Tarefa paper_repro.ajr_2001.tab2_c2.design

Veredito

Pergunta feita ao analista

Across the former European colonies in the paper's base sample, how strongly is income associated with institutions? Report the association between log GDP per capita in 1995 and average protection against expropriation risk (1985-1995), per point of the protection index.

Valores esperados e de onde vêm

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

   Across the former European colonies in the paper's base sample, how strongly is income associated with institutions? Report the association between log GDP per capita in 1995 and average protection against expropriation risk (1985-1995), per point of the protection index.

   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_543fbf7dgemini-3.8-flashFim: STOP11.245 ms14347 de entrada · 39 de saída · 345 de raciocínio · 0 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process** Okay, so the user wants to understand the relationship between income and institutional quality, specifically focusing on former European colonies within their defined "base sample." They're asking for the strength of this association, quantified as the change in log GDP per capita for each one-point increase in the expropriation risk protection index. My immediate thought is that this sounds like a regression problem. I need to model the dependent variable (log GDP per capita) as a function of the independent variable (protection against expropriation risk), but *only* for the countries that meet the criteria of being former European colonies in the specified base sample. First, I need to access the relevant dataset. The user mentioned `maketable2`, so that's where I'll start. I'll need to inspect its columns to confirm I have the right variables. I see `logpgp95` for log GDP per capita and `avexpr` for average protection against expropriation risk, which are exactly what I need. Crucially, there's a column called `baseco`. The user's prompt states "1 for the former colonies in the paper's base sample and missing for every other country." This is perfect – it's the exact filter I need to apply. So, the core task is to run an Ordinary Least Squares (OLS) regression. The model will be `logpgp95 ~ avexpr`. The key is to apply the filter `baseco == 1` to the data before running the regression. Now, I need to consider the available tools. I recall there's an `infer__ols` function. I'll check its capabilities. Aha! It has a `subset` argument. This is exactly what I need. I can directly specify `subset="baseco == 1"` within the `infer__ols` call. This means I don't need to manually filter the DataFrame beforehand, which simplifies the process. Before diving into the regression, it's always good practice to confirm the data and the specific values within the `baseco` column. I'll use `data__describe` on `maketable2`. This will show me the column names again, and importantly, it will let me see the unique values in `baseco` and their counts. This confirms that `1` is indeed the identifier for the relevant subset and that there are other (likely missing or zero) values for countries outside this base sample. Once `infer__ols` is called with `logpgp95` as the dependent variable, `avexpr` as the independent variable, and `subset="baseco == 1"`, I'll be looking for the coefficient associated with `avexpr`. This coefficient will directly represent the association requested: the change in log GDP per capita for each one-point increase in the protection against expropriation risk index, within the specified subset of countries.

   Chamadas pedidas

   • data.describe

     {
       "columns": [
         "logpgp95",
         "avexpr",
         "baseco"
       ],
       "dataset": "maketable2"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
3. 2Ferramenta
   data.describev0.1.0call_3ad4acd5decidido pelo modeloreexecutável

   Argumentos

   {
     "columns": [
       "logpgp95",
       "avexpr",
       "baseco"
     ],
     "dataset": "maketable2"
   }

   Figuras call_3ad4acd5

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

   Tabelas (1)

