{"call_id":"call_c1f6af88","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q150/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":"2026-10-15T17:11:03.844639Z","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\": \"reinsberg_does_earmarked\",\n \"rows\": 64,\n \"description\": \"The dataset compiles performance evaluations of 32 international organizations (IOs) from 2009 to 2021, encompassing 64 assessments conducted by the Multilateral Organization Performance Assessment Network (MOPAN). Organizations vary significantly in their funding composition, with some receiving predominantly core contributions that provide operational flexibility, while others rely heavily on earmarked resources that restrict fund usage to specific purposes, sectors, or geographic areas. Due to their specific nature, earmarked funding does not offer flexibility. Thus, there is a possibility that earmarked funding may limit organizational performance. The dataset tracks both short-term and long-term earmarked funding patterns alongside core funding levels. International organizations' performance is given by two metrics: 1) Process performance, the extent to which organizations have rules to plan and manage operations strategically, and 2) outcome performance, which measures the organization's success in achieving desired results.\\n\\nVariables:\\norganization: international organization name\\nacronym: organization abbreviation\\nyear: assessment year\\nmopan: The version of MOPAN methodology used in the assessment (one of 3.0, 3.1, or 3.0*, CA = older version)\\nnewmopan: Binary variable that is 1 if the new MOPAN assessment methodology was used\\ny1: process performance factor score\\ny2: outcome performance factor score\\ny1n: process performance factor score after removing 2 micro indicators\\ny2n: outcome performance factor score after removing 2 micro indicators\\nEMST: earmarked funding share in the year before MOPAN assessment (t-1 year)\\nEMLT: earmarked funding share in the years t-2 and t-3 before MOPAN assessment, i.e., 2 and 3 years before\\nun: indicator for UN system organizations (1 = UN organization, 0 = non-UN organization)\\ncoreSTd: non-core funding amount\\ncoreST: core funding amount\\nemST: earmarked funding amount\\nemST_MBI: earmarked funding amount computed by MOPAN\\nemshareLTd: Share of earmarked funding in the IO budget based on data from UN DESA Data\\nemshareLTb: Share of earmarked funding in the IO budget based on data from UN Data Cube\\nemshareLT: Share of earmarked funding share for long-term projects\\nnproj_lt: number of earmarked projects\\nndon_lt: number of donors providing earmarked funding\\nstates_num: number of member states\\nstates_unga_sd: standard deviation of the preferences of the member states with respect to ideal point estimates\\ntenure: executive head tenure length\\nselectorate: Variable indicating how the executive is selected (1 = appointed by the chief of parent organization, 2 = selected by the board, 3 = chosen by the assembly of member states)\\nfounding: organization founding year\\nhq: headquarters location\\nfield_presence: number of staff in the field\\nstaff_count: total staff size\\nnormio: indicator for norm-making IOs: FAO, ILO, UNESCO, UNHCR, UNIDO, WHO, GEF\\nlnsectors: logarithm of the number of sectors in which the organization receives donor funding\\nmeanwSIM: weighted mean cosine similarity of sector-specific contribution profile, which is proportional to the total budget of the IOs\",\n \"columns\": {\n  \"organization\": \"str\",\n  \"acronym\": \"str\",\n  \"year\": \"int64\",\n  \"mopan\": \"str\",\n  \"newmopan\": \"int64\",\n  \"y1\": \"float64\",\n  \"y2\": \"float64\",\n  \"y1n\": \"float64\",\n  \"y2n\": \"float64\",\n  \"EMST\": \"float64\",\n  \"EMLT\": \"float64\",\n  \"un\": \"int64\",\n  \"coreSTd\": \"float64\",\n  \"coreST\": \"float64\",\n  \"emST\": \"float64\",\n  \"emST_MBI\": \"float64\",\n  \"emshareLTd\": \"float64\",\n  \"emshareLTb\": \"float64\",\n  \"emshareLT\": \"float64\",\n  \"nproj_lt\": \"int64\",\n  \"ndon_lt\": \"int64\",\n  \"states_num\": \"int64\",\n  \"states_unga_sd\": \"float64\",\n  \"tenure\": \"str\",\n  \"selectorate\": \"int64\",\n  \"founding\": \"int64\",\n  \"hq\": \"str\",\n  \"field_presence\": \"int64\",\n  \"staff_count\": \"int64\",\n  \"normio\": \"int64\",\n  \"lnsectors\": \"float64\",\n  \"meanwSIM\": \"float64\"\n }\n}\n\n# Question\nHow much does earmarked funding affect the performance of international organisations?","response":"causcibench_metadata_rule/1: iv\ninfer__iv_2sls({\"dataset\": \"reinsberg_does_earmarked\", \"y\": \"y1\", \"endog\": [\"EMST\"], \"instruments\": [\"meanwSIM\"], \"exog\": [\"newmopan\"]})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_c1f6af88","text":"causcibench_metadata_rule/1: iv","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"reinsberg_does_earmarked","y":"y1","endog":["EMST"],"instruments":["meanwSIM"],"exog":["newmopan"]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"e74b16c2067be13213bc9ce2aeb0c7a533948d658af66c3e975ccf1984e494b6","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["68fe3e07e05c327db1772c140c8d58e12503623e1ad1c35d84b3b2b8a327d136"],"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":0.9129580575972795}}
