Tarefa paper_repro.ajr_2001.tab4_pa_c1.design

Veredito

Pergunta feita ao analista

Do institutions that protect property cause higher income? Among the former European colonies in the paper's base sample, estimate the causal effect of average protection against expropriation risk (1985-1995) on log GDP per capita in 1995, using a source of variation in institutions that is not driven by income itself.

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Verificações

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   Do institutions that protect property cause higher income? Among the former European colonies in the paper's base sample, estimate the causal effect of average protection against expropriation risk (1985-1995) on log GDP per capita in 1995, using a source of variation in institutions that is not driven by income itself.

   maketable4 (agent)

   The authors' replication file for Table 4 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; rich4, 1 for the four 'neo-Europes' (United States, Canada, Australia, New Zealand); 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); logem4, log settler mortality (mortality of European settlers); 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 cd3a1cb197e3065411fce6fabbe06161555468049e6810a28c608d6e361aed34 · frame —
2. 1Turno do modelo
   call_02ba1a7fgemini-3.8-flashFim: STOP8.417 ms18430 de entrada · 72 de saída · 458 de raciocínio · 0 em cache

   Texto visível

   I will inspect the variables and sample summary for the base sample in `maketable4` to understand the data before estimating the model.

   Resumo do raciocínio, como declarado pelo modelo

   **My Analytical Strategy for Assessing the Impact of Property Rights on Income** Okay, here's the game plan. I'm tasked with examining the relationship between institutions that protect property rights and income levels, specifically within the framework of Acemoglu, Johnson, and Robinson's (2001) seminal work. The core question is: Does stronger protection against expropriation lead to higher income? And, crucially, I need to establish a *causal* effect, not just a correlation. I'm focusing on former European colonies from the original paper's "base sample" (`baseco == 1`). My treatment variable is the average protection against expropriation risk between 1985 and 1995 (`avexpr`), and my outcome is log GDP per capita in 1995 (`logpgp95`). The real challenge lies in finding a valid instrument. I need a variable that's correlated with property rights protection but *not* directly influenced by a country's income level. The text suggests using historical European settler mortality (`logem4`) as the instrument. This seems reasonable, as higher mortality might have led to less extractive institutions and therefore better property rights. My immediate thought is to reach for instrumental variables (IV) estimation. The task mentions `infer__iv_2sls`, which is likely a 2-stage least squares (2SLS) implementation, the standard approach for this. Before I dive into the estimation, I'll need to double-check my understanding of the data (`maketable4`) and the variable definitions, just to be absolutely sure. Then I'll jump right into 2SLS, using settler mortality as my instrument for property rights protection. This seems like a straightforward application of classic IV methodology, a common approach for analyzing historical data of this kind.

   Chamadas pedidas

   • data.describe

     {
       "columns": [
         "baseco",
         "avexpr",
         "logpgp95",
         "logem4"
       ],
       "dataset": "maketable4"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
3. 2Ferramenta
   data.describev0.1.0call_ba7d7e94decidido pelo modeloreexecutável

   Motivo: I will inspect the variables and sample summary for the base sample in `maketable4` to understand the data before estimating the model.

   Argumentos

   {
     "columns": [
       "baseco",
       "avexpr",
       "logpgp95",
       "logem4"
     ],
     "dataset": "maketable4"
   }

   Figuras call_ba7d7e94

   Chave	Valor	Valor bruto	Erro-padrão	IC
   n_rows Rows in the dataset	163 rows	163	—	—

   Tabelas (1)

   {
     "columns": [
       {
         "column": "baseco",
         "dtype": "float32",
         "max": 1,
         "mean": 1,
         "min": 1,
         "n_missing": 99,
         "n_unique": 1,
         "sd": 0
       },
       {
         "column": "avexpr",
         "dtype": "float32",
         "max": 10,
         "mean": 7.06649138021075,
         "min": 1.6363636255264282,
         "n_missing": 42,
         "n_unique": 80,
         "sd": 1.804286796987983
       },
       {
         "column": "logpgp95",
         "dtype": "float32",
         "max": 10.288749694824219,
         "mean": 8.30250908233024,
         "min": 6.10924768447876,
         "n_missing": 15,
         "n_unique": 138,
         "sd": 1.1053422036053233
       },
       {
         "column": "logem4",
         "dtype": "float32",
         "max": 7.9861650466918945,
         "mean": 4.595983834102236,
         "min": 0.9360933303833008,
         "n_missing": 76,
         "n_unique": 44,
         "sd": 1.3033334764007718
       }
     ]
   }

