{"attempt":1,"datasets":{"maketable2":{"description_shown":"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.","file_sha256":"f8213d9b42290cf0d3a2e968483bf3b5964e33261b8e48f92e9ea4c87c709b92","frame_sha256":null,"role":"agent","transforms":[]}},"epoch":1,"event":"sample_init","question":"Acemoglu, 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.","question_sha256":"c67bc8fac6e6107d1753da9531ccd48c3e1d43fc025598a0afbe16ccfdefab71","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f","seq":0,"skills":[],"task_id":"paper_repro.ajr_2001.tab2_c2.agent","task_sha256":"754dca75f8be5d462387ca3c5d4041d3adc8105f1d4aa1f940dbce5154d49b3c","timestamp":"2026-10-15T12:41:00.392585Z"}
{"attempt":1,"attempts":[{"config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false,"error":null,"finish_reason":"STOP","latency_ms":9710.03995789215,"raw_ref":"d0251c4c9ba43fcf36a1fdb59e5d8e2fc93ae0e85a7015b9d52314d9b2d2ec2e","started_at":"2026-10-15T12:41:00.648653Z","usage":{"cached":0,"input":18415,"output":85,"thoughts":622}}],"call_id":"call_268ad34d","epoch":1,"event":"model","finish_reason":"STOP","latency_ms":9710.03995789215,"model_reported":"gemini-3.8-flash","model_requested":"gemini-3.8-flash","provider":"gemini","request":{"config":{"automatic_function_calling":{"disable":true,"maximum_remote_calls":10},"max_output_tokens":32000,"seed":null,"temperature":null,"thinking_config":{"include_thoughts":true},"tool_config":{"function_calling_config":{"mode":"AUTO"}},"top_k":null,"top_p":null},"contents_refs":["ca9f74584eae45bceaa9761be3d71ff46e87e52ef305400f8b72e2ee27d539b5"],"messages_refs":["3ea53614aa4095f6b78f0d7f9ca140c3245ca0e49d814602ed537685c108e854"],"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"35fc5c0245791591c4c81ff6abc185344bac215d1b5b7a6806976a677d5cd33a","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd"},"response":{"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).","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) \u2013 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","tool_calls":[{"args":{"dataset":"maketable2","subset":"baseco == 1","x":["avexpr"],"y":"logpgp95"},"llm_name":"infer__ols","tool_id":"infer.ols","vendor_id":"call_519605"}]},"response_id":"3snQaoXUDpz_qtsPvIKauQ8","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f","seq":1,"task_id":"paper_repro.ajr_2001.tab2_c2.agent","timestamp":"2026-10-15T12:41:00.392585Z","usage":{"cached":0,"input":18415,"output":85,"thoughts":622},"vendor":true}
{"arguments":{"cluster":null,"controls":[],"dataset":"maketable2","fe":[],"intercept":true,"level":0.95,"se":"HC1","subset":"baseco == 1","weights":null,"x":["avexpr"],"y":"logpgp95"},"attempt":1,"cached":false,"call_id":"call_62baac04","card_version":"0.1.0","code_version":"38e979372a0f","dataset_digests":{"maketable2":"f2d6f55b0be66f82f98d12727d9a9b67b5c96add4aa011b150d70fa4b81b43c8"},"decided_by":"model","duration_ms":4.347417037934065,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"2d5469daefad6e0a6aa04de3f8cf4cae49b9347a0320565a356992cc6e6abdeb","llm_name":"infer__ols","output_digest":"15f3fa79a979a8c866b7532a6d2c959cd17758bcccfef07b6f0dc1b57e452a17","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).","replay_class":"replayed","result":{"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent across observations; their variance may differ"],"call_id":"call_62baac04","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":64},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":2},{"flag":false,"name":"r2","note":"","value":0.5401149553120992},{"flag":false,"name":"df_resid","note":"","value":62},{"flag":false,"name":"se_type","note":"heteroskedasticity-robust standard errors (HC1)","value":"HC1"},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null}],"figures":[{"ci_high":0.6219007378785918,"ci_level":0.95,"ci_low":0.42231332157059037,"derivation":{"note_pt":"","steps":[{"formula":"","result":0.5221070297245911,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":64.