{"call_id":"call_268ad34d","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f/paper_repro.ajr_2001.tab2_c2.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":"2026-10-15T12:41:00.392585Z","duration_ms":9710.03995789215,"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\": \"maketable2\",\n \"rows\": 163,\n \"description\": \"The authors' replication file for Table 2 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; other, 1 for countries outside Asia, Africa and the Americas; 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); 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  \"avexpr\": \"float32\",\n  \"logpgp95\": \"float32\",\n  \"other\": \"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 2, column (2): in the paper's base sample of former colonies, regress log GDP per capita in 1995 (PPP basis) on average protection against expropriation risk over 1985-1995 by ordinary least squares, with no other regressors. Report the coefficient on average protection against expropriation risk.","response":"I will run an OLS regression of log GDP per capita in 1995 on average protection against expropriation risk for the base sample of former colonies (baseco == 1).\ninfer__ols({\"subset\": \"baseco == 1\", \"x\": [\"avexpr\"], \"y\": \"logpgp95\", \"dataset\": \"maketable2\"})","tokens_in":18415,"tokens_out":707,"meta":{"turn_call_id":"call_268ad34d","text":"I will run an OLS regression of log GDP per capita in 1995 on average protection against expropriation risk for the base sample of former colonies (baseco == 1).","tool_calls":[{"vendor_id":"call_519605","llm_name":"infer__ols","tool_id":"infer.ols","args":{"subset":"baseco == 1","x":["avexpr"],"y":"logpgp95","dataset":"maketable2"}}],"request":{"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"35fc5c0245791591c4c81ff6abc185344bac215d1b5b7a6806976a677d5cd33a","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd","contents_refs":["ca9f74584eae45bceaa9761be3d71ff46e87e52ef305400f8b72e2ee27d539b5"],"messages_refs":["3ea53614aa4095f6b78f0d7f9ca140c3245ca0e49d814602ed537685c108e854"],"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":"2026-10-15T12:41:00.648653Z","latency_ms":9710.03995789215,"finish_reason":"STOP","usage":{"input":18415,"output":85,"thoughts":622,"cached":0},"error":null,"raw_ref":"d0251c4c9ba43fcf36a1fdb59e5d8e2fc93ae0e85a7015b9d52314d9b2d2ec2e","config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false}],"response_id":"3snQaoXUDpz_qtsPvIKauQ8","finish_reason":"STOP","model_reported":"gemini-3.8-flash","usage":{"input":18415,"output":85,"thoughts":622,"cached":0},"thoughts":"**Reproducing Acemoglu, Johnson, and Robinson (2001), Table 2, Column (2)**\n\nOkay, so the goal is clear: I need to replicate the regression from Acemoglu, Johnson, and Robinson (2001) – specifically, Table 2, column (2). This involves regressing log GDP per capita in 1995 (PPP basis) on average protection against expropriation risk between 1985 and 1995 using ordinary least squares (OLS), but only for their base sample of former colonies. No other variables are to be included as regressors. The target is the reported coefficient on the expropriation risk variable.\n\nFirst, I need to make sure the data is structured correctly. I'll inspect the dataset `maketable2` to confirm the column names and data types are as expected. I'm looking for the columns `logpgp95` (log GDP), `avexpr` (expropriation risk), and `baseco` (the indicator for former colonies in the base sample). I've got to make sure there are 163 rows and that the data is there.\n\nNext, I need to correctly subset the data. Only the rows where `baseco` equals 1 should be included in the regression. This defines the base sample.\n\nNow, I'm ready to specify the OLS regression. My outcome variable (`y`) is `logpgp95`, and the regressor (`x`) is `['avexpr']`. The subset condition is `'baseco == 1'`. There are no other variables.\n\nThe `infer__ols` tool seems perfectly suited for this. Let's think carefully about the standard errors (`se`). The prompt just asks for the *coefficient*, and the point estimate from OLS, $\\hat{\\beta} = (X'X)^{-1}X'y$, is independent of the standard error type! So, whether I use \"classical\" OLS standard errors, or something like HC1, the coefficient itself will be the same. The `infer__ols` tool defaults to \"HC1\". Even if the original paper reports classical OLS standard errors, the coefficient is what matters here. I will just run `infer__ols` with the specifications above. I'm confident I can reproduce the requested coefficient.\n\n\n","vendor":true,"wall_ms":9967.110292054713}}
