Tarefa paper_repro.mrw_1992.tab1_nonoil_lngd.design

Veredito

Pergunta feita ao analista

In the Solow growth model, how does income per working-age person in 1985 vary across the non-oil-producing countries with the growth rate of the working-age population plus technological progress and depreciation (assumed to total 0.05), holding the investment share fixed? Report the elasticity of income with respect to that sum.

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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

   In the Solow growth model, how does income per working-age person in 1985 vary across the non-oil-producing countries with the growth rate of the working-age population plus technological progress and depreciation (assumed to total 0.05), holding the investment share fixed? Report the elasticity of income with respect to that sum.

   growthdj (agent)

   The data of Mankiw, Romer and Weil (1992), 'A Contribution to the Empirics of Economic Growth', as transcribed by Durlauf and Johnson (R package AER, GrowthDJ): one row per country (121 rows). Columns: rownames, a row number; oil, 'yes' for the oil producers MRW exclude from the non-oil sample, else 'no'; inter, 'yes' for the 'intermediate' sample (no Summers-Heston grade of D, 1960 population of at least one million); oecd, 'yes' for the OECD sample; gdp60 and gdp85, real GDP per working-age person in 1960 and 1985 (dollars); gdpgrowth, average annual growth of GDP per working-age person 1960-1985 (percent); popgrowth, average annual growth of the working-age population 1960-1985 (percent); invest, average share of real investment in real GDP 1960-1985 (percent); school, percent of the working-age population in secondary school; literacy60, adult literacy rate in 1960 (percent). Columns built from the paper's definitions (g + δ = 0.05): ly, log of gdp85; s_share, invest divided by 100; ls, log of s_share (ln(I/GDP)); n_rate, popgrowth divided by 100; g_plus_delta, the constant 0.05; ngd, n_rate + g_plus_delta; lngd, log of ngd (ln(n + g + δ)). Some countries miss gdp85 or popgrowth.

   file 3d06303f1da9959175e990eb068bf242fac0e9bc1f9daad1f5fffe96d100e796 · frame —

   1. derive {"args":{"column":"gdp85"},"name":"ly","op":"log"}
   2. derive {"args":{"column":"invest","factor":0.01},"name":"s_share","op":"scale"}
   3. derive {"args":{"column":"s_share"},"name":"ls","op":"log"}
   4. derive {"args":{"column":"popgrowth","factor":0.01},"name":"n_rate","op":"scale"}
   5. derive {"args":{"value":0.05},"name":"g_plus_delta","op":"constant"}
   6. derive {"args":{"columns":["n_rate","g_plus_delta"]},"name":"ngd","op":"sum"}
   7. derive {"args":{"column":"ngd"},"name":"lngd","op":"log"}
2. 1Transformação
   growthdj · derive {"args":{"column":"gdp85"},"name":"ly","op":"log"}

   121 → 121 linhas

   Frame antes → depois: 6f822eca9c87 → 256189baba87
3. 2Transformação
   growthdj · derive {"args":{"column":"invest","factor":0.01},"name":"s_share","op":"scale"}

   121 → 121 linhas

   Frame antes → depois: 256189baba87 → ddc60cb0ca4e
4. 3Transformação
   growthdj · derive {"args":{"column":"s_share"},"name":"ls","op":"log"}

   121 → 121 linhas

   Frame antes → depois: ddc60cb0ca4e → 832bd9c4b876
5. 4Transformação
   growthdj · derive {"args":{"column":"popgrowth","factor":0.01},"name":"n_rate","op":"scale"}

   121 → 121 linhas

   Frame antes → depois: 832bd9c4b876 → ff997d0e044f
6. 5Transformação
   growthdj · derive {"args":{"value":0.05},"name":"g_plus_delta","op":"constant"}

   121 → 121 linhas

   Frame antes → depois: ff997d0e044f → b9e3acd5adf8
7. 6Transformação
   growthdj · derive {"args":{"columns":["n_rate","g_plus_delta"]},"name":"ngd","op":"sum"}