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":2.0}],"text_pt":"Coeficiente de avexpr na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de logpgp95.","unit":""},{"formula":"ep = \u221aV_jj","result":0.04992252631098114,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.002492258633270604}],"text_pt":"Erro-padr\u00e3o robusto \u00e0 heterocedasticidade (HC1): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":0.5221070297245911},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9989715170333786},{"note":"","symbol":"ep","unit":"","value":0.04992252631098114}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 62 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.avexpr","label":"Coefficient on avexpr in the regression of logpgp95","se":0.04992252631098114,"unit":"","value":0.5221070297245911}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":5.300317041930751,"ci_low":4.020449555298915,"coef":4.660383298614833,"p":1.1355641347475712e-21,"se":0.3201314965536011,"t":14.557715653681464,"term":"Intercept"},{"ci_high":0.6219007378785918,"ci_low":0.42231332157059037,"coef":0.5221070297245911,"p":2.5665704437506074e-15,"se":0.04992252631098114,"t":10.458345526671526,"term":"avexpr"}]},"tool":"infer.ols","version":"0.1.0","warnings":[]},"result_for_model_ref":"0e5be67e39fdc870af623030fc7081ea7fa9e07b4568a4a133872353b328a04e","result_sha256":"93ebf9ba2048a0a7492346b90a6955c7bec8c9ce1e39f0347a96eba5c26c2256","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f","seq":2,"synthetic":false,"task_id":"paper_repro.ajr_2001.tab2_c2.agent","timestamp":"2026-10-15T12:41:10.363931Z","tool_id":"infer.ols","turn_id":"call_268ad34d"}
{"attempt":1,"attempts":[{"config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false,"error":null,"finish_reason":"STOP","latency_ms":5668.338417075574,"raw_ref":"41c385335853987b7d24c7f02acdfab1fc06ea35a8efe09d364982087e4c0d9b","started_at":"2026-10-15T12:41:10.381080Z","usage":{"cached":16806,"input":20038,"output":77,"thoughts":205}}],"call_id":"call_6c0457b2","epoch":1,"event":"model","finish_reason":"STOP","latency_ms":5668.338417075574,"model_reported":"gemini-3.8-flash","model_requested":"gemini-3.8-flash","provider":"gemini","request":{"config":{"automatic_function_calling":{"disable":true,"maximum_remote_calls":10},"max_output_tokens":32000,"seed":null,"temperature":null,"thinking_config":{"include_thoughts":true},"tool_config":{"function_calling_config":{"mode":"AUTO"}},"top_k":null,"top_p":null},"contents_refs":["ca9f74584eae45bceaa9761be3d71ff46e87e52ef305400f8b72e2ee27d539b5","26d3e8dd421412b53dc7e28b466c59f6b2c4a5f2af9b6ed035465fdb1ee4c287","a583df065872984365d6469fbaaa7454b71c4ac99e8d8d782424085ad2526751"],"messages_refs":["3ea53614aa4095f6b78f0d7f9ca140c3245ca0e49d814602ed537685c108e854","3353be0c203c31cf4f57e1be893a42f482aaa77c239dca88b5db4d69fa75cbf3","d11b918ddee452e5f607a4eaa8928e8c41db3926e7a21cf3213d3f9ae8745dea"],"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"35fc5c0245791591c4c81ff6abc185344bac215d1b5b7a6806976a677d5cd33a","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd"},"response":{"text":"I