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

   Digests dos dados

   maketable2 f2d6f55b0be66f82f98d12727d9a9b67b5c96add4aa011b150d70fa4b81b43c8

   sha256 do resultado

   d03175be97296ff680da40e179056ded22aa49a95f47df80f57c22011c2e4535

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_25b8b6fcgemini-3.8-flashFim: STOP5.575 ms14878 de entrada · 18 de saída · 238 de raciocínio · 12519 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Replicating AJR (2001) Table 2** Alright, so I'm trying to replicate Table 2 from Acemoglu, Johnson, and Robinson (2001). The first thing that caught my eye was the `baseco` dataset. I see it has 64 non-missing rows, which immediately clicked with me – that's exactly the number of countries in the *base sample* as reported in AJR's Table 2. This is a good sign that I'm on the right track. Now, to be more precise, I need to examine AJR's Table 2 itself. It presents OLS regressions of log GDP per capita in 1995, using average protection against expropriation risk from 1985-1995 as the predictor. I recall that Panel A of this table has different specifications. Column 1, if I remember correctly, uses the entire world sample (around 111 countries), while column 2 is precisely that *base sample* of 64 countries. This is where my current focus lies. Before proceeding, a crucial detail I need to confirm is the type of standard errors used in Table 2. The paper typically employs either classical OLS standard errors or robust standard errors. I need to be sure about this for my replication. My immediate thought is to check the default behavior of `infer.ols` in my current environment. I seem to recall it uses `se='HC1'` by default, which suggests robust standard errors, but I must verify this. To confirm the standard error options, I'll either load the `reporting-numbers` library or delve into the documentation for `infer.ols` to see what options are available and what the default is. This step is critical to ensure my replication accurately reflects the methodology in the original paper.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "reporting-numbers"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
5. 4Habilidade
   reporting-numbersTexto da habilidade
6. 5Turno do modelo
   call_835655aegemini-3.8-flashFim: STOP9.160 ms15570 de entrada · 52 de saída · 359 de raciocínio · 12632 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Analyzing the Association Between Institutions and Income in Former Colonies** Right, so my first thought here is to meticulously dissect the user's query to ensure I'm addressing the core question precisely. They've asked about the strength of association between income and institutions specifically within former European colonies, and they want this association reported as the impact on log GDP per capita for each unit increase in the protection against expropriation index. This immediately signals a bivariate regression analysis. I need to regress log GDP per capita in 1995 (which I'll represent as `logpgp95`) against the average protection against expropriation risk from 1985-1995 (which I'll denote as `avexpr`). Crucially, this analysis must be confined to the "base sample," which the user has clarified corresponds to `baseco == 1`. Now, the critical next step is to determine the source of the data and the precise statistical methodology employed. The prompt directly references "AJR (2001)," implying the seminal Acemoglu, Johnson, and Robinson paper. My instinct is to consult Table 2 of that paper or, if I have the capability and the data loaded, to run an `infer__ols` command to replicate the findings. When considering how to run `infer__ols`, I'm mindful of potential nuances in the standard error estimation. The user's question is quite specific, and the way AJR presents their results in Table 2 will be key. I need to confirm whether AJR reports standard errors in parentheses. Furthermore, I need to be prepared to run the regression with different standard error configurations. The prompt suggests checking both 'classical' and 'HC1' standard errors. Since 'HC1' is often the default in many statistical packages, and it's robust to heteroskedasticity, I'll likely start with that. However, to be thorough, I should also consider running it with 'classical' standard errors to see if there's a significant difference and to align with potentially older reporting conventions if necessary. So, in essence, my mental checklist is: 1. **Identify the variables:** `logpgp95` and `avexpr`. 2. **Define the sample:** `baseco == 1` (former European colonies). 3. **Specify the model:** Bivariate OLS regression. 4. **Determine the output format:** The regression coefficient for `avexpr` and its associated uncertainty. 5. **Verify the methodology:** Consult AJR (2001) Table 2 or run `infer__ols`. 6. **Address standard errors:** Decide between 'classical' and 'HC1' (and potentially run both) based on how AJR presents their results and the robustness of the estimates. The core of my thought process is about rigorous adherence to the question's constraints and a systematic approach to statistical analysis, ensuring accuracy and replicability.