   Digests dos dados

   maketable4 dc8a5df19428e33e3bb4109922ccbd092a69bde0d19a217451baa154782debfc

   sha256 do resultado

   d112fee6bcff60e8df5eef7ab0fbcc0415bd74a571a62dd8b33d0ccd4cc99792

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_63688480gemini-3.8-flashFim: STOP6.320 ms19672 de entrada · 107 de saída · 233 de raciocínio · 16712 em cache

   Texto visível

   I will estimate the effect of expropriation protection on log GDP per capita via two-stage least squares (2SLS) using log settler mortality as the instrument, for the base sample of former colonies (`baseco == 1`).

   Resumo do raciocínio, como declarado pelo modelo

   **Analyzing the AJR 2001 Replication** Okay, here's what's running through my head as I look at this data. First thing I'm noticing is that the `baseco` variable is equal to 1 for 64 rows. Bingo! That immediately clicks, confirming that this is the 64-country base sample used in Table 4 of AJR (2001). That's a good starting point. Now, let's get into the heart of the matter: checking the instrument and the 2SLS regression. Specifically, I need to focus on the endogenous regressor, `avexpr`, instrument `logem4`, and the outcome variable `logpgp95`, all within that critical `baseco == 1` subset. Wait a minute... shouldn't I also throw in an OLS regression as a benchmark for comparison? Absolutely. It’s always a good idea to have that point of reference. Right, let's look at the tool description for `infer__iv_2sls` here; it's designed for exactly this. It’s a two-stage least squares estimator, perfect for identifying the effect of an endogenous regressor using excluded instruments. It spits out the first stage results, along with the all-important F statistic, and, if we're overidentified, the Sargan test. And the documentation wisely recommends running `infer.ols` afterward, which I’ll keep in mind for scaling effects as well. So, let's get the ball rolling and run `infer__iv_2sls`. I’ll set the outcome to be `y='logpgp95'`, the endogenous variable will be `endog=['avexpr']`, the instrument will be `instruments=['logem4']`, and the subset is going to be `subset='baseco == 1'`. Let’s see what comes out of that.

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "maketable4",
       "endog": [
         "avexpr"
       ],
       "instruments": [
         "logem4"
       ],
       "subset": "baseco == 1",
       "y": "logpgp95"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
5. 4Ferramenta
   infer.iv_2slsv0.1.0call_0f97b69ddecidido pelo modeloreexecutável

   Motivo: I will estimate the effect of expropriation protection on log GDP per capita via two-stage least squares (2SLS) using log settler mortality as the instrument, for the base sample of former colonies (`baseco == 1`).

   Argumentos

   {
     "cluster": null,
     "dataset": "maketable4",
     "endog": [
       "avexpr"
     ],
     "exog": [],
     "instruments": [
       "logem4"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": "baseco == 1",
     "y": "logpgp95"
   }

   Figuras call_0f97b69d

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.avexpr 2SLS effect of avexpr on logpgp95	0,944279	0.9442793851547994	0,1789140.17891351798924446	0,586636 – 1,30192