{"call_id":"call_fd8a6dff","run_id":"20261015T171059Z-suite-causcibench_real-scripted-ded01d/causcibench_real.q150/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"2925aeab2262","inputs":{"dataset":"reinsberg_does_earmarked","y":"y1","endog":["EMST"],"instruments":["meanwSIM"],"exog":["newmopan"],"cluster":null,"se":"HC1","subset":null,"level":0.95},"input_digest":"0b5cd4ae5bd255d22fb66158b016516dd54dd4165e5a5472cc2a76b0bc5fe89c","dataset_digests":{"reinsberg_does_earmarked":"5e2cf774bc4e473b2623db2b5c2e832456bd6da928a073ef89f69de82001d0be"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_fd8a6dff","status":"ok","reason":"","figures":[{"key":"coef.EMST","value":-1.5006258173803875,"unit":"","label":"2SLS effect of EMST on y1","se":0.7350075466238339,"ci_low":-2.9829098030302545,"ci_high":-0.018341831730520397,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de meanwSIM na forma reduzida (y1 sobre meanwSIM e os controles) e no primeiro estágio (EMST sobre meanwSIM e os controles).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":-1.8924041277939492,"unit":"","note":"forma reduzida"},{"symbol":"π","value":1.2610766160864015,"unit":"","note":"primeiro estágio"}],"result":-1.5006258173803875,"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":0.5402360935939874,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.7350075466238339,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 43 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":-1.5006258173803875,"unit":"","note":""},{"symbol":"c","value":2.016692199227824,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.7350075466238339,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.EMST","value":45.0750057061979,"flag":false,"note":"Wald F of the excluded instruments in the first stage for EMST (heteroskedasticity-robust standard errors (HC1))"},{"name":"partial_r2.EMST","value":0.43125062503238787,"flag":false,"note":"share of the variation the instruments explain beyond the controls"},{"name":"weak_instrument","value":false,"flag":false,"note":"every first-stage F is at least 10"},{"name":"n_obs","value":46,"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":0.7352793963158135,"se":0.49122393548353027,"t":1.496831369978033,"p":0.1417419170822108,"ci_low":-0.25536808244781384,"ci_high":1.7259268750794408},{"term":"newmopan","coef":-0.023988510914568537,"se":0.30969974626807545,"t":-0.07745731536313281,"p":0.9386190515919255,"ci_low":-0.6485575733162326,"ci_high":0.6005805514870955},{"term":"EMST","coef":-1.5006258173803875,"se":0.7350075466238339,"t":-2.0416468161086594,"p":0.04735217727016545,"ci_low":-2.9829098030302545,"ci_high":-0.018341831730520397}],"first_stage":[{"endog":"EMST","instrument":"meanwSIM","coef":1.2610766160864015,"se":0.18783372772046456,"t":6.713792200105534,"p":3.346539847734253e-8,"ci_low":0.8822738026406576,"ci_high":1.6398794295321455}],"reduced_form":[{"instrument":"meanwSIM","coef":-1.8924041277939492,"se":0.9029280189153219,"t":-2.095852701599929,"p":0.04201790139442328,"ci_low":-3.713332020004712,"ci_high":-0.07147623558318639}]},"datasets":{},"warnings":["18 rows were dropped because one of 'y1', 'EMST', 'meanwSIM', 'newmopan' was missing"],"assumptions":["the instruments affect the outcome only through the endogenous regressors (exclusion restriction)","the instruments are as good as randomly assigned given the controls","errors are independent across observations; their variance may differ"],"notes":""},"outputs":null,"output_digest":"2b24b514d16eaeb7531a900f9115c7c5bf29d079130996ff407667df4bb3b9bc","params_versions":[],"sources":[],"started_at":"2026-10-15T17:11:03.847376Z","duration_ms":3.603625111281872,"error":null,"decided_by":"model","rationale":"causcibench_metadata_rule/1: iv","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_c1f6af88","llm_name":"infer__iv_2sls","vendor_id":"script_1","result_for_model_ref":"31999b306e56265882598ade057f22d7ac6ecd1f519e6825ac00bd5512fd96c2"}}
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