   Chamadas pedidas

   • infer.ols

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

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
7. 6Ferramenta
   infer.olsv0.1.0call_96ce6ea3decidido pelo modeloreexecutável

   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_96ce6ea3

   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

   41ef9a3ba5efba39344709b4ddb9fd05e74b4db8361c2ac652469db0f6407c79

   Resultado mostrado ao modelo
8. 7Turno do modelo
   call_37b2fc87gemini-3.8-flashFim: STOP22.437 ms16247 de entrada · 53 de saída · 19 de raciocínio · 12658 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process Regarding Robust Standard Errors** Alright, so to really solidify my understanding and ensure the results are as reliable as possible, I've decided to implement the `se='HC1'` option. This is going to allow me to compute robust standard errors. For someone with my level of expertise in this area, understanding and verifying the robustness of our estimates is paramount. It's not just about getting a point estimate; it's about knowing how much confidence I can place in that estimate, especially in the face of potential heteroskedasticity in the data. By running with `HC1`, I'm essentially stress-testing the model's assumptions and ensuring that the standard errors are not artificially deflated or inflated due to non-constant variance. This step is crucial for accurate inference, hypothesis testing, and ultimately, for drawing sound conclusions from the analysis. I'm looking for the most dependable and transparent outcome, and robust standard errors are a key component of that.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "maketable2",
       "se": "HC1",
       "subset": "baseco == 1",
       "x": [
         "avexpr"
       ],
       "y": "logpgp95"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
9. 8Ferramenta
   infer.olsv0.1.0call_7b39ac16decidido pelo modeloreexecutável

   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_7b39ac16

   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

   2292804a3aaff013b08226cc5bbb8165be4955827c3d63534e35b6645b569fdf

   Resultado mostrado ao modelo
10. 9Turno do modelo
    call_9c9e34dagemini-3.8-flashFim: STOP31.845 ms16597 de entrada · 43 de saída · 299 de raciocínio · 12627 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the Acemoglu, Johnson, and Robinson (2001) Data** Alright, diving into the details of Table 2, specifically column (2) from Acemoglu, Johnson, and Robinson (2001). I'm looking at the dependent variable, which is the natural logarithm of GDP per capita in 1995. The key independent variable of interest here is the average protection against expropriation risk. I see the coefficient for average protection against expropriation risk is 0.52, with a standard error of 0.06. The R-squared for this model is 0.54, and it's based on 64 observations. Now, I'm double-checking the standard error. With classical standard errors, it's precisely 0.06, or more specifically, 0.061185. My next immediate thought is to scrutinize the question being asked. Does it require me to convert the coefficient into a percentage effect, perhaps using a `scale_effect` function? Or is it specifically asking for the association in terms of log-points per unit of the protection index, phrased something like: "Across the former European colonies in the paper's base sample, how strongly is income associated with institutions? Report the association between log GDP per capita in 1995 and average protection against expropriation risk (1985-1995), per point of the protection index." This phrasing implies a direct interpretation of the coefficient as is. However, to be thorough, I should consider the possibility that a percent effect conversion might be expected or useful. So, I'm contemplating whether I should run `core.scale_effect` on the coefficient of average expropriation risk (`call_96ce6ea3:coef.avexpr`). If the question doesn't explicitly demand it, I might still want to report both the log-point coefficient and the calculated percentage effect to provide a more comprehensive answer. I'm also noting that the standard error changes slightly with robust standard errors (HC1), coming in at 0.050 (or 0.0499). This is a good practice to check, but for the direct interpretation of the coefficient, the classical standard error is usually the starting point unless otherwise specified.

    Chamadas pedidas

    • core.scale_effect

      {
        "coefficient": "call_96ce6ea3:coef.avexpr",
        "transform": "log_points_to_percent"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
11. 10Ferramenta
    core.scale_effectv0.1.0call_80172071decidido pelo modeloreexecutável

    Argumentos

    {
      "baseline": null,
      "coefficient": "call_96ce6ea3:coef.avexpr",
      "sd": null,
      "transform": "log_points_to_percent"
    }

    Figuras call_80172071

    Chave	Valor	Valor bruto	Erro-padrão	IC
    effect Effect as a percent change (from log points): Coefficient on avexpr in the regression of logpgp95	68,5575 %	68.55754683214667	—	49,1526 – 90,4871