   Diagnósticos

   • first_stage_F.avexpr = 16,320616.32061844215525
   • partial_r2.avexpr = 0,2701310.2701313941078145
   • weak_instrument = false
   • n_obs = 6464
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 4.293917992701939,
         "ci_low": -0.47458491160845706,
         "coef": 1.909666540546741,
         "p": 0.11444349079561447,
         "se": 1.1927390819923254,
         "t": 1.601076521578278,
         "term": "Intercept"
       },
       {
         "ci_high": 1.301922411627538,
         "ci_low": 0.5866363586820607,
         "coef": 0.9442793851547994,
         "p": 0.0000017722510641748919,
         "se": 0.17891351798924446,
         "t": 5.277853768498172,
         "term": "avexpr"
       }
     ],
     "first_stage": [
       {
         "ci_high": -0.3065384195674211,
         "ci_low": -0.9070180781003664,
         "coef": -0.6067782488338938,
         "endog": "avexpr",
         "instrument": "logem4",
         "p": 0.00014986892666534062,
         "se": 0.15019715223959304,
         "t": -4.039878518242256
       }
     ],
     "reduced_form": [
       {
         "ci_high": -0.42710584400315166,
         "ci_low": -0.7188305394651986,
         "coef": -0.5729681917341751,
         "instrument": "logem4",
         "p": 7.141770691068322e-11,
         "se": 0.07296869739669624,
         "t": -7.852246403950703
       }
     ]
   }

   Digests dos dados

   maketable4 dc8a5df19428e33e3bb4109922ccbd092a69bde0d19a217451baa154782debfc

   sha256 do resultado

   b0cc8405f907cad097e8070b8c46c1c9b7050328d8c6d8ccf7ed9fe1ef1559c7

   Resultado mostrado ao modelo
6. 5Turno do modelo
   call_f4a11607gemini-3.8-flashFim: STOP6.091 ms21097 de entrada · 78 de saída · 226 de raciocínio · 18197 em cache

   Texto visível

   I will run an OLS regression of log GDP per capita on protection against expropriation risk in the same sample for comparison with the 2SLS estimate.

   Resumo do raciocínio, como declarado pelo modelo

   **My Analysis of the Instrumental Variables Regression Results** Okay, let's break down this `infer__iv_2sls` output. I see the Two-Stage Least Squares (2SLS) estimate for the coefficient of my endogenous variable, `coef.avexpr`, is 0.944279. The standard error is 0.178914, giving me a 95% confidence interval of [0.586636, 1.30192]. That t-statistic of 5.28 and the p-value of 1.77e-06 are definitely statistically significant, which is what I would expect with that narrow interval, given the size of the coefficient. Now, looking at the first stage, the coefficient on `logem4` is -0.606778, with a standard error of 0.150197, and a t-statistic of -4.04. The first-stage F-statistic is 16.32, which is reassuringly above the critical value of 10. That's a good indication that the instrument is reasonably strong, which is reflected in the partial R-squared of 0.27. The weak instrument test confirmed my suspicion by returning a `false` value. Finally, I have a sample size of 64 observations. Given the common practice in this field, I should probably run an OLS regression as a comparison point. That should give me a good baseline, allowing me to further probe the impact of potential endogeneity issues. I will also be sure to check for scale effects.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "maketable4",
       "subset": "baseco == 1",
       "x": [
         "avexpr"
       ],
       "y": "logpgp95"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
7. 6Ferramenta
   infer.olsv0.1.0call_233c60dddecidido pelo modeloreexecutável

   Motivo: I will run an OLS regression of log GDP per capita on protection against expropriation risk in the same sample for comparison with the 2SLS estimate.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "maketable4",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": "baseco == 1",
     "weights": null,
     "x": [
       "avexpr"
     ],
     "y": "logpgp95"
   }

   Figuras call_233c60dd

   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

   maketable4 dc8a5df19428e33e3bb4109922ccbd092a69bde0d19a217451baa154782debfc

   sha256 do resultado

   5e13ee1b8ceb35211a24b32059eacef4a76f9cfde43d02ff645ee83a643f1584

   Resultado mostrado ao modelo
8. 7Turno do modelo
   call_199d2575gemini-3.8-flashFim: STOP14.081 ms22179 de entrada · 74 de saída · 240 de raciocínio · 18469 em cache

   Texto visível

   I will transform the estimated 2SLS effect from log points into a percentage change in GDP per capita using `core.scale_effect`.

