{"call_id":"call_be68a177","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f/paper_repro.ajr_2001.tab4_pa_c1.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.gemini","version":"","code_version":"38e979372a0f","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","load_skill","submit_answer"],"exemplars":[]},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T12:41:00.349648Z","duration_ms":8337.67508296296,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"gemini-3.8-flash","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# Skills\nProcedures for kinds of problems. Load one with load_skill(name) when the right design is not obvious:\n- choose-identification-strategy: Decide which design identifies the effect a question asks about - an experiment, an instrument, a discontinuity, differences-in-differences, fixed effects or none - from how treatment was assigned and what the data record. Load it whenever a question asks for an effect and the right estimator is not obvious.\n- differences-in-differences: Estimate the effect of a policy adopted by some units and not others by comparing changes over time - the classic two-group two-period design, and staggered adoption across periods, where two-way fixed effects fails when effects vary. Load it for laws passed in one municipality, programs rolled out over several years, and any before-and-after comparison with a control group.\n- evidence-pooling: Combine published estimates of the same effect from several studies into one random-effects average, checking that they are independent, in the same unit and about the same outcome, and reporting how much they disagree. Load it when a question lists studies with estimates and standard errors or intervals and asks what the literature finds.\n- experiments-and-power: Estimate the effect of a randomized program (by individual or by cluster, large or tiny samples) and plan an evaluation - how many units are needed, or what effect a fixed sample can detect. Load it for lotteries, pilots, randomized rollouts and any sample-size or power question.\n- instrumental-variables: Estimate the effect of a self-selected or endogenous regressor with an instrument - a randomized offer with partial take-up, distance, eligibility rules, a lottery - and judge whether the instrument is strong and plausibly excluded. Load it when the question warns that those who take a treatment differ from those who do not.\n- panel-fixed-effects: Relate an outcome to a regressor that varies over time within the same units - municipalities, schools, firms observed for several years - removing fixed unobserved differences between units, and recognise when a pooled comparison across units misleads (Simpson's paradox). Load it for panels and \"within the same municipality over time\" questions.\n- poverty-and-inequality: Measure poverty (headcount, gap, severity at a given line) and inequality (Gini, Palma, Theil) from household survey microdata - with the survey weights, the income concept and the period the line is defined in. Load it for any question about how many are poor or how unequal incomes are.\n- regression-discontinuity: Estimate an effect where treatment switches at a threshold of a running variable - vote margins, eligibility scores, population thresholds for transfers, age cutoffs - and test for manipulation around the threshold before trusting it. Load it for close elections, eligibility cutoffs and any \"just above versus just below\" question.\n- reporting-numbers: Write the final answer so that every number is traceable - quote only what tools stated, cite each figure by reference, do derived arithmetic with a tool, and state uncertainty and caveats. Load it before submitting an answer that needs a ratio, a difference, a percent or a conversion.\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":"A regressão por Mínimos Quadrados em Dois Estágios (2SLS) estima o efeito da proteção contra risco de expropriação sobre o log do PIB per capita de 1995 na amostra base de ex-colônias, instrumentada pela mortalidade dos colonizadores europeus.\ninfer__iv_2sls({\"subset\": \"baseco == 1\", \"instruments\": [\"logem4\"], \"endog\": [\"avexpr\"], \"se\": \"classical\", \"dataset\": \"maketable4\", \"y\": \"logpgp95\"})","tokens_in":18474,"tokens_out":508,"meta":{"turn_call_id":"call_be68a177","text":"A regressão por Mínimos Quadrados em Dois Estágios (2SLS) estima o efeito da proteção contra risco de expropriação sobre o log do PIB per capita de 1995 na amostra base de ex-colônias, instrumentada pela mortalidade dos colonizadores europeus.","tool_calls":[{"vendor_id":"call_519035","llm_name":"infer__iv_2sls","tool_id":"infer.iv_2sls","args":{"subset":"baseco == 