    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

    522c172b1d3d6c3bc6ea9978ba7fb2491c12699649e716ddcb3f0eb5c7f8ff04

    Resultado mostrado ao modelo
12. 11Turno do modelo
    call_cee68a69gemini-3.8-flashFim: STOP24.777 ms17062 de entrada · 316 de saída · 2250 de raciocínio · 12665 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Analyzing the Association Between Income and Institutions in Former European Colonies** My primary goal here is to accurately and concisely answer the user's question regarding the relationship between income and institutional quality in former European colonies, adhering strictly to the provided rules. First, I need to identify the specific question being asked. The user wants to know "Across the former European colonies in the paper's base sample, how strongly is income associated with institutions? Report the association between log GDP per capita in 1995 and average protection against expropriation risk (1985-1995), per point of the protection index." Now, I recall Rule 7, which dictates the format of the final answer: "Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers." A crucial detail in Rule 7 is "Write the answer in the language of the question." The question is clearly in English. However, there's a parenthetical note "(Portuguese: decimal comma)". This seems to be a default instruction for when the question is typically in Portuguese. Since the question is English, I will write the answer in English. I need to ensure I use a standard English decimal point. Next, I'll examine the available tool results to find the relevant data. I have several options: * `core.scale_effect`: This gives me `call_80172071:effect` = 68.56% (IC 95%: [49.15%, 90.49%]). This is a percentage effect, which is useful. * `call_96ce6ea3` (classical SE): This provides `coef.avexpr`: 0.522107 (se: 0.061185, 95% CI: [0.3998, 0.644414]). This gives me the coefficient in log points, its standard error, and a confidence interval. It also indicates `r2`: 0.540114955312099 and `n_obs`: 64. * `call_7b39ac16` (HC1 robust SE): This gives `coef.avexpr`: 0.522107 (se: 0.0499225, 95% CI: [0.422313, 0.621901]). While it's robust SE, the classical SE from `call_96ce6ea3` is often the default for reporting unless robustness is specifically requested or implied. The question doesn't specify which SE to use, so the classical one, which also corresponds to the typical reporting in academic papers like AJR, seems appropriate. I need to confirm that the data from `call_96ce6ea3` directly addresses the question. The question asks about "former European colonies in the paper's base sample," which aligns with the `baseco == 1` sample mentioned in the context of this call (implied by AJR Table 2 column 2). The dependent variable is "log GDP per capita in 1995" and the independent variable is "average protection against expropriation risk (1985-1995)." This matches precisely the bivariate OLS regression described for Column (2) of Table 2 in AJR (2001). So, the key figures are: * Coefficient (`coef.avexpr`): 0.522107 * Standard Error (`se` for `coef.avexpr`): 0.061185 * 95% Confidence Interval (`ci` for `coef.avexpr`): [0.3998, 0.644414] * Percentage Effect (`effect` from `core.scale_effect`): 68.5575% * 95% Confidence Interval for Percentage Effect (`ci` for `effect`): [49.1526%, 90.4871%] * Sample Size (`n_obs`): 64 * R-squared (`r2`): 0.540114955312099 Now, I need to consider the "what could undermine it" part, which translates to caveats. Rule 7 requires me to "put diagnostics that limit the conclusion in caveats." Based on my understanding of econometric analysis, two primary caveats apply here: 1. **Correlation vs. Causation:** The OLS estimates represent an association, not a definitive causal effect. There's potential for omitted variable bias (e.g., geographic factors, climate, health) and reverse causality (richer countries can afford better institutions). 2. **Sample Specificity:** The sample is limited to the 64 former colonies included in the authors' "base sample." This means the findings are specific to this subset and may not generalize to all countries or all types of colonies. Finally, I will assemble the answer following Rule 7 precisely. I will state the estimate with its uncertainty, identify the design (which regression this comes from), and list the caveats. I must ensure every number I quote comes directly from the tool outputs and is presented as they stated it. I also need to list the figure references using the `<call_id>:<key>` format. * For the estimate, I can report it both in log points and as a percentage effect, as both are provided by the tools and relevant to the question. * The design is the bivariate OLS regression of log GDP per capita on protection against expropriation in the base sample of former European colonies. This is explicitly linked to `call_96ce6ea3`. * The caveats are the ones I've identified regarding correlation and sample limitations. * Figure references will include `call_96ce6ea3:coef.avexpr`, `call_96ce6ea3:n_obs`, and `call_80172071:effect`. I've reviewed all the requirements, cross-referenced the tool outputs with the question, and considered the formatting and content rules. I am now ready to formulate the final answer.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Across the 64 former European colonies in the base sample, an ordinary least squares (OLS) regression indicates a strong positive association between institutions and income: each 1-point increase in the average protection against expropriation risk index (1985–1995) is associated with an increase of 0.522107 log points in 1995 GDP per capita (standard error of 0.061185; 95% CI: [0.3998, 0.644414]; R² of 0.540114955312099).\n\nIn percentage terms, this corresponds to an estimated 68.5575% increase in GDP per capita per index point (95% CI: [49.1526%, 90.4871%]).",
        "caveats": [
          "This OLS specification estimates a cross-sectional association, not a causal effect, and may be subject to omitted variable bias (such as geography or disease environment) and reverse causality.",
          "The analysis is restricted to the 64 countries in the authors' base sample of former colonies with non-missing data."
        ],
        "figure_refs": [
          "call_96ce6ea3:coef.avexpr",
          "call_80172071:effect"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
13. 12Guarda
    resposta aceita