   Resumo do raciocínio, como declarado pelo modelo

   **Scaling the 2SLS Coefficient** Okay, let's see if we can get a handle on this 2SLS coefficient. I want to understand the magnitude – can we convert this into a more interpretable percent or scale effect? Right, time to dig into the `core.scale_effect` function. Wait, before I run that, let's just make sure I have the relevant skills loaded, or maybe just refresh my memory on what `core.scale_effect` actually *does*. Ah, here it is: "Converts an estimated coefficient into a percent effect...". Perfect. Now, the outcome variable, `logpgp95`, is *log* GDP per capita. So, a one-unit increase in `avexpr` translates to a 0.944 *log point* change in GDP per capita. Okay, let's get this in percentage terms. That's just 100 times `(exp(0.944) - 1)`. Alright, let's try `core.scale_effect` with `transform='log_points_to_percent'`. The question is, can this function work on `call_0f97b69d:coef.avexpr`? Time to call `core.scale_effect` and see what we get!

   Chamadas pedidas

   • core.scale_effect

     {
       "coefficient": "call_0f97b69d:coef.avexpr",
       "transform": "log_points_to_percent"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
9. 8Ferramenta
   core.scale_effectv0.1.0call_ac13a82cdecidido pelo modeloreexecutável

   Motivo: I will transform the estimated 2SLS effect from log points into a percentage change in GDP per capita using `core.scale_effect`.

   Argumentos

   {
     "baseline": null,
     "coefficient": "call_0f97b69d:coef.avexpr",
     "sd": null,
     "transform": "log_points_to_percent"
   }

   Figuras call_ac13a82c

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Effect as a percent change (from log points): 2SLS effect of avexpr on logpgp95	157,096 %	157.09600389260225	—	79,7931 – 267,636

   Diagnósticos

   • transform = "log_points_to_percent"

   • the standard error is not carried: a percent effect is a nonlinear function of b, so only the interval, transformed endpoint by endpoint, keeps its meaning

   Digests dos dados

   sha256 do resultado

   11ee1a7d9e43d12034d4cc3dd5974bb6723bff3ecb6b2bf569e07ebc7dbbefa6

   Resultado mostrado ao modelo
10. 9Turno do modelo
    call_29e6d78dgemini-3.8-flashFim: STOP4.548 ms22964 de entrada · 38 de saída · 38 de raciocínio · 20266 em cache

    Texto visível

    I will load the instrumental variables skill to review identification diagnostics and best practices for reporting IV results.

    Resumo do raciocínio, como declarado pelo modelo

    Okay, here's my take on those thoughts, tailored for an expert audience: **Considering Skill Granularity and Discoverability** Right, so I'm thinking about how we're structuring these skill guidelines. It's crucial to ensure we're both comprehensive *and* granular enough to be truly useful. We've defined some skills, but are we being too broad in our categorization? I'm wondering, for instance, if there are other, more specialized skills that warrant their own explicit guidelines and calls. We already have the foundational ones, but perhaps there are other sub-skills at play, for example, `load_skill('instrumental-variables')` or `load_skill('reporting-numbers')`. If we can clearly modularize and define these, it'll assist in providing a smoother and more robust user experience for complex tasks. It also improves searchability and reusability of modules. Are there other obvious edge cases where this might be relevant?

    Chamadas pedidas

    • load_skill (meta-ferramenta)

      {
        "name": "instrumental-variables"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
11. 10Habilidade
    instrumental-variablesTexto da habilidade
12. 11Turno do modelo
    call_adad72d3gemini-3.8-flashFim: STOP4.070 ms23732 de entrada · 36 de saída · 11 de raciocínio · 20944 em cache

    Texto visível

    I will load the reporting-numbers skill to review the standards for formulating the final answer.

    Resumo do raciocínio, como declarado pelo modelo

    Okay, here's my take on those thoughts, tailored for an expert audience: **Loading the "reporting-numbers" Skill** Alright, time to get down to brass tacks. I'm initiating the load sequence for the `reporting-numbers` skill. Given my existing expertise, I know exactly what this entails. I anticipate this skill is built to ingest raw data, likely from various sources - perhaps databases, APIs, or flat files - and transform it into readily consumable numerical reports. My immediate mental checklist includes considerations for data validation, error handling, aggregation methods (SUM, AVERAGE, COUNT, etc.), and the ultimate output format – are we talking tables, charts, dashboards, or something else entirely? The nuances of this specific implementation will determine the optimal strategy for integration. I'm keen to see if this skill leverages any pre-built reporting libraries or if it takes a more custom approach to data manipulation. Let's see what we've got.