   121 → 121 linhas

   Frame antes → depois: b9e3acd5adf8 → 837704298f36
8. 7Transformação
   growthdj · derive {"args":{"column":"ngd"},"name":"lngd","op":"log"}

   121 → 121 linhas

   Frame antes → depois: 837704298f36 → e3f509733efe
9. 8Turno do modelo
   call_041da9aegemini-3.8-flashFim: STOP11.133 ms14636 de entrada · 46 de saída · 628 de raciocínio · 0 em cache

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on the Solow Model Inquiry** Here I am, presented with a specific question about the Solow growth model and its empirical application, as detailed in Mankiw, Romer, and Weil (1992). The user wants to understand how income per working-age person in 1985 varies across *non-oil-producing countries* based on the sum of population growth rate, technological progress, and depreciation. Crucially, this variation needs to be examined while *holding the investment share fixed*, and I'm asked to report the *elasticity* of income with respect to this sum. My immediate thought is to anchor this in the econometric specification of the Solow model, which is typically represented as: $\ln(Y/L) = \beta_0 + \beta_1 \ln(I/GDP) + \beta_2 \ln(n + g + \delta) + \epsilon$ Where: * $\ln(Y/L)$ is the log of income per working-age person. * $\ln(I/GDP)$ is the log of the investment share. * $\ln(n + g + \delta)$ is the log of the sum of population growth rate ($n$), technological progress ($g$), and depreciation ($\delta$). * $\beta_0$, $\beta_1$, and $\beta_2$ are coefficients to be estimated, and $\epsilon$ is the error term. Now, let's map this to the dataset at hand, `growthdj`. 1. **Identifying the Outcome Variable:** The question asks about "income per working-age person in 1985." In the Solow model context, this corresponds to `ly` in the dataset, which represents the logarithm of GDP per working-age person in 1985. This is my dependent variable. 2. **Filtering the Data:** The question specifically directs me to consider "non-oil-producing countries." The `growthdj` dataset has an `oil` column, where 'no' indicates non-oil producers. So, my first step in data preparation is to filter the dataset to include only rows where `oil == 'no'`. 3. **Identifying the Regressors of Interest:** * The core of the question is about how income varies "with the growth rate of the working-age population plus technological progress and depreciation (assumed to total 0.05)." This directly maps to the term $(n + g + \delta)$. In the dataset, this is represented by `lngd`, which is the log of $(n + g + \delta)$. * The crucial constraint is "holding the investment share fixed." The investment share in the dataset is `ls` (log of the investment share, $\ln(I/GDP)$). 4. **Interpreting "Elasticity":** This is a key term. The question asks for "the elasticity of income with respect to that sum." Given the log-log specification of the Solow model, the coefficient $\beta_2$ on $\ln(n + g + \delta)$ directly represents the elasticity of income ($\ln(Y/L)$) with respect to $(n + g + \delta)$. This is because: $\text{Elasticity} = \frac{\partial \ln(Y/L)}{\partial \ln(n + g + \delta)} = \beta_2$ This means if $\ln(n + g + \delta)$ increases by 1%, $\ln(Y/L)$ is expected to change by $\beta_2 \%$. 5. **Constructing the Regression:** I need to run an Ordinary Least Squares (OLS) regression. The question implies that $\ln(n + g + \delta)$ (i.e., `lngd`) is the primary variable whose effect on $\ln(Y/L)$ (i.e., `ly`) I want to measure, while controlling for or holding fixed the investment share ($\ln(I/GDP)$, i.e., `ls`). This leads to two potential, but equivalent, ways of thinking about the regression setup: * **Model 1 (Standard OLS):** Regress `ly` on both `ls` and `lngd`. The coefficient on `lngd` will be $\beta_2$, which is exactly the elasticity I need. In this case, `ls` is a control variable, effectively holding its effect constant. * **Model 2 (Conditional Expectation):** Think about the conditional expectation: $E[\ln(Y/L) | \ln(I/GDP), \ln(n + g + \delta)]$. The coefficient on $\ln(n + g + \delta)$ in this regression directly answers the question. The prompt mentions `infer__ols`, which is a function that can handle these OLS estimations. I need to ensure that when I call it, I specify `ly` as the outcome, `lngd` as a regressor, and `ls` as another regressor (or control). The question doesn't explicitly state *which* regression function I should use, but the context of OLS and coefficients strongly points towards an OLS estimation. 6. **Final Check of the Question's Nuances:** * "income per working-age person in 1985": `ly`. * "across the non-oil-producing countries": filter `oil == 'no'`. * "with the growth rate of the working-age population plus technological progress and depreciation (assumed to total 0.05)": `lngd`. * "holding the investment share fixed": `ls` is included in the regression as a covariate. * "Report the elasticity of income with respect to that sum": I need the coefficient on `lngd`. Therefore, my plan is to: 1. Load the `growthdj` dataset. 2. Filter it to keep only rows where `oil == 'no'`. 3. Perform an OLS regression of `ly` on `ls` and `lngd`. 4. Extract and report the coefficient associated with `lngd`. This coefficient will be the desired elasticity. The question mentions the sum *is assumed to total 0.05*, which is slightly ambiguous. However, in the context of the regression specification using `ln(n + g + delta)`, it implies that `lngd` is the log of the sum. The actual *value* of that sum (0.05) isn't directly used in estimating the elasticity itself, but rather the *variation* of the log of that sum across countries. If the question intended for me to *plug in* 0.05 to *calculate* something, it would be phrased differently (e.g., "predict income when this sum is 0.05"). As it stands, it's about the empirical relationship represented by the coefficient. The "assumed to total 0.05" might be a reference to how the data was constructed or a typical value used in the literature, but my task is to estimate the elasticity from the *observed* data. My focus is on the coefficient estimation.