will run the same OLS specification with classical standard errors to check the classical standard errors as originally published in the paper.","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","tool_calls":[{"args":{"dataset":"maketable2","se":"classical","subset":"baseco == 1","x":["avexpr"],"y":"logpgp95"},"llm_name":"infer__ols","tool_id":"infer.ols","vendor_id":"call_633550"}]},"response_id":"5snQau7COaammtkPh6iC-A4","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f","seq":3,"task_id":"paper_repro.ajr_2001.tab2_c2.agent","timestamp":"2026-10-15T12:41:10.368579Z","usage":{"cached":16806,"input":20038,"output":77,"thoughts":205},"vendor":true}
{"arguments":{"cluster":null,"controls":[],"dataset":"maketable2","fe":[],"intercept":true,"level":0.95,"se":"classical","subset":"baseco == 1","weights":null,"x":["avexpr"],"y":"logpgp95"},"attempt":1,"cached":false,"call_id":"call_6014ef91","card_version":"0.1.0","code_version":"38e979372a0f","dataset_digests":{"maketable2":"f2d6f55b0be66f82f98d12727d9a9b67b5c96add4aa011b150d70fa4b81b43c8"},"decided_by":"model","duration_ms":3.1866661738604307,"epoch":1,"error":null,"event":"tool","informed_by":[],"input_digest":"3d150e864c58d2ebc0666233d9a20f8d870028cb6b0ef496d2ef051147a66d4b","llm_name":"infer__ols","output_digest":"e315bf98193a7083cb7242db73ca69b047120823702ac61be8581f03bcd3dd49","rationale":"I will run the same OLS specification with classical standard errors to check the classical standard errors as originally published in the paper.","replay_class":"replayed","result":{"assumptions":["the conditional mean of the outcome is linear in the regressors","errors are independent and have the same variance for every observation"],"call_id":"call_6014ef91","datasets":{},"diagnostics":[{"flag":false,"name":"n_obs","note":"observations used","value":64},{"flag":false,"name":"k","note":"parameters estimated, fixed-effect dummies included","value":2},{"flag":false,"name":"r2","note":"","value":0.5401149553120992},{"flag":false,"name":"df_resid","note":"","value":62},{"flag":false,"name":"se_type","note":"classical (homoskedastic) standard errors","value":"classical"},{"flag":false,"name":"n_clusters","note":"errors not clustered","value":null}],"figures":[{"ci_high":0.6444141790277619,"ci_level":0.95,"ci_low":0.39979988042142034,"derivation":{"note_pt":"","steps":[{"formula":"","result":0.5221070297245911,"terms":[{"note":"","symbol":"n","unit":"observa\u00e7\u00f5es","value":64.0},{"note":"","symbol":"k","unit":"par\u00e2metros","value":2.0}],"text_pt":"Coeficiente de avexpr na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios de logpgp95.","unit":""},{"formula":"ep = \u221aV_jj","result":0.061185038536558864,"terms":[{"note":"diagonal da matriz de covari\u00e2ncia","symbol":"V_jj","unit":"","value":0.0037436089407201934}],"text_pt":"Erro-padr\u00e3o cl\u00e1ssico (homoced\u00e1stico): a raiz da vari\u00e2ncia estimada do coeficiente.","unit":""},{"formula":"IC = \u03b2 \u00b1 c \u00b7 ep","result":null,"terms":[{"note":"","symbol":"\u03b2","unit":"","value":0.5221070297245911},{"note":"quantil 0.975","symbol":"c","unit":"","value":1.9989715170333786},{"note":"","symbol":"ep","unit":"","value":0.061185038536558864}],"text_pt":"Intervalo de 95% de confian\u00e7a: a estimativa mais ou menos o valor cr\u00edtico da distribui\u00e7\u00e3o t de Student com 62 graus de liberdade vezes o erro-padr\u00e3o.","unit":""}]},"key":"coef.avexpr","label":"Coefficient