.","formula":"","terms":[{"symbol":"n","value":976.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":-0.0030928450771122583,"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":8.487306759810567e-6,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.002913298261388725,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 974 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.0030928450771122583,"unit":"","note":""},{"symbol":"c","value":1.9624025592512073,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.002913298261388725,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":976,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.0013691880040898052,"flag":false,"note":""},{"name":"df_resid","value":974,"flag":false,"note":""},{"name":"se_type","value":"HC1","flag":false,"note":"heteroskedasticity-robust standard errors (HC1)"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":0.43895306387439154,"se":0.012707627816150298,"t":34.54248662496395,"p":2.4199451124464376e-171,"ci_low":0.4140155825259664,"ci_high":0.4638905452228167},{"term":"log_tot_trade","coef":-0.0030928450771122583,"se":0.002913298261388725,"t":-1.061630083710669,"p":0.28866681128037686,"ci_low":-0.008809909041123585,"ci_high":0.002624218886899068}]},"datasets":{},"warnings":["313 rows were dropped because one of 'log_HRVOTE', 'log_tot_trade' was missing"],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"a702c6dbf253ef30869df83ef0f9c73f8f4e069c7fbb897ea0f624ca4daa3bc9","params_versions":[],"sources":[],"started_at":"2026-10-15T17:10:29.286865Z","duration_ms":4.535958869382739,"error":null,"decided_by":"model","rationale":"Linha de base ingênua: regressão do resultado no tratamento, sem controles.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_7dd90dba","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"fc177bcd4574cf59b5df7e42f86baaf7c3f7634ff102929674de450c3a1ace4a"}}
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