    Chamadas pedidas

    • load_skill (meta-ferramenta)

      {
        "name": "reporting-numbers"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
13. 12Habilidade
    reporting-numbersTexto da habilidade
14. 13Turno do modelo
    call_4bd5f574gemini-3.8-flashFim: STOP17.616 ms24435 de entrada · 502 de saída · 1438 de raciocínio · 21694 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Analysis of the Causal Effect of Property Rights on Income** Okay, let's break this down. The core question is: do institutions that protect property lead to higher income? Specifically, we're looking at former European colonies and estimating the causal impact of average protection against expropriation risk between 1985 and 1995 on log GDP per capita in 1995, using a variation in institutions that's independent of income itself. The question's in English, so the answer will be too. Right, the approach here is instrumental variables (IV) with a two-stage least squares (2SLS) framework. We're using the average protection against expropriation risk (`avexpr`) as our endogenous variable representing institutional quality, and the log GDP per capita in 1995 (`logpgp95`) as the outcome. The instrument is historical European settler mortality in log terms (`logem4`), following the logic established by Acemoglu, Johnson, and Robinson (2001). The dataset consists of 64 former European colonies (`baseco == 1`). Let's review the results, which seem pretty robust. First, that first stage is strong; the F-statistic is 16.32, which is well above the conventional threshold of 10. The first-stage coefficient of log settler mortality is -0.607 (with a standard error of 0.150). So, mortality strongly and negatively predicts institutional quality. Now, the 2SLS results are what we're after. The estimated coefficient on `avexpr` is 0.944 (with a standard error of 0.179). The 95% confidence interval is [0.587, 1.302]. The core.scale_effect converts this to a 157.10% increase in GDP per capita (95% CI: [79.79%, 267.64%]). For comparison, the OLS estimate is 0.522 (standard error of 0.050) with a 95% confidence interval [0.422, 0.622]. This 2SLS estimate is notably larger, which they attribute to attenuation bias from measurement error in institutional quality and possible biases in OLS. Of course, we need to be mindful of the assumptions. The exclusion restriction demands that European settler mortality only affects contemporary income through its influence on institutions. Other potential channels, like current disease environments, geography, or early human capital investments, must be negligible. Also, let's keep in mind this is only for the 64 former colonies in the base sample. So, summing it up, a 1-point increase in the protection against expropriation index increases log GDP per capita by 0.944 log points, or a 157.10% increase in GDP per capita. That should be a good, concise answer.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Yes, institutions protecting property rights have a positive and statistically significant causal effect on income. \n\nUsing historical European settler mortality (`logem4`) as an instrumental variable for average protection against expropriation risk (`avexpr`) in a two-stage least squares (2SLS) regression among the 64 former colonies in the base sample (`baseco == 1`):\n\n- **First stage**: Settler mortality strongly and negatively predicts institutional quality (coefficient of -0.606778, SE = 0.150197). The first-stage Wald F-statistic is 16.32, which is above the conventional threshold of 10, indicating a strong instrument.\n- **2SLS causal estimate**: A 1-point increase in average protection against expropriation risk increases 1995 log GDP per capita by 0.944 log points (SE = 0.179; 95% CI: [0.587, 1.302]).\n- **Economic magnitude**: Transforming this effect from log points, a 1-point improvement in expropriation protection increases GDP per capita by approximately 157.10% (95% CI: [79.79%, 267.64%]).\n- **Comparison with OLS**: The corresponding OLS estimate is 0.522 (SE = 0.050; 95% CI: [0.422, 0.622]). The 2SLS estimate is larger than OLS, which is consistent with attenuation bias from measurement error in institutional quality dampening the OLS coefficient.",
        "caveats": [
          "The exclusion restriction relies on the assumption that historical European settler mortality affects modern GDP per capita solely through its persistent effect on institutions, and not directly via modern health/disease environments or geography.",
          "The causal estimate applies specifically to the 64 former colonies in the base sample for which historical mortality and institutional data were available."
        ],
        "figure_refs": [
          "call_0f97b69d:coef.avexpr",
          "call_ac13a82c:effect",
          "call_233c60dd:coef.avexpr"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
15. 14Guarda
    resposta aceita