{"call_id":"call_62baac04","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f/paper_repro.ajr_2001.tab2_c2.agent/e1a1","step":"analysis","kind":"tool","name":"infer.ols","version":"0.1.0","code_version":"38e979372a0f","inputs":{"dataset":"maketable2","y":"logpgp95","x":["avexpr"],"controls":[],"fe":[],"cluster":null,"se":"HC1","weights":null,"subset":"baseco == 1","intercept":true,"level":0.95},"input_digest":"2d5469daefad6e0a6aa04de3f8cf4cae49b9347a0320565a356992cc6e6abdeb","dataset_digests":{"maketable2":"f2d6f55b0be66f82f98d12727d9a9b67b5c96add4aa011b150d70fa4b81b43c8"},"result":{"tool":"infer.ols","version":"0.1.0","call_id":"call_62baac04","status":"ok","reason":"","figures":[{"key":"coef.avexpr","value":0.5221070297245911,"unit":"","label":"Coefficient on avexpr in the regression of logpgp95","se":0.04992252631098114,"ci_low":0.42231332157059037,"ci_high":0.6219007378785918,"ci_level":0.95,"derivation":{"steps":[{"text_pt":"Coeficiente de avexpr na regressão por mínimos quadrados ordinários de logpgp95.","formula":"","terms":[{"symbol":"n","value":64.0,"unit":"observações","note":""},{"symbol":"k","value":2.0,"unit":"parâmetros","note":""}],"result":0.5221070297245911,"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":0.002492258633270604,"unit":"","note":"diagonal da matriz de covariância"}],"result":0.04992252631098114,"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":0.5221070297245911,"unit":"","note":""},{"symbol":"c","value":1.9989715170333786,"unit":"","note":"quantil 0.975"},{"symbol":"ep","value":0.04992252631098114,"unit":"","note":""}],"result":null,"unit":""}],"note_pt":""}}],"diagnostics":[{"name":"n_obs","value":64,"flag":false,"note":"observations used"},{"name":"k","value":2,"flag":false,"note":"parameters estimated, fixed-effect dummies included"},{"name":"r2","value":0.5401149553120992,"flag":false,"note":""},{"name":"df_resid","value":62,"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":4.660383298614833,"se":0.3201314965536011,"t":14.557715653681464,"p":1.1355641347475712e-21,"ci_low":4.020449555298915,"ci_high":5.300317041930751},{"term":"avexpr","coef":0.5221070297245911,"se":0.04992252631098114,"t":10.458345526671526,"p":2.5665704437506074e-15,"ci_low":0.42231332157059037,"ci_high":0.6219007378785918}]},"datasets":{},"warnings":[],"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":"15f3fa79a979a8c866b7532a6d2c959cd17758bcccfef07b6f0dc1b57e452a17","params_versions":[],"sources":[],"started_at":"2026-10-15T12:41:10.363931Z","duration_ms":4.347417037934065,"error":null,"decided_by":"model","rationale":"I will run an OLS regression of log GDP per capita in 1995 on average protection against expropriation risk for the base sample of former colonies (baseco == 1).","cached":false,"informed_by":[],"model":null,"prompt":null,"response":null,"tokens_in":null,"tokens_out":null,"meta":{"turn_id":"call_268ad34d","llm_name":"infer__ols","vendor_id":"call_519605","result_for_model_ref":"0e5be67e39fdc870af623030fc7081ea7fa9e07b4568a4a133872353b328a04e"}}
{"call_id":"call_6c0457b2","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f/paper_repro.ajr_2001.tab2_c2.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":"2026-10-15T12:41:10.368579Z","duration_ms":5668.338417075574,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"gemini-3.8-flash","prompt":null,"response":"I will run the same OLS specification with classical standard errors to check the classical standard errors as originally published in the paper.\ninfer__ols({\"se\": \"classical\", \"y\": \"logpgp95\", \"dataset\": \"maketable2\", \"subset\": \"baseco == 1\", \"x\": [\"avexpr\"]})","tokens_in":20038,"tokens_out":282,"meta":{"turn_call_id":"call_6c0457b2","text":"I will run the same OLS specification with classical standard errors to check the classical standard errors as originally published in the paper.","tool_calls":[{"vendor_id":"call_633550","llm_name":"infer__ols","tool_id":"infer.ols","args":{"se":"classical","y":"logpgp95","dataset":"maketable2","subset":"baseco == 