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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 Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers.\n\n# Datasets\nPass a dataset's id as the `dataset` argument.\n{\n \"id\": \"ds_0001\",\n \"rows\": 3186,\n \"description\": \"| Variable Name | Description |\\n| --- | --- |\\n| dv | Natural log of total annual exploration spending on non‑fuel minerals (USD, constant 1997) plus one, aggregated to the country–year. Figures come from SNL Metals & Mining’s compilation of company surveys and reports and cover activities such as drilling, geophysics, sampling, assaying, and feasibility studies for metals and minerals (e.g., copper, gold, diamonds, iron ore, silver, uranium). Values are deflated to 1997 dollars and summed across firms within each country and year; the log(+1) form preserves observations with zero reported spending. |\\n| ucdp_lead | Indicator equal to 1 if the UCDP Georeferenced Event Dataset records at least one fatal armed conflict event within the country in the current calendar year or the previous year; 0 otherwise. UCDP defines events as organized use of armed force resulting in at least one direct death and classifies state‑based, one‑sided, and non‑state violence. |\\n| country | Country or territory corresponding to the observation. The panel spans a broad global sample (about 177 countries). |\\n| year | Calendar year of the observation (1997–2014). Stored numerically; represents the Gregorian year. |\\n\\nDataset Context:\\nThis global panel links country–year exploration budgets in the mining sector with standardized information on the presence of armed conflict. Exploration spending is compiled by SNL Metals & Mining from company surveys and public financial reports, then aggregated from firm-level entries to the country–year level. The series covers commercially oriented exploration for non‑fuel minerals—such as base and precious metals, diamonds, iron ore, rare earths, and uranium—and excludes hydrocarbons. Expenditures typically encompass early‑stage and appraisal activities (e.g., drilling campaigns, geophysical surveys, sampling and assaying, scoping and feasibility studies). SNL’s process captures the vast majority of global nonferrous exploration outlays and excludes very small projects below common reporting thresholds; amounts are converted to constant 1997 USD to remove the effects of inflation. Because many country–years legitimately report zero exploration, the spending measure is stored as ln(expenditure + 1).\\n\\nConflict information is sourced from the Uppsala Conflict Data Program’s georeferenced event data, which catalogs individual incidents where organized actors use armed force resulting in at least one direct fatality. Events include state‑based battles, one‑sided violence against civilians, and non‑state clashes. For each country–year, the dataset flags whether any fatal incident occurred in that year or the year immediately prior, capturing near‑term conflict conditions that overlap with annual financial reporting cycles.\\n\\nThe 1997–2014 window spans substantial swings in global metals prices and investment appetites, including the mid‑2000s commodity upcycle and subsequent downturn, and includes both “junior” and “major” mining companies whose exploration budgets respond to financing conditions and market sentiment. The resulting dataset provides harmonized, inflation‑adjusted coverage of exploration activity across roughly 177 countries aligned with a consistent, event‑based record of armed conflict incidence.\",\n \"columns\": {\n  \"dv\": \"float64\",\n  \"ucdp_lead\": \"int64\",\n  \"country\": \"str\",\n  \"year\": \"float64\"\n }\n}\n{\n \"id\": \"ref_panel\",\n \"rows\": 214,\n \"description\": \"The script's two-period panel: one pre and one post row per country with d_pre = 0, treated when d_post > 0\",\n \"columns\": {\n  \"country\": \"str\",\n  \"post\": \"int64\",\n  \"d_post\": \"float64\",\n  \"d_pre\": \"float64\",\n  \"treated\": \"int64\",\n  \"y\": \"float64\",\n  \"interaction\": \"int64\"\n }\n}\n{\n \"id\": \"ref_post\",\n \"rows\": 177,\n \"description\": \"Country means over 2000-2002\",\n \"columns\": {\n  \"country\": \"str\",\n  \"y1\": \"float64\",\n  \"d_post\": \"float64\"\n }\n}\n{\n \"id\": \"ref_pre\",\n \"rows\": 177,\n \"description\": \"Country means of dv and the binarised ucdp_lead over 1997-1999\",\n \"columns\": {\n  \"country\": \"str\",\n  \"y0\": \"float64\",\n  \"d_pre\": \"float64\"\n }\n}\n\n# Question\nFor countries that newly experienced at least one fatal armed conflict in 2000-2002, how did the log of annual non-fuel mineral exploration investment change relative to countries that remained conflict-free in both 1997-1999 and 2000-2002?","response":"sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country\ninfer__ols({\"dataset\": \"ref_panel\", \"y\": \"y\", \"x\": [\"interaction\"], \"controls\": [\"treated\", \"post\"], \"cluster\": \"country\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_9a6293da","text":"sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__ols","tool_id":"infer.ols","args":{"dataset":"ref_panel","y":"y","x":["interaction"],"controls":["treated","post"],"cluster":"country"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"bca0691f3712ae542ed27a8dbe46580f0b3887dc74b4f4ce337c9e71c6f0e514","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["08248f5e93af52048a7e72fd68ea872012275580941d216c7d23111707545d4e"],"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":0.5093330983072519}}
{"call_id":"call_b49deb7a","run_id":"20261015T170050Z-suite-crb_papers-scripted-8b13b2/crb_papers.q001/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"ref_panel","y":"y","x":["interaction"],"controls":["treated","post"],"fe":[],"cluster":"country","se":"HC1","weights":null,"subset":null,"intercept":true,"level":0.95},"input_digest":"3b6e19dcd8932dbfac8d7ddc6c65ce27423f0853ab5cdbd69c6acae48f3e2c43","dataset_digests":{"ref_panel":"2b9356ec0b9bde6fad91668b9d998d6f995d46c83231f41522dcf118f76c42fa"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_b49deb7a","status":"ok","reason":"","figures":[{"key":"coef.interaction","value":-0.0834813334522015,"unit":"","label":"Coefficient on interaction in the regression of y","se":0.4171809476066827,"ci_low":-0.9105831378379473,"ci_high":0.7436204709335442,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de interaction na regressão por mínimos quadrados ordinários de y.","formula":"","terms":[{"symbol":"n","value":214.0,"unit":"observações","note":""},{"symbol":"k","value":4.0,"unit":"parâmetros","note":""}],"result":-0.0834813334522015,"unit":""},{"text_pt":"Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.17403994304600973,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.4171809476066827,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 106 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-0.0834813334522015,"unit":"","note":""},{"symbol":"c","value":1.9825972617655003,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.4171809476066827,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":214,"flag":false,"note":"observations used"},{"name":"k","value":4,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.054860876876529185,"flag":false,"note":""},{"name":"df_resid","value":210,"flag":false,"note":""},{"name":"se_type","value":"cluster","flag":false,"note":"cluster-robust standard errors (CR1) by 'country'"},{"name":"n_clusters","value":107,"flag":false,"note":"107 clusters of 'country'"}],"tables":{"coefficients":[{"term":"Intercept","coef":7.853694688021908,"se":0.7509532837457076,"t":10.458299947565546,"p":5.168148451502389e-18,"ci_low":6.364856763953857,"ci_high":9.342532612089958},{"term":"interaction","coef":-0.0834813334522015,"se":0.4171809476066827,"t":-0.2001082118709494,"p":0.8417793598356013,"ci_low":-0.9105831378379473,"ci_high":0.7436204709335442},{"term":"treated","coef":6.8186295100508625,"se":2.459012651147964,"t":2.7729135540915815,"p":0.006565168162864022,"ci_low":1.9433977612381854,"ci_high":11.693861258863539},{"term":"post","coef":-0.8910653177330753,"se":0.358070386901932,"t":-2.488519995866398,"p":0.014383952558234266,"ci_low":-1.600974686324159,"ci_high":-0.18115594914199162}]},"datasets":{},"warnings":[],"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across clusters of 'country' and may be correlated within them"],"notes":""},"outputs":null,"output_digest":"33791b2c0af0ef5f8a1c12edae347630c95bf71c16429caf684dc09ec725fb5b","params_versions":[],"sources":[],"started_at":"2026-10-15T17:00:51.340738Z","duration_ms":20.718791987746954,"error":null,"decided_by":"model","rationale":"sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_9a6293da","llm_name":"infer__ols","vendor_id":"script_1","result_for_model_ref":"d78935fc7c3ee95c7c8442060f5725c4d1394b391e2c621e95784a03ddf32ef1"}}
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