{"call_id":"call_0ed2f186","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.ajr_2001.tab4_pa_c1.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","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":"2026-10-15T16:57:45.118302Z","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\": \"maketable4\",\n \"rows\": 163,\n \"description\": \"The authors' replication file for Table 4 of Acemoglu, Johnson and Robinson (2001), 'The Colonial Origins of Comparative Development': one row per country (163 rows). Columns: shortnam, the 3-letter country code; africa, 1 for countries in Africa; asia, 1 for countries in Asia; rich4, 1 for the four 'neo-Europes' (United States, Canada, Australia, New Zealand); lat_abst, absolute latitude of the capital divided by 90; avexpr, average protection against expropriation risk over 1985-1995 (Political Risk Services, 0 to 10, higher means more protection); logpgp95, log GDP per capita in 1995, PPP basis (World Bank); logem4, log settler mortality (mortality of European settlers); loghjypl, log output per worker in 1988 (Hall and Jones); baseco, 1 for the former colonies in the paper's base sample and missing for every other country.\",\n \"columns\": {\n  \"shortnam\": \"str\",\n  \"africa\": \"float32\",\n  \"lat_abst\": \"float32\",\n  \"rich4\": \"float32\",\n  \"avexpr\": \"float32\",\n  \"logpgp95\": \"float32\",\n  \"logem4\": \"float32\",\n  \"asia\": \"float32\",\n  \"loghjypl\": \"float32\",\n  \"baseco\": \"float32\"\n }\n}\n\n# Question\nAcemoglu, Johnson and Robinson (2001), 'The Colonial Origins of Comparative Development', Table 4, Panel A, column (1): in the paper's base sample of former colonies, estimate by two-stage least squares the effect of average protection against expropriation risk over 1985-1995 on log GDP per capita in 1995 (PPP basis), instrumenting protection against expropriation risk with log European settler mortality and with no other regressors. Report the coefficient on average protection against expropriation risk.","response":"Table 4, Panel A, column (1): 2SLS of log GDP per capita on expropriation risk, instrumented by log settler mortality, in the base sample (the authors' maketable4.do: ivreg, classical errors).\ninfer__iv_2sls({\"dataset\": \"maketable4\", \"y\": \"logpgp95\", \"endog\": [\"avexpr\"], \"instruments\": [\"logem4\"], \"exog\": [], \"subset\": \"baseco == 1\", \"se\": \"classical\"})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_0ed2f186","text":"Table 4, Panel A, column (1): 2SLS of log GDP per capita on expropriation risk, instrumented by log settler mortality, in the base sample (the authors' maketable4.do: ivreg, classical errors).","tool_calls":[{"vendor_id":"script_1","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"dataset":"maketable4","y":"logpgp95","endog":["avexpr"],"instruments":["logem4"],"exog":[],"subset":"baseco == 1","se":"classical"}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"438237559235ba9eff244483d2a91d6ac5399b4d63eb2c6383096d4de95e77e5","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["ef0ff53deead73c69937a13bdf31b8b31941ebe29bd1d7c128b7caaedd841e49"],"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":0.7782501634210348}}
{"call_id":"call_f8fbf39c","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.ajr_2001.tab4_pa_c1.agent/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"62cdbe0860ad","inputs":{"dataset":"maketable4","y":"logpgp95","endog":["avexpr"],"instruments":["logem4"],"exog":[],"cluster":null,"se":"classical","subset":"baseco == 1","level":0.95},"input_digest":"1b2bb7f5f82037c03141e2199a9d95588c1c3877bc4c2ccfbc949196031e8a34","dataset_digests":{"maketable4":"dc8a5df19428e33e3bb4109922ccbd092a69bde0d19a217451baa154782debfc"},"result":{"tool":"infer.iv_2sls","version":"0.1.0","call_id":"call_f8fbf39c","status":"ok","reason":"","figures":[{"key":"coef.avexpr","value":0.9442793851547994,"unit":"","label":"2SLS effect of avexpr on logpgp95","se":0.15652545732957907,"ci_low":0.6313894542623473,"ci_high":1.2571693160472515,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de logem4 na forma reduzida (logpgp95 sobre logem4) e no primeiro estágio (avexpr sobre logem4).","formula":"β = ρ / π","terms":[{"symbol":"ρ","value":-0.5729681917341751,"unit":"","note":"forma