    Números encontrados

    64 · 1 · 1985 · 1995 · 0.522107 · 1995 · 0.061185 · 95% · 0.3998 · 0.644414 · 0.540114955312099 · 68.5575% · 95% · 49.1526% · 90.4871% · 64

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
14. 13Resposta

    Across the 64 former European colonies in the base sample, an ordinary least squares (OLS) regression indicates a strong positive association between institutions and income: each 1-point increase in the average protection against expropriation risk index (1985–1995) is associated with an increase of 0.522107 log points in 1995 GDP per capita (standard error of 0.061185; 95% CI: [0.3998, 0.644414]; R² of 0.540114955312099). In percentage terms, this corresponds to an estimated 68.5575% increase in GDP per capita per index point (95% CI: [49.1526%, 90.4871%]). Ressalvas: This OLS specification estimates a cross-sectional association, not a causal effect, and may be subject to omitted variable bias (such as geography or disease environment) and reverse causality. The analysis is restricted to the 64 countries in the authors' base sample of former colonies with non-missing data.

    • This OLS specification estimates a cross-sectional association, not a causal effect, and may be subject to omitted variable bias (such as geography or disease environment) and reverse causality.
    • The analysis is restricted to the 64 countries in the authors' base sample of former colonies with non-missing data.

    Figuras citadas: call_96ce6ea3:coef.avexpr, call_80172071:effect · Parada: answered

    Figura da resposta: call_96ce6ea3:coef.avexpr = 0,5221070.5221070297245911 (se 0,0611850.061185038536558864)
15. 14Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
16. 15Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_96ce6ea3:coef.avexpr (infer.ols)
17. 16Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.ols
18. 17Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'logpgp95'
19. 18Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x[0] = 'avexpr'
20. 19Pontuação
    #5argumento igualpassouregistradainfer.ols.y = 'logpgp95'
21. 20Pontuação
    #6argumento igualpassouregistradainfer.ols.x = ['avexpr']
22. 21Pontuação
    #7argumento igualpassouregistradainfer.ols.controls = []
23. 22Pontuação
    #8argumento igualpassouregistradainfer.ols.fe = []
24. 23Pontuação
    #9diagnósticopassouregistrada64 vs 64
25. 24Pontuação
    #10figura da resposta próximapassouregistrada0.5221070297 vs 0.5221070297245913
26. 25Pontuação
    #11figura da resposta próximafalhouregistrada0.06118503854 vs 0.04992252631098126
27. 26Pontuação
    #12figura da resposta próximapassouregistrada0.5221070297 vs 0.52
28. 27Pontuação
    #13figura da resposta próximapassouregistrada0.06118503854 vs 0.06

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• época 1, tentativa 1passoupontuada(exibindo)

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