1","instruments":["logem4"],"endog":["avexpr"],"se":"classical","dataset":"maketable4","y":"logpgp95"}}],"request":{"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"438237559235ba9eff244483d2a91d6ac5399b4d63eb2c6383096d4de95e77e5","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd","contents_refs":["c837d819101eb7482490acf19747c7570bf0ec8cf6c2b4480ec5883599174206"],"messages_refs":["ef0ff53deead73c69937a13bdf31b8b31941ebe29bd1d7c128b7caaedd841e49"],"config":{"max_output_tokens":32000,"tool_config":{"function_calling_config":{"mode":"AUTO"}},"automatic_function_calling":{"disable":true,"maximum_remote_calls":10},"thinking_config":{"include_thoughts":true},"temperature":null,"top_p":null,"top_k":null,"seed":null}},"attempts":[{"started_at":"2026-10-15T12:41:00.652496Z","latency_ms":8337.67508296296,"finish_reason":"STOP","usage":{"input":18474,"output":131,"thoughts":377,"cached":0},"error":null,"raw_ref":"1616298c3cc1cec486415b444f9149d3664a3ec09bb9aaa49fc7381eb9578c6e","config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false}],"response_id":"3cnQao32Jszoz7IPnYC4iQU","finish_reason":"STOP","model_reported":"gemini-3.8-flash","usage":{"input":18474,"output":131,"thoughts":377,"cached":0},"thoughts":"**Replicating Acemoglu, Johnson, and Robinson (2001), Table 4, Panel A, Column (1)**\n\nOkay, here's the plan. I want to replicate Acemoglu, Johnson, and Robinson's (2001) Table 4, Panel A, column (1). Specifically, I'm after a 2SLS estimate using their core data. I'm going to focus on the base sample of former colonies (`baseco == 1`). The outcome variable I'm interested in is `logpgp95`, which represents log GDP per capita in 1995 (PPP basis). My endogenous regressor is `avexpr`, representing the average protection against expropriation risk over 1985-1995. The instrument will be `logem4`, which is the log of European settler mortality. No other regressors are needed, so `exog` will be handled as empty or `None`. The subset condition is `baseco == 1`, as the paper focuses on former colonies.\n\nNow, a crucial detail: the standard errors. In AJR (2001), they used OLS/2SLS (classical) standard errors, which were standard back then. However, I want to confirm what this tool supports and returns. The default `se` for the function `infer__iv_2sls` is HC1, so I'll need to check the available options: classical or HC1. I need to make sure the standard errors match the original paper for a clean replication. I should look into the function's parameters.\n\nBefore I start, I'll need to call `infer__iv_2sls`. Before I do that, though, I need to make sure the right skill is loaded, so let me quickly verify that. Okay, I'm ready to call `infer__iv_2sls`.\n\n\n","vendor":true,"wall_ms":8641.4511250332}}
{"call_id":"call_c77e46a0","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f/paper_repro.ajr_2001.tab4_pa_c1.agent/e1a1","step":"analysis","kind":"tool","name":"infer.iv_2sls","version":"0.1.0","code_version":"38e979372a0f","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_c77e46a0","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":"2026-10-15T12:41:08.994039Z","duration_ms":12.851792154833674,"error":null,"decided_by":"model","rationale":"A regressão por Mínimos Quadrados em Dois Estágios (2SLS) estima o efeito da proteção contra risco de expropriação sobre o log do PIB per capita de 1995 na amostra base de ex-colônias, instrumentada pela mortalidade dos colonizadores europeus.","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_be68a177","llm_name":"infer__iv_2sls","vendor_id":"call_519035","result_for_model_ref":"a489091fc8a649114bfa5452203f610e0d16e65eaf87dbf479f1edb072b5577a"}}
{"call_id":"call_928aba4c","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f/paper_repro.ajr_2001.tab4_pa_c1.agent/e1a1","step":"analysis","kind":"llm","name":"llm.analyst.gemini","version":"","code_version":"38e979372a0f","inputs":{},"input_digest":"","dataset_digests":{},"result":null,"outputs":null,"output_digest":"","params_versions":[],"sources":[],"started_at":"2026-10-15T12:41:09.007271Z","duration_ms":12238.899416988716,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"gemini-3.8-flash","prompt":null,"response":"submit_answer({\"figure_refs\": [\"call_c77e46a0:coef.avexpr\"], \"caveats\": [\"A validade causal depende crucialmente da hipótese de exclusão, ou seja, de que a mortalidade dos colonizadores afete o produto per capita contemporâneo unicamente por meio das instituições legadas, sem canais diretos persistentes (como condições epidemiológicas ou geográficas).\", \"Como o modelo é exatamente identificado (um instrumento para um regressor endógeno), não é possível realizar teste de sobreidentificação (Sargan).\"], \"answer\": \"Na replicação da Tabela 4, Painel A, coluna (1) de Acemoglu, Johnson e Robinson (2001), a estimação por Mínimos Quadrados em Dois Estágios (2SLS) na amostra base de 64 ex-colônias indica que o coeficiente do índice de proteção contra risco de expropriação (avexpr) sobre o log do PIB per capita em 1995 (logpgp95) é de 0,944279 (erro padrão de 0,156525; IC 95% [0,631389; 1,25717]). \\n\\nO instrumento (log da mortalidade dos colonizadores europeus, logem4) é forte no primeiro estágio, com estatística F de 22,946797 (R² parcial de 0,270131).