Tarefa paper_repro.ajr_2001.tab2_c2.agent

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Pergunta feita ao analista

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.

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1. 0Início

   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.

   maketable2 (agent)

   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 f8213d9b42290cf0d3a2e968483bf3b5964e33261b8e48f92e9ea4c87c709b92 · frame —
2. 1Turno do modelo
   call_268ad34dgemini-3.8-flashFim: STOP9.710 ms18415 de entrada · 85 de saída · 622 de raciocínio · 0 em cache

   Texto visível

   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).

   Resumo do raciocínio, como declarado pelo modelo

   **Reproducing Acemoglu, Johnson, and Robinson (2001), Table 2, Column (2)** Okay, 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. First, 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. Next, 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. Now, 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. The `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.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "maketable2",
       "subset": "baseco == 1",
       "x": [
         "avexpr"
       ],
       "y": "logpgp95"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
3. 2Ferramenta
   infer.olsv0.1.0call_62baac04decidido pelo modeloreexecutável

   Motivo: 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).

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "maketable2",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": "baseco == 1",
     "weights": null,
     "x": [
       "avexpr"
     ],
     "y": "logpgp95"
   }

   Figuras call_62baac04

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.avexpr Coefficient on avexpr in the regression of logpgp95	0,522107	0.5221070297245911	0,04992250.04992252631098114	0,422313 – 0,621901

   Diagnósticos

   • n_obs = 6464
   • k = 22
   • r2 = 0,5401150.5401149553120992
   • df_resid = 6262
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (1)

   {
     "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"
       }
     ]
   }

   Digests dos dados

   maketable2 f2d6f55b0be66f82f98d12727d9a9b67b5c96add4aa011b150d70fa4b81b43c8

   sha256 do resultado

   93ebf9ba2048a0a7492346b90a6955c7bec8c9ce1e39f0347a96eba5c26c2256

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_6c0457b2gemini-3.8-flashFim: STOP5.668 ms20038 de entrada · 77 de saída · 205 de raciocínio · 16806 em cache

   Texto visível

   I will run the same OLS specification with classical standard errors to check the classical standard errors as originally published in the paper.

   Resumo do raciocínio, como declarado pelo modelo

   **Checking Classical Standard Errors for the AJR (2001) Replication** Okay, 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. My 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.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "maketable2",
       "se": "classical",
       "subset": "baseco == 1",
       "x": [
         "avexpr"
       ],
       "y": "logpgp95"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
5. 4Ferramenta
   infer.olsv0.1.0call_6014ef91decidido pelo modeloreexecutável

   Motivo: I will run the same OLS specification with classical standard errors to check the classical standard errors as originally published in the paper.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "maketable2",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "classical",
     "subset": "baseco == 1",
     "weights": null,
     "x": [
       "avexpr"
     ],
     "y": "logpgp95"
   }

   Figuras call_6014ef91

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.avexpr Coefficient on avexpr in the regression of logpgp95	0,522107	0.5221070297245911	0,0611850.061185038536558864	0,3998 – 0,644414

   Diagnósticos

   • n_obs = 6464
   • k = 22
   • r2 = 0,5401150.5401149553120992
   • df_resid = 6262
   • se_type = "classical"
   • n_clusters = —

   Tabelas (1)

   {
     "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"
       }
     ]
   }

   Digests dos dados

   maketable2 f2d6f55b0be66f82f98d12727d9a9b67b5c96add4aa011b150d70fa4b81b43c8

   sha256 do resultado

   0708382969d6db4869f4c0b5fefbbae19da755cd088026e6367c516f5fb56da1

   Resultado mostrado ao modelo
6. 5Turno do modelo
   call_3d43467bgemini-3.8-flashFim: STOP17.233 ms20969 de entrada · 535 de saída · 1536 de raciocínio · 18148 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **Analysis of the AJR (2001) Regression** Okay, 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. Now, 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. The 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]. Of 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. ``` In 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. ``` ```tool_code submit_answer ```

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "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.",
       "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)."
       ],
       "figure_refs": [
         "call_6014ef91:coef.avexpr",
         "call_62baac04:coef.avexpr"
       ]
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
7. 6Guarda
   resposta aceita

   Números encontrados

   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

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
8. 7Resposta

   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. In 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. Ressalvas: 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).

   • 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).

   Figuras citadas: call_6014ef91:coef.avexpr, call_62baac04:coef.avexpr · Parada: answered

   Figura da resposta: call_6014ef91:coef.avexpr = 0,5221070.5221070297245911 (se 0,0611850.061185038536558864)
9. 8Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
10. 9Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_6014ef91:coef.avexpr (infer.ols)
11. 10Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.ols
12. 11Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'logpgp95'
13. 12Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x = ['avexpr']
14. 13Pontuação
    #5argumento igualpassouobrigatóriainfer.ols.controls = []
15. 14Pontuação
    #6argumento igualpassouobrigatóriainfer.ols.fe = []
16. 15Pontuação
    #7diagnósticopassouobrigatória64 vs 64
17. 16Pontuação
    #8figura da resposta próximapassouobrigatória0.5221070297 vs 0.5221070297245913
18. 17Pontuação
    #9figura da resposta próximafalhouregistrada0.06118503854 vs 0.04992252631098126
19. 18Pontuação
    #10figura da resposta próximapassouregistrada0.5221070297 vs 0.52
20. 19Pontuação
    #11figura da resposta próximapassouregistrada0.06118503854 vs 0.06

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• época 1, tentativa 1passoupontuada(exibindo)

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