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "ls"
       ],
       "dataset": "growthdj",
       "subset": "oil == 'no'",
       "x": [
         "lngd"
       ],
       "y": "ly"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
10. 9Ferramenta
    infer.olsv0.1.0call_2e1883a7decidido pelo modeloreexecutável

    Argumentos

    {
      "cluster": null,
      "controls": [
        "ls"
      ],
      "dataset": "growthdj",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": "oil == 'no'",
      "weights": null,
      "x": [
        "lngd"
      ],
      "y": "ly"
    }

    Figuras call_2e1883a7

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.lngd Coefficient on lngd in the regression of ly	-1,98977	-1.9897744846061867	0,5452410.5452412830469935	-3,07222 – -0,907334

    Diagnósticos

    • n_obs = 9898
    • k = 33
    • r2 = 0,6008650.6008648011032094
    • df_resid = 9595
    • se_type = "HC1"
    • n_clusters = —

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 8.577647478110181,
          "ci_low": 2.2821187134531207,
          "coef": 5.429883095781651,
          "p": 0.0009107501109057599,
          "se": 1.5855750113060267,
          "t": 3.4245513817155175,
          "term": "Intercept"
        },
        {
          "ci_high": -0.9073336802845178,
          "ci_low": -3.072215288927856,
          "coef": -1.9897744846061867,
          "p": 0.0004296714962462008,
          "se": 0.5452412830469935,
          "t": -3.649346713966065,
          "term": "lngd"
        },
        {
          "ci_high": 1.6859924961246415,
          "ci_low": 1.1620360706141963,
          "coef": 1.424014283369419,
          "p": 3.409312626758573e-18,
          "se": 0.1319622618463002,
          "t": 10.791072109903698,
          "term": "ls"
        }
      ]
    }