    Números encontrados

    64 · 1 · -0.606778 · 0.150197 · 16.32 · 10 · 1 · 1995 · 0.944 · 0.179 · 95% · 0.587 · 1.302 · 1 · 157.10% · 95% · 79.79% · 267.64% · 0.522 · 0.050 · 95% · 0.422 · 0.622 · 64

    Sem lastro

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    Não citados

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    1 nova(s) tentativa(s) restante(s)
16. 15Resposta

    Yes, institutions protecting property rights have a positive and statistically significant causal effect on income. Using historical European settler mortality (`logem4`) as an instrumental variable for average protection against expropriation risk (`avexpr`) in a two-stage least squares (2SLS) regression among the 64 former colonies in the base sample (`baseco == 1`): - **First stage**: Settler mortality strongly and negatively predicts institutional quality (coefficient of -0.606778, SE = 0.150197). The first-stage Wald F-statistic is 16.32, which is above the conventional threshold of 10, indicating a strong instrument. - **2SLS causal estimate**: A 1-point increase in average protection against expropriation risk increases 1995 log GDP per capita by 0.944 log points (SE = 0.179; 95% CI: [0.587, 1.302]). - **Economic magnitude**: Transforming this effect from log points, a 1-point improvement in expropriation protection increases GDP per capita by approximately 157.10% (95% CI: [79.79%, 267.64%]). - **Comparison with OLS**: The corresponding OLS estimate is 0.522 (SE = 0.050; 95% CI: [0.422, 0.622]). The 2SLS estimate is larger than OLS, which is consistent with attenuation bias from measurement error in institutional quality dampening the OLS coefficient. Ressalvas: The exclusion restriction relies on the assumption that historical European settler mortality affects modern GDP per capita solely through its persistent effect on institutions, and not directly via modern health/disease environments or geography. The causal estimate applies specifically to the 64 former colonies in the base sample for which historical mortality and institutional data were available.

    • The exclusion restriction relies on the assumption that historical European settler mortality affects modern GDP per capita solely through its persistent effect on institutions, and not directly via modern health/disease environments or geography.
    • The causal estimate applies specifically to the 64 former colonies in the base sample for which historical mortality and institutional data were available.

    Figuras citadas: call_0f97b69d:coef.avexpr, call_ac13a82c:effect, call_233c60dd:coef.avexpr · Parada: answered

    Figura da resposta: call_0f97b69d:coef.avexpr = 0,9442790.9442793851547994 (se 0,1789140.17891351798924446)
17. 16Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
18. 17Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_0f97b69d:coef.avexpr (infer.iv_2sls)
19. 18Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.iv_2sls
20. 19Pontuação
    #3argumento igualpassouobrigatóriainfer.iv_2sls.y = 'logpgp95'
21. 20Pontuação
    #4argumento igualpassouobrigatóriainfer.iv_2sls.endog[0] = 'avexpr'
22. 21Pontuação
    #5argumento igualpassouobrigatóriainfer.iv_2sls.instruments = ['logem4']
23. 22Pontuação
    #6argumento igualpassouregistradainfer.iv_2sls.y = 'logpgp95'
24. 23Pontuação
    #7argumento igualpassouregistradainfer.iv_2sls.endog = ['avexpr']
25. 24Pontuação
    #8argumento igualpassouregistradainfer.iv_2sls.instruments = ['logem4']
26. 25Pontuação
    #9argumento igualpassouregistradainfer.iv_2sls.exog = []
27. 26Pontuação
    #10diagnósticopassouregistrada64 vs 64
28. 27Pontuação
    #11figura da resposta próximapassouregistrada0.9442793852 vs 0.9442793851548288
29. 28Pontuação
    #12figura da resposta próximafalhouregistrada0.178913518 vs 0.15652545732958245
30. 29Pontuação
    #13figura da resposta próximapassouregistrada0.9442793852 vs 0.94
31. 30Pontuação
    #14figura da resposta próximafalhouregistrada0.178913518 vs 0.16

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