1","x":["avexpr"]}}],"request":{"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"35fc5c0245791591c4c81ff6abc185344bac215d1b5b7a6806976a677d5cd33a","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd","contents_refs":["ca9f74584eae45bceaa9761be3d71ff46e87e52ef305400f8b72e2ee27d539b5","26d3e8dd421412b53dc7e28b466c59f6b2c4a5f2af9b6ed035465fdb1ee4c287","a583df065872984365d6469fbaaa7454b71c4ac99e8d8d782424085ad2526751"],"messages_refs":["3ea53614aa4095f6b78f0d7f9ca140c3245ca0e49d814602ed537685c108e854","3353be0c203c31cf4f57e1be893a42f482aaa77c239dca88b5db4d69fa75cbf3","d11b918ddee452e5f607a4eaa8928e8c41db3926e7a21cf3213d3f9ae8745dea"],"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":"2026-10-15T12:41:10.381080Z","latency_ms":5668.338417075574,"finish_reason":"STOP","usage":{"input":20038,"output":77,"thoughts":205,"cached":16806},"error":null,"raw_ref":"41c385335853987b7d24c7f02acdfab1fc06ea35a8efe09d364982087e4c0d9b","config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false}],"response_id":"5snQau7COaammtkPh6iC-A4","finish_reason":"STOP","model_reported":"gemini-3.8-flash","usage":{"input":20038,"output":77,"thoughts":205,"cached":16806},"thoughts":"**Checking Classical Standard Errors for the AJR (2001) Replication**\n\nOkay, so the goal is to replicate the findings from Acemoglu, Johnson, and Robinson's (2001) paper, specifically Table 2, column (2). The prompt is pretty clear: we're looking at former colonies and regressing log GDP per capita in 1995 on average protection against expropriation risk between 1985 and 1995 using OLS, no other regressors.\n\nMy instinct is to run the standard errors with the \"classical\" (or ordinary least squares) method, just to get that specific figure reported in the paper. I remember the table, and the standard error was around 0.061 if I recall correctly. So let's double-check that, and it's always good to have that as a point of reference. I want to test using `se=\"classical\"` as an option. Ultimately, it is always helpful to have both sets of figures for comparison or diagnostic purposes; there might be a difference, and it's good to keep that figure handy.\n\n\n","vendor":true,"wall_ms":5681.744625093415}}
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{"call_id":"call_3d43467b","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f/paper_repro.ajr_2001.tab2_c2.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":"2026-10-15T12:41:16.056061Z","duration_ms":17233.205792028457,"error":null,"decided_by":"model","rationale":"","cached":false,"informed_by":[],"model":"gemini-3.8-flash","prompt":null,"response":"submit_answer({\"figure_refs\": [\"call_6014ef91:coef.avexpr\", \"call_62baac04:coef.avexpr\"], \"caveats\": [\"A regressão por MQO reflete uma correlação e não identifica necessariamente um efeito causal devido a potenciais problemas de variáveis omitidas e causalidade reversa, o que motivou a estratégia de variáveis instrumentais dos autores.\", \"A amostra é restrita a 64 ex-colônias com dados válidos na amostra base (baseco == 1).\"], \"answer\": \"Na regressão por mínimos quadrados ordinários (MQO) correspondente à Tabela 2, coluna (2), de Acemoglu, Johnson e Robinson (2001), utilizando a amostra base de ex-colônias (baseco == 1, com 64 observações), o coeficiente estimado para a proteção média contra o risco de desapropriação (avexpr) sobre o logaritmo do PIB per capita de 1995 é de 0,522107 (erro padrão clássico de 0,061185; IC 95%: [0,3998, 0,644414]; com erro padrão robusto HC1 de 0,0499225; IC 95%: [0,422313, 0,621901]). O R² da regressão é de 0,540115.\\n\\nIn the OLS regression replicating Table 2, column (2) of Acemoglu, Johnson, and Robinson (2001) for the base sample of 64 former colonies (baseco == 1), the estimated coefficient on average protection against expropriation risk (avexpr) on log GDP per capita in 1995 (logpgp95) is 0.522107 (classical standard error of 0.061185, 95% CI [0.3998, 0.644414]; HC1 robust standard error of 0.0499225, 95% CI [0.422313, 0.621901]), with an R² of 0.540115.\"})","tokens_in":20969,"tokens_out":2071,"meta":{"turn_call_id":"call_3d43467b","text":"","tool_calls":[{"vendor_id":"call_553263","llm_name":"submit_answer","tool_id":null,"args":{"figure_refs":["call_6014ef91:coef.avexpr","call_62baac04:coef.avexpr"],"caveats":["A regressão por MQO reflete uma correlação e não identifica necessariamente um efeito causal devido a potenciais problemas de variáveis omitidas e causalidade reversa, o que motivou a estratégia de variáveis instrumentais dos autores.","A amostra é restrita a 64 ex-colônias com dados válidos na amostra base (baseco == 1)."],"answer":"Na regressão por mínimos quadrados ordinários (MQO) correspondente à Tabela 2, coluna (2), de Acemoglu, Johnson e Robinson (2001), utilizando a amostra base de ex-colônias (baseco == 1, com 64 observações), o coeficiente estimado para a proteção média contra o risco de desapropriação (avexpr) sobre o logaritmo do PIB per capita de 1995 é de 0,522107 (erro padrão clássico de 0,061185; IC 95%: [0,3998, 0,644414]; com erro padrão robusto HC1 de 0,0499225; IC 95%: [0,422313, 0,621901]). O R² da regressão é de 0,540115.