{"candidates":[{"content":{"parts":[{"text":"**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","thought":true},{"function_call":{"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"]},"id":"call_553263","name":"submit_answer"},"thought_signature":"Ep0tCpotAWkUfRMv7I0tpfzcn9y7i6GDwiQPkKYugkgb1ZrJy68Q5M8pJsN30jDpRMW08QHkx5MwYr9dME1-ylcAAFP3vuadBEQQ4n3V1bTcW7zyBqV9YIVdYQ1ROsFvL8148jqXMbC5aeXPZZq5Z5_aMdFYVDousovHXvM8rAbwsHkPk65b71-NOfU5GcOvXTidmt5nx_KYBDun90IPUV7hFyxhKOIcq2-N5puiRmzdVroFiBSMHvuwsQmsjwlN50Fl3Fqvjd5DSRKiT-xOO8FfE30MtgfARTYy2734-HpS7FnD_lg8FzwKnIjCOD3RtlxkpTRlRETx392mlcm9DoHSAa6oGVzAFQTcYhPCeOuhlrSnW40FoAhavWnrSGFDXpMit8BwIZbXBHFSHNHv8ql3w_tEiyUpCYYp9hftU0L5Zm-OR6YoCRPzvT7lnpfCqKma_jD_aEbHcuCdTnbBecaqw0BYtgw9MFzLdPPQ5vdGtcAUaNiq3kPPjeSlpE8AIB1v90eFXz632Nx0_442esZmArR-HLAfEQCUoau6DSAKmjeSL0DUb2pLCo3TaVuhoHVb8D5e-8dS7n7f4WnrrH3_UEnufPEwVD4_UEbLBU6NagSkJlMJdKHFqWPceE9hQCYBBlAxSkye_DGG-pWPc59pJFjZeYurDWA96Jqs0p22kOxWZovK2DzQHz0yC4R49KeHqueZ8hGaXY_pTXNfWXL-MCDD-_9ceSZYYrw6jKEoSli0ed8Ft6b0SZf7CTOj-5OMluto86SxoP9hgC86OmYsOpf3KI1y6QjReIHBK9ZOS0XmKM_i-54czIpEGb8hr-BXiB2s84hMYbTBYQWENBrnrmFBNqmY2GFFnBH1epl1y9aVRODKV_crVn5ZwZWapYvpgDysjvtyFN8E-apbpFtu-BkII57gUP6u25R2LAD15_vFbUwsZwkKBpGR3sxNZ__D0NVaH_rm73oWQzbfomm-eEwTIfJZgjaNtgdlxWO5lLXLIcf_YORn3-rIPCOJpwMX1oa1teujl_LCaciSM78bjoIZhuJbNrC5UEhXZ1_eqwGKMSMeTapfdq3VDr4QC3nySN_tSc3hGZ5G02_4dMa_bX5H3IS_RHalUHdvDdqA1i--1xjYQHF8cW_fOkYfPHTjFtDNFsCg8QOqLOQY5O6qksR4VmJu9pdnIgRP7EN0icsxYT-EpgL7H0WkDVztvASvmdRi0dKgwn0JS0l_Bn3ipRQDUIZMgy94knBvRtfAm29qjEHyxRigkcOTvdAV053lpk5SVOR564jxbZ74aRcLxO4AxSG9QALcsaj6_PSOkFfgD30ZkysbzHCCk_JSeGhbozUjhalAqyEE9BrEScvb4SADq3NwSlcCXRMFkJYbdq50iOMd1y_vTTf8NkkwiRJ-0B3RDRF5SzlsA24YfnPO69AKodXscn_WtEFNet_y_4qCvgaoOgQ21