on avexpr in the regression of logpgp95","se":0.061185038536558864,"unit":"","value":0.5221070297245911}],"notes":"","reason":"","status":"ok","tables":{"coefficients":[{"ci_high":5.476975586729225,"ci_low":3.8437910105004414,"coef":4.660383298614833,"p":7.188986293316496e-17,"se":0.4085062148990871,"t":11.408353480658997,"term":"Intercept"},{"ci_high":0.6444141790277619,"ci_low":0.39979988042142034,"coef":0.5221070297245911,"p":4.723617611116862e-12,"se":0.061185038536558864,"t":8.533246725220664,"term":"avexpr"}]},"tool":"infer.ols","version":"0.1.0","warnings":[]},"result_for_model_ref":"1d39a46904d411e8afd72c44517c269f97b6328c1c4585347470504641bf1a1d","result_sha256":"0708382969d6db4869f4c0b5fefbbae19da755cd088026e6367c516f5fb56da1","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f","seq":4,"synthetic":false,"task_id":"paper_repro.ajr_2001.tab2_c2.agent","timestamp":"2026-10-15T12:41:16.052658Z","tool_id":"infer.ols","turn_id":"call_6c0457b2"}
{"attempt":1,"attempts":[{"config_ref":"8ee0d84c29b117269608a8edad648afadcb20e12f00a5d88b7d64223f4b057e2","discarded":false,"error":null,"finish_reason":"STOP","latency_ms":17233.205792028457,"raw_ref":"6a3759dcd18f2028ab03fe303634506f920e0fbe05b3185ce53f7b8e3fd1fe1f","started_at":"2026-10-15T12:41:16.066569Z","usage":{"cached":18148,"input":20969,"output":535,"thoughts":1536}}],"call_id":"call_3d43467b","epoch":1,"event":"model","finish_reason":"STOP","latency_ms":17233.205792028457,"model_reported":"gemini-3.8-flash","model_requested":"gemini-3.8-flash","provider":"gemini","request":{"config":{"automatic_function_calling":{"disable":true,"maximum_remote_calls":10},"max_output_tokens":32000,"seed":null,"temperature":null,"thinking_config":{"include_thoughts":true},"tool_config":{"function_calling_config":{"mode":"AUTO"}},"top_k":null,"top_p":null},"contents_refs":["ca9f74584eae45bceaa9761be3d71ff46e87e52ef305400f8b72e2ee27d539b5","26d3e8dd421412b53dc7e28b466c59f6b2c4a5f2af9b6ed035465fdb1ee4c287","a583df065872984365d6469fbaaa7454b71c4ac99e8d8d782424085ad2526751","a535ab31b7d15407ab1dfe07f398da2d7ffdc37e4cfa8710c268b86aa45dc733","792bb786343abd2133dca05b46f4c8b9737ec98a1f7061d9e5f974d480c3c2f7"],"messages_refs":["3ea53614aa4095f6b78f0d7f9ca140c3245ca0e49d814602ed537685c108e854","3353be0c203c31cf4f57e1be893a42f482aaa77c239dca88b5db4d69fa75cbf3","d11b918ddee452e5f607a4eaa8928e8c41db3926e7a21cf3213d3f9ae8745dea","47be0b73fff841614eb2804780af10d43f4d780706ac7dca50bc7966a3fa6878","73f6cc662d90b8e23ecf3767eade31c90701ae556ece9518b9579e6d98fe28f5"],"system_stable_ref":"265d63d091732725cd5b15f51e1686b68b6b276ae88794f6120dc818830a2a97","system_task_ref":"35fc5c0245791591c4c81ff6abc185344bac215d1b5b7a6806976a677d5cd33a","tools_ref":"a44ef51f03df5188f084f1e0227b68df116b942a22fd50c0a88670cf3461fedd"},"response":{"text":"","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","tool_calls":[{"args":{"answer":"Na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios (MQO) correspondente \u00e0 Tabela 2, coluna (2), de Acemoglu, Johnson e Robinson (2001), utilizando a amostra base de ex-col\u00f4nias (baseco == 1, com 64 observa\u00e7\u00f5es), o coeficiente estimado para a prote\u00e7\u00e3o m\u00e9dia contra o risco de desapropria\u00e7\u00e3o (avexpr) sobre o logaritmo do PIB per capita de 1995 \u00e9 de 0,522107 (erro padr\u00e3o cl\u00e1ssico de 0,061185; IC 95%: [0,3998, 0,644414]; com erro padr\u00e3o robusto HC1 de 0,0499225; IC 95%: [0,422313, 0,621901]). O R\u00b2 da regress\u00e3o \u00e9 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 