\"})","tokens_in":20056,"tokens_out":1022,"meta":{"turn_call_id":"call_928aba4c","text":"","tool_calls":[{"vendor_id":"call_521182","llm_name":"submit_answer","tool_id":null,"args":{"figure_refs":["call_c77e46a0:coef.avexpr"],"caveats":["A validade causal depende crucialmente da hipótese de exclusão, ou seja, de que a mortalidade dos colonizadores afete o produto per capita contemporâneo unicamente por meio das instituições legadas, sem canais diretos persistentes (como condições epidemiológicas ou geográficas).","Como o modelo é exatamente identificado (um instrumento para um regressor endógeno), não é possível realizar teste de sobreidentificação (Sargan)."],"answer":"Na replicação da Tabela 4, Painel A, coluna (1) de Acemoglu, Johnson e Robinson (2001), a estimação por Mínimos Quadrados em Dois Estágios (2SLS) na amostra base de 64 ex-colônias indica que o coeficiente do índice de proteção contra risco de expropriação (avexpr) sobre o log do PIB per capita em 1995 (logpgp95) é de 0,944279 (erro padrão de 0,156525; IC 95% [0,631389; 1,25717]). \n\nO instrumento (log da mortalidade dos colonizadores europeus, logem4) é forte no primeiro estágio, com estatística F de 22,946797 (R² parcial de 0,270131)."}}],"request":{"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"438237559235ba9eff244483d2a91d6ac5399b4d63eb2c6383096d4de95e77e5","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd","contents_refs":["c837d819101eb7482490acf19747c7570bf0ec8cf6c2b4480ec5883599174206","6bc3e2e3291fcf837fe99748a4243b5d7c9577a38c7a1e23fbd57a126996ce20","fad07ce283546ec4741f8a0ecca36994e8f3516446cc59c45a370ab3cb2dbe7e"],"messages_refs":["ef0ff53deead73c69937a13bdf31b8b31941ebe29bd1d7c128b7caaedd841e49","03fe6d682d3561d879c2e6a3d5dd7565a48f7b25f9c259c418a95ff11224c864","1bb87812b24482afaf5e03c84b7c1c4cfeb06eb42111693b4472d070660b3c23"],"config":{"max_output_tokens":32000,"tool_config":{"function_calling_config":{"mode":"AUTO"}},"automatic_function_calling":{"disable":true,"maximum_remote_calls":10},"thinking_config":{"include_thoughts":true},"temperature":null,"top_p":null,"top_k":null,"seed":null}},"attempts":[{"started_at":"2026-10-15T12:41:09.019517Z","latency_ms":12238.899416988716,"finish_reason":"STOP","usage":{"input":20056,"output":344,"thoughts":678,"cached":16729},"error":null,"raw_ref":"9e20df08693144afecb7a5e1a68e126501102a03270d671065e81027b82a6c5d","config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false}],"response_id":"5snQaqHiDKWP6dkP8LOKgQc","finish_reason":"STOP","model_reported":"gemini-3.8-flash","usage":{"input":20056,"output":344,"thoughts":678,"cached":16729},"thoughts":"**My Analysis of the AJR (2001) Coefficient**\n\nOkay, let's dive into this. First, I'm checking the results I have: the coefficient on `avexpr` is 0.944279 (about 0.94), with a standard error of 0.156525. *That* looks familiar; based on my memory and quick check to confirm, those are the numbers reported in Table 4, Panel A, column (1) of AJR 2001 (0.94, SE 0.16!). The first-stage F-statistic is 22.95 – I remember the paper reported the F-statistic or R2 as 0.27; the sample size is 64.  Let me note that I don't need to consider HC1 standard errors here, as the original study used OLS/2SLS SEs (0.16), so it makes sense to stay consistent.\n\nI'm making a quick note that I will use the reporting-numbers skill to submit the final answer, so I will prepare that now.\n\nLet me ensure the figure reference is correct: `call_c77e46a0:coef.avexpr`.\n\nNow, let's verify what I have: a coefficient of 0,944279 (approximately 0,94), a standard error of 0,156525, a confidence interval of [0,631389, 1,25717], a first-stage F-statistic of 22,9467965870538, and 64 observations.\n\nI'll double-check my figure reference is `call_c77e46a0:coef.avexpr`.\n\nLet's review the prompt to ensure a correct answer: I'm focusing on Acemoglu, Johnson and Robinson (2001), specifically Table 4, Panel A, column (1). I am estimating, using two-stage least squares, the impact of average protection against expropriation risk (1985-1995) on log GDP per capita in 1995 (PPP basis). The instrument is log European settler mortality, with no other regressors. My task is to report the coefficient on average protection against expropriation risk.\n\nI have to remember my caveats. I must state that the exclusion restriction assumes that historical settler mortality affects 1995 income *only* through institutional development. This can be violated if other channels (like disease environment or geography) persist directly. Also, the model is exactly identified (one instrument for one endogenous variable), which means no overidentification test (Sargan) is possible.\n\nEverything seems correct, so I'm submitting the answer.\n\n\n","vendor":true,"wall_ms":12252.428834093735}}
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