xxXlafdMld_8mxsVfULUkB4luVfzi3hn3AmDhExuj1_Gnm42diJYdfYOhfcsvY5Otmb5Vrvx4lDqwBJh1n_VE_712kDBfHI8t7bT8A-qxQIhRI8PxL8f2rGOAxg6AeKRIDQUcB0Bf5I19iKerjI0bs1j-wfqYY_iyDOKPbmMVO2rMS8dZsV4mHAnZ852LvUJVEq9ilTb5sAurwCdiN4wLm0pA7TXsc1YLWPwGRbjPT0RhQ90hn2q7uM83Jt2AEHAaJKY4bSfSEgA_rEEvN4MNeYM5EPCXmoMB_9ffIXVV2QYDy-RBMRnWb18CSGCH0_WHiycTkOWxiqe9EHNWgEfwL3ST6pdpEEtGRwJiIZKVj7eJ8wNnIOKKbXoE8458r5PhaJyGH14C5c5ml6EgZF0tkRcqW_2uFoV-PRifQc7HVzpPJ1XOTB8sgBnXsw25wM_64hepWTAnRyGHIUVb6bulBRnGZcdBPJNDWXkpqrIgEEtf5dIZLwj8L-1gv7F5WCgyY7r3kVGcJmOHGS5IW2-20_9Lj-fATkF42TubCaZapjp4J45Ses30ULQ0pbi1CFyUBHAtZw94ANIJons_uLqB6F_nGc0-7MJXLUGqBmb3uOw3joeQqUWcDcUcI4p3Vx1yFxmrcKzITk5t4aBCzuh8yOWQlGrJWQUpH9AS6mNlyVH8eU_KA6-h1lUiuDAdU1_YdMF4lFqVadNRE-dGIB4k80F4u4GiBDc6jmpd76xwyos2oUkrg1AfE13rOHTjCswSwztD1dtylIGHtKhCx1Equgb5CVcE7ua0RsUoOGiy3HYEg-BCLzzz9PtbPkdfd44HOiCLAcjOaupNfme5HCOhRbwNkCVtzYjYlTxWhFgv-0uSe61jNNwwB-_rMzzfgvDsjafY5KBFR5-F2BpovFOs2QJgQ98jtRpm-AA8KHCqhNyDmvAkZNGARucW0C4v3GsYoGfKswYQ6StzLZl4iyQl2KA1JGtNdIYKMUQ4Yjyu9UebidoKUKTi2oi3aP4A2RkwezZ39c0vaqShwL1-iXQevLvV30iSJdi6BFXq8Nwx76hjZqVgVPstVqM1r70a63WUNpwktQXuGLOf0-g5-vGoXygvNQfrGUHU4ZbG1UClZ3PKstADm4u-Ym6SyJxC07JJR15qW45ytLCi4WWqfVcbPWxGKE_iK0Y9zE-j6FcFfdEO6m4gG0O4t1HXp-QTZpNjDAfUSoN4Fej-C8fcYwzoCznZpTY3OQZpOHZx33FqlNdJynuMIu850xcxjRChS0A26vRJShH2Ntm3IL8p7-Z2RD84IrFZQ-JmdBLFcPSyok6z3FrgyLnLOY8zn63ecJXA_1DPEjEXxOM5MIgrNp5oQnd_Skd3tokK0UpntRxNMkY5zLsN276gtNfocc5w9T4WYdyLuwt2wJTJZFdpSUC5_usP1mkSCu6TJjCRH0-2peQWMkgsxN6OASUPgkIBXRMhszo3-lApE2huZYRZDEQka1J5AeigNEJqLav_ymGstTmv0HIMgejGwICBN91M0QnMo05satXn5V37RB53ukbxyzEIGjkbLg3OGUciItl-CVIgRJrDVky042c_NCPe8YRb410ywvQhuJ1VCq6FDvW6fifSyQpe6h1oO18KBOjE9bvcqta7oTJAJ2oSQKD3pdOXe6ahsfx5D86ysNrHNDrv0HwVukqUhtsLmelGVObbMjO_aHnN7Hi-21bwYFed81qh2mY_ouoTcmoiMIATNek5mZnMFbxPyyaD62ql7mq3j2L5fnfsBl2dnqEaHtaK2zi_VON93jiwJGg9e8RsQmI-1EbKGLztTa3BVjwIImWfsgBwI9c95NuWOddw00MHzg7xTIydUjobA__isVoRGJgUFlLSfGFimRPhmp8BcoX84fE6gJW75SxpkBBzJxaMG8cwNNjGpM5IYChBtTxmGfHzBn2Ii0VqELUN2zY2ixMijNk3N4UrE1_hX3Oqjsg5p_ii07_Ktgf4LBJPcjE5tTl3B2tKrzRWe17ggWAzY6WyrMFn9XQ5nbwg6yPmFuA3PPUHTDXNvAhbxeCVMGx7JlbNOE_d4lQP1Y6bgK4k3BOuonT-jDO9Un000SM6C4aemhiWXwWU3NJY_JbMakBlC2z8UxztA7XUfeSHJc7azgUMOIgF293S7JtnOPtjU8RuYlHXJ_Mh0AvsMTKvSeYZU8kiUHsOfHItPcKNxJ-I6VslQDthOeat4hSqu1_ZCJYVsk1eZv1msCpdn88xfvnfx8y2oHFvjhPWC_5u4wrpx1AhmsaQ6wnhtRIZzsM5yC0UohUCfI2tIKtDRFmb9tvinA39GJGlCAleYYv8XgJarbtLaJnIWeovpwAJSIprUFh3oRjNlRdL-6XF11vM7PmmDK9L4mSACYyKTlQe7qXfKjcyT2lNBBfEnqI-eP-bN7Nqs4Msdn4m6ILKPqWZBDvdL3UVYgGP1T7z6xSSL_oGVJ-Ii-Ddmbk9XUICPOxmXi7g3Fg4m_DGRm29-5dhZQTw7oTnIWqno8HpBEmyjFUp9HbXOm9ZYq07cAuiOQiH5n8FvkQvlFaXX3RUHtr1YrfsfWXphpMLLq6sNDGJqF2cSbPCwHE3XvTQ-pEVy1irj0SYZcdlInScJE9krDG6uluLucDoqqsFWvBHvdlVp2lld19oHmbjDYFQO0PAuYiltWzARccbWbOzUow6N7vUMLapnqM8tpWp-xzCJ3-5ILjt5g8yu7NaHQr2dFtrG_UG8gCxd4VGvRc5e_EoVvDWBesorClUN5d3PZNguNDOJi5KGx0_QSGJNM2cdV_cdyPaLm2r4ngYGpw2DYctpHsaC_XBbsK7xcFKPhgTjDuqDZxED39VJYcQftbSVFQ8D1-aIcQSVNJ3OvCDOhQtGnA59OqzzX9wnO2Msv7MR0rAywRmV9GfIpDIN62xCw3EDM9TkVTV21__R9A5ggRG6XO1xs9dMHUKR9RhwTiA4vEoCkQI0S4d0_xF55K0OKIyV3FRFfDid8qbjyy7ssMcjTnnLayu2r8dxCCC9ZWl1n0BMNsViVGCPg85qO8pZFijaUvdZ9CEbPN93LJRC2RmIfrAFD_pwjx1qE0o5GbxGEd3Rv8DqtpXzX86RMRTHLIDuxET4QLhpvv6e5epTdf0gCa2PV-Lc8vGV7YAlLE5Ll8NWz1I-tuuBcSl4UBFn19JI6bI-jAoUEmyU4PWTtN9ZF8TUBmL-ErWBZs90xMpHi_AeEIT6IojPhEVc1hQDE3XWzfe1Smqwxs-VrH0Zzjl1gUzkQnlwQvVCPlHyStvRcgujYXlAcSovLOeaV3Bfjavk7u2JBatiKi5D1mHHY4HWr7Szrt4HVIn95Z4_7qoZyNwuvItS44Dms4p21HgX7fu86vbR2rMs7G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