R\u00b2 of 0.540115.","caveats":["A regress\u00e3o por MQO reflete uma correla\u00e7\u00e3o e n\u00e3o identifica necessariamente um efeito causal devido a potenciais problemas de vari\u00e1veis omitidas e causalidade reversa, o que motivou a estrat\u00e9gia de vari\u00e1veis instrumentais dos autores.","A amostra \u00e9 restrita a 64 ex-col\u00f4nias com dados v\u00e1lidos na amostra base (baseco == 1)."],"figure_refs":["call_6014ef91:coef.avexpr","call_62baac04:coef.avexpr"]},"llm_name":"submit_answer","tool_id":null,"vendor_id":"call_553263"}]},"response_id":"7MnQarzhItvwqtsP-vOUyA4","run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f","seq":5,"task_id":"paper_repro.ajr_2001.tab2_c2.agent","timestamp":"2026-10-15T12:41:16.056061Z","usage":{"cached":18148,"input":20969,"output":535,"thoughts":1536},"vendor":true}
{"attempt":1,"call_id":"call_ea563d15","epoch":1,"event":"guard","numbers_found":["2","2","2001","1","64","1995","0,522107","0,061185","95%","0,3998","0,644414","0,0499225","95%","0,422313","0,621901","0,540115","2","2","2001","64","1","1995","0.522107","0.061185","95%","0.3998","0.644414","0.0499225","95%","0.422313","0.621901","0.540115","64","1"],"retries_left":1,"run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f","seq":6,"task_id":"paper_repro.ajr_2001.tab2_c2.agent","timestamp":"2026-10-15T12:41:33.305488Z","turn_id":"call_3d43467b","unbacked":[],"uncited":[],"verdict":"accepted"}
{"answer":"Na regress\u00e3o por m\u00ednimos quadrados ordin\u00e1rios (MQO) correspondente \u00e0 Tabela 2, coluna (2), de Acemoglu, Johnson e Robinson (2001), utilizando a amostra base de ex-col\u00f4nias (baseco == 1, com 64 observa\u00e7\u00f5es), o coeficiente estimado para a prote\u00e7\u00e3o m\u00e9dia contra o risco de desapropria\u00e7\u00e3o (avexpr) sobre o logaritmo do PIB per capita de 1995 \u00e9 de 0,522107 (erro padr\u00e3o cl\u00e1ssico de 0,061185; IC 95%: [0,3998, 0,644414]; com erro padr\u00e3o robusto HC1 de 0,0499225; IC 95%: [0,422313, 0,621901]). O R\u00b2 da regress\u00e3o \u00e9 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 R\u00b2 of 0.540115.\n\nRessalvas: A regress\u00e3o por MQO reflete uma correla\u00e7\u00e3o e n\u00e3o identifica necessariamente um efeito causal devido a potenciais problemas de vari\u00e1veis omitidas e causalidade reversa, o que motivou a estrat\u00e9gia de vari\u00e1veis instrumentais dos autores. A amostra \u00e9 restrita a 64 ex-col\u00f4nias com dados v\u00e1lidos na amostra base (baseco == 1).","answer_figure":{"call_id":"call_6014ef91","ci_high":0.6444141790277619,"ci_low":0.39979988042142034,"formatted":0.5221070297245911,"key":"coef.avexpr","ref":"call_6014ef91:coef.avexpr","se":0.061185038536558864,"tool_id":"infer.ols","unit":"","value":0.5221070297245911},"attempt":1,"caveats":["A regress\u00e3o por MQO reflete uma correla\u00e7\u00e3o e n\u00e3o identifica necessariamente um efeito causal devido a potenciais problemas de vari\u00e1veis omitidas e causalidade reversa, o que motivou a estrat\u00e9gia de vari\u00e1veis instrumentais dos autores.","A amostra \u00e9 restrita a 64 ex-col\u00f4nias com dados v\u00e1lidos na amostra base (baseco == 1)."],"epoch":1,"event":"answer","figure_refs":["call_6014ef91:coef.avexpr","call_62baac04:coef.avexpr"],"run_id":"20261015T124100Z-suite-paper_repro-gemini-c5ea8f","seq":7,"stopped":"answered","task_id":"paper_repro.ajr_2001.tab2_c2.agent","timestamp":"2026-10-15T12:41:33.308423Z"}
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