{"candidates":[{"content":{"parts":[{"text":"**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","thought":true},{"text":"A regress\u00e3o por M\u00ednimos Quadrados em Dois Est\u00e1gios (2SLS) estima o efeito da prote\u00e7\u00e3o contra risco de expropria\u00e7\u00e3o sobre o log do PIB per capita de 1995 na amostra base de ex-col\u00f4nias, instrumentada pela mortalidade dos colonizadores europeus."},{"function_call":{"args":{"dataset":"maketable4","endog":["avexpr"],"instruments":["logem4"],"se":"classical","subset":"baseco == 1","y":"logpgp95"},"id":"call_519035","name":"infer__iv_2sls"},"thought_signature":"EqIKCp8KAWkUfRMhcGLuNG__atUzVvvd-g8xTYEpZk3rlBSGtmKFCJ76V9hP27FTktPluxiw1Vrosi_oxf8o27fyZ2u-6-YT_CxayMK9QE5663qYsGO5kaTPTUFo8Gh6PZd0NYaW01RD_gOZxIZGxjz3hZKnJ0EGH6i5EKqjv_xbzDf3iYr11e-ZRcVDPVrJMuoTurgZ9TOcfx0YojvR5BKmRY2AyQmjoHEN1bbphM_yXaQ7nlisPHgbRk41sfYG3JzjYm_fl8-yFzm-vvfa79clbaDSItco-cakk9fb-v3NZyx0MXgLB4NPlLcrsbqxN_6I1xb7c5deNtjXyARnaaQlTwQ5uGBFkeoZ39hFIhJDC2RCq76xvNUfYmwxaBdd-4P1f6YHLY4TFozJxsGpHeMTodF_PpQxha2Nl9gNsEZo9WXSeyFmvGeAMEWViJGzQZ6QQ1Ake3VOcIfHvH_wsmm288qqIw9YyDndl5wEyZypw-h3Q8xrwqi5VBRZYbPyUAN5BpGnkSWftVMdvGooRSEUVNZMcFfdKZP5suMebhigf-JGIVDE1e6zzTBHW8mm6wv0KApBDorLM75wEmXS_1lFUteLq1X2IJmj1N3odimPLCLBRv0Yd7YnUyUlMuF2xdDd41b9rUtLx9jRt1etIT73_CtBaCvYirradNmLLr33isGy1RqjAvhGkQn7vfuxNdWmPU82CM4Irh1p3S2wWvzgVjkdvrkY8-JNXpxrHQ5Fv1fvUjlwl4lY5psogPP-H_2zqqcuE0bUreoq8HQB4JpX0773OhRdRCu3MWCv3eLPbl0v9YRkpthQeCDblJLmVOCWUKwbRMpKsE0CmKB3nO8EY6XAZIsNSn3K9YyoFeM4LTv9LvSt7fp0cL4RHH738J2xa70ePz4Ow9jdOLoKk0Ha8IsND0Gq-vLxKfEpnIo_7TZBh1mv_sjsk7k-svKGopbOURwlqmkuXmv9ahOWIy_gUv1IrZfUUL9fWGQorldWtjFbIw88RRe_m7Uqvke2qvhQbxwkktF-zpaMrfY1nHwo4i1VrOCH1TJ4qaU2HdhyDsTN1eRls4Ge0kGVehGs76-ACrfYDhvSfQRjJuQv0P5KYmaxOu3iUcSkw1eF5GLWKnMbAFLC1Fh9n3UKtiZ_m-yCFL4MAFjGj1K4NoWaQKNxzjZP10eDgeP9-5jFVY-amqCZUGT3WcyZ0yCLeA9oGXor0CRoA87VDcIr9KcPKTvJhSB7SfjoVVBMShQwc2MnyahDJ8BKMQi4jdf70Pi6hIO-vOCy-GqYRjduh7mdv5lQcVMWCk946zLG9a0EGWnDX30k2IGlSnrQRXoPMgY1U9si8zRC687qL8VtkaOmjm1b36Pxu1bLfmaY6n0x0n2myweiGbUzBAGq5x8c9qHXnDLRMF0rybQJR_5H-yr1fxBbRHJ3KyaepxtfyKn4Cve1jmZ_E5Yhfp1dpExWc9JDHOawtdMn0sBUCj9ir5WlJLHWFZ7HM74-IMi9rTHl0X89TjpIXQpoY_1aCiNo6Q8G3QHpNADfe8cwWDOKtHw9RpZe4tu_xZB0qITYAl0zGJuzq_cjNrJsivhhHvGoQGKDmp7N6vSOFJfxmjDcmh8t81hwDS-IoxEebjFPp1R63KGozV7YNOoQEikwySQTyKtplWnkW4PGr9eCODD5wv279zSlmgaym6BbogWQ_6A4ABO9DiMgQD56GZQEpvxQPzY9QpiXi1UacAzG_7aeQISiUjfTs88h"}],"role":"model"},"finish_reason":"STOP","index":0}],"model_version":"gemini-3.8-flash","response_id":"3cnQao32Jszoz7IPnYC4iQU","usage_metadata":{"candidates_token_count":131,"prompt_token_count":18474,"prompt_tokens_details":[{"modality":"TEXT","token_count":18474}],"thoughts_token_count":377,"total_token_count":18982}}