\n\nIn the OLS regression replicating Table 2, column (2) of Acemoglu, Johnson, and Robinson (2001) for the base sample of 64 former colonies (baseco == 1), the estimated coefficient on average protection against expropriation risk (avexpr) on log GDP per capita in 1995 (logpgp95) is 0.522107 (classical standard error of 0.061185, 95% CI [0.3998, 0.644414]; HC1 robust standard error of 0.0499225, 95% CI [0.422313, 0.621901]), with an R² of 0.540115."}}],"request":{"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"35fc5c0245791591c4c81ff6abc185344bac215d1b5b7a6806976a677d5cd33a","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd","contents_refs":["ca9f74584eae45bceaa9761be3d71ff46e87e52ef305400f8b72e2ee27d539b5","26d3e8dd421412b53dc7e28b466c59f6b2c4a5f2af9b6ed035465fdb1ee4c287","a583df065872984365d6469fbaaa7454b71c4ac99e8d8d782424085ad2526751","a535ab31b7d15407ab1dfe07f398da2d7ffdc37e4cfa8710c268b86aa45dc733","792bb786343abd2133dca05b46f4c8b9737ec98a1f7061d9e5f974d480c3c2f7"],"messages_refs":["3ea53614aa4095f6b78f0d7f9ca140c3245ca0e49d814602ed537685c108e854","3353be0c203c31cf4f57e1be893a42f482aaa77c239dca88b5db4d69fa75cbf3","d11b918ddee452e5f607a4eaa8928e8c41db3926e7a21cf3213d3f9ae8745dea","47be0b73fff841614eb2804780af10d43f4d780706ac7dca50bc7966a3fa6878","73f6cc662d90b8e23ecf3767eade31c90701ae556ece9518b9579e6d98fe28f5"],"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":"2026-10-15T12:41:16.066569Z","latency_ms":17233.205792028457,"finish_reason":"STOP","usage":{"input":20969,"output":535,"thoughts":1536,"cached":18148},"error":null,"raw_ref":"6a3759dcd18f2028ab03fe303634506f920e0fbe05b3185ce53f7b8e3fd1fe1f","config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false}],"response_id":"7MnQarzhItvwqtsP-vOUyA4","finish_reason":"STOP","model_reported":"gemini-3.8-flash","usage":{"input":20969,"output":535,"thoughts":1536,"cached":18148},"thoughts":"**Analysis of the AJR (2001) Regression**\n\nOkay, so I've been given the task to replicate the Acemoglu, Johnson, and Robinson (2001) Table 2, column (2) regression. The key here is to report the coefficient on average protection against expropriation risk. I've got the data. Running the OLS regression of log GDP per capita in 1995 on average protection against expropriation risk for the base sample of 64 former colonies, I find a coefficient on average protection against expropriation risk of 0.522107.\n\nNow, let's nail down the standard errors. With the classical standard errors, I get 0.061185, which is what AJR reports in their table (0.06 when rounded). But wait, I should also compute robust standard errors. Using HC1 robust standard errors, the standard error is 0.0499225. The number of observations is 64, and the R-squared is 0.540115, a decent fit.\n\nThe model is the OLS regression of log GDP per capita on the average protection against expropriation risk, from 1985 to 1995, in a sample of former colonies (baseco == 1). The estimate is 0.522107. The classical standard error is 0.061185, and the HC1 robust standard error is 0.0499225. The 95% confidence intervals are therefore [0.3998, 0.644414] and [0.422313, 0.621901].\n\nOf course, this is a simple OLS regression, which is subject to endogeneity concerns. We can't immediately make causal claims. The usual suspects: omitted variables, measurement error, and reverse causality are all potential problems. The authors use this as a benchmark, and their instrumental variable strategy (settler mortality) is meant to address these issues. This is also restricted to their base sample of 64 former colonies.\n\n```\nIn Table 2, column (2) of Acemoglu, Johnson, and Robinson (2001), the OLS regression of log GDP per capita in 1995 on the average protection against expropriation risk (for the base sample of 64 former colonies, baseco == 1) yields a coefficient of 0.522107 on avexpr, with classical standard error 0.061185 and HC1 robust standard error 0.0499225. Endogeneity is a concern; the authors instrument avexpr with settler mortality.\n```\n\n```tool_code\nsubmit_answer\n```\n\n\n","vendor":true,"wall_ms":17245.134207885712}}
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