reduzida"},{"symbol":"π","value":-0.6067782488338938,"unit":"","note":"primeiro estágio"}],"result":0.9442793851547994,"unit":""},{"text_pt":"Erro-padrão clássico (homocedástico): a raiz da variância estimada do coeficiente.","formula":"ep = √V_jj","terms":[{"symbol":"V_jj","value":0.024500218792233876,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.15652545732957907,"unit":""},{"text_pt":"Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 62 graus de liberdade vezes o erro-padrão.","formula":"IC = β ± c · ep","terms":[{"symbol":"β","value":0.9442793851547994,"unit":"","note":""},{"symbol":"c","value":1.9989715170333786,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.15652545732957907,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"first_stage_F.avexpr","value":22.94679658705377,"flag":false,"note":"Wald F of the excluded instruments in the first stage for avexpr (classical (homoskedastic) standard errors)"},{"name":"partial_r2.avexpr","value":0.2701313941078145,"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":64,"flag":false,"note":""},{"name":"se_type","value":"classical","flag":false,"note":"classical (homoskedastic) standard errors"},{"name":"n_clusters","value":null,"flag":false,"note":"errors not clustered"}],"tables":{"coefficients":[{"term":"Intercept","coef":1.909666540546741,"se":1.0267272828674519,"t":1.8599549972154334,"p":0.0676371961257388,"ci_low":-0.1427320536663681,"ci_high":3.9620651347598503},{"term":"avexpr","coef":0.9442793851547994,"se":0.15652545732957907,"t":6.032752762807972,"p":9.798644711494112e-8,"ci_low":0.6313894542623473,"ci_high":1.2571693160472515}],"first_stage":[{"endog":"avexpr","instrument":"logem4","coef":-0.6067782488338938,"se":0.12666860022743895,"t":-4.79028147263329,"p":0.000010765461520046432,"ci_low":-0.8599851727910319,"ci_high":-0.3535713248767556}],"reduced_form":[{"instrument":"logem4","coef":-0.5729681917341751,"se":0.07615726511746891,"t":-7.523486969370543,"p":2.6586798876505685e-10,"ci_low":-0.7252043955191552,"ci_high":-0.4207319879491951}]},"datasets":{},"warnings":[],"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 and have the same variance for every observation"],"notes":""},"outputs":null,"output_digest":"98f84285f217edb692d9f674412ad2fa98a8d225f6e0345f0c055d997ff189f2","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:45.124392Z","duration_ms":4.765083082020283,"error":null,"decided_by":"model","rationale":"Table 4, Panel A, column (1): 2SLS of log GDP per capita on expropriation risk, instrumented by log settler mortality, in the base sample (the authors' maketable4.do: ivreg, classical errors).","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_0ed2f186","llm_name":"infer__iv_2sls","vendor_id":"script_1","result_for_model_ref":"c847d410c16485ab55f95511a4cf2e3cead6adc0d7862e051230b848dd8f4ccc"}}
{"call_id":"call_d522e38c","run_id":"20261015T165744Z-suite-paper_repro-scripted-def9df/paper_repro.ajr_2001.tab4_pa_c1.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.scripted","version":"","code_version":"62cdbe0860ad","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T16:57:45.129360Z","duration_ms":0.0,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"reference","prompt":null,"response":"submit_answer({\"answer\": \"Estimativa (infer.iv_2sls): coef.avexpr = 0,9443.\", \"figure_refs\": [\"call_f8fbf39c:coef.avexpr\"], \"caveats\": []})","tokens_in":0,"tokens_out":0,"meta":{"turn_call_id":"call_d522e38c","text":"","tool_calls":[{"vendor_id":"script_2","llm_name":"submit_answer","tool_id":null,"args":{"answer":"Estimativa (infer.iv_2sls): coef.avexpr = 0,9443.","figure_refs":["call_f8fbf39c:coef.avexpr"],"caveats":[]}}],"request":{"system_stable_ref":"433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420","system_task_ref":"438237559235ba9eff244483d2a91d6ac5399b4d63eb2c6383096d4de95e77e5","tools_ref":"52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b","contents_refs":[],"messages_refs":["ef0ff53deead73c69937a13bdf31b8b31941ebe29bd1d7c128b7caaedd841e49","b5d91d6fbe93debcf8b69865839cd3df23936283c5af4da96455cdae69fdff01","38d315ed47c15868f61d74b7eec03476836e41a2e215c6ed6817f5b39d821e7f"],"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":0.22466620430350304}}
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