    Digests dos dados

    growthdj e3f509733efe06358f9503c526bd45111b6a64feb75b292419aae56055b598f2

    sha256 do resultado

    d44c924de783d3383deff615112359828168e2452da23fd32f9cef3577a159eb

    Resultado mostrado ao modelo
11. 10Turno do modelo
    call_ec30233cgemini-3.8-flashFim: STOP6.425 ms15633 de entrada · 52 de saída · 241 de raciocínio · 0 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **Replicating MRW 1992 Table I and Confirming Standard Error Calculation** My current objective is to meticulously replicate the findings presented in Table I of the Mankiw, Romer, and Weil (1992) paper. Specifically, I want to ensure my standard error calculations align with their reported values, particularly for the coefficient on `ln(n + g + delta)`. To do this, I've decided to run `infer__ols` using classical standard errors, as a direct comparison to their published results. Looking at MRW 1992, Table I, for the non-oil sample (N=98), with `log GDP per working-age person in 1985` as the dependent variable, I see the following reported: * Constant: 5.43 (standard error 1.58) * `ln(I/GDP)`: 1.42 (standard error 0.14) * `ln(n + g + delta)`: -1.99 (standard error 0.56) * R-squared: 0.59 (or 0.60) I've already run a regression using Heteroskedasticity-Consistent (HC1) standard errors, which produced a standard error of 0.545241 for the coefficient -1.98977 on `ln(n + g + delta)`. This is quite close to the reported 0.56, but I want to be absolutely certain. Now, I need to perform the specific check with classical standard errors. I'm pausing to consider if the prompt specifies a particular type of standard error or if HC1 is the default assumption. My current `infer__ols` implementation uses HC1 by default. Therefore, to verify MRW's result of 0.56, I will proceed to execute `infer__ols` with the explicit `se="classical"` option. This will allow me to directly compare the classical standard error generated by my code with the value reported in their seminal paper.

    Chamadas pedidas

    • infer.ols

      {
        "controls": [
          "ls"
        ],
        "dataset": "growthdj",
        "se": "classical",
        "subset": "oil == 'no'",
        "x": [
          "lngd"
        ],
        "y": "ly"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
12. 11Ferramenta
    infer.olsv0.1.0call_72f58bb7decidido pelo modeloreexecutável

    Argumentos

    {
      "cluster": null,
      "controls": [
        "ls"
      ],
      "dataset": "growthdj",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "classical",
      "subset": "oil == 'no'",
      "weights": null,
      "x": [
        "lngd"
      ],
      "y": "ly"
    }

    Figuras call_72f58bb7

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.lngd Coefficient on lngd in the regression of ly	-1,98977	-1.9897744846061867	0,5633620.5633618948953983	-3,10819 – -0,87136

    Diagnósticos

    • n_obs = 9898
    • k = 33
    • r2 = 0,6008650.6008648011032094
    • df_resid = 9595
    • se_type = "classical"
    • n_clusters = —

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 8.574302070149807,
          "ci_low": 2.2854641214134954,
          "coef": 5.429883095781651,
          "p": 0.0008999523718049453,
          "se": 1.5838898803304133,
          "t": 3.428194827944055,
          "term": "Intercept"
        },
        {
          "ci_high": -0.8713597174283383,
          "ci_low": -3.108189251784035,
          "coef": -1.9897744846061867,
          "p": 0.0006385352057009347,
          "se": 0.5633618948953983,
          "t": -3.531964981365373,
          "term": "lngd"
        },
        {
          "ci_high": 1.708115249178197,
          "ci_low": 1.1399133175606408,
          "coef": 1.424014283369419,
          "p": 2.1046445253797267e-16,
          "se": 0.14310581649732002,
          "t": 9.950778509384255,
          "term": "ls"
        }
      ]
    }

    Digests dos dados

    growthdj e3f509733efe06358f9503c526bd45111b6a64feb75b292419aae56055b598f2

    sha256 do resultado

    af70ae6036b658b12df063b547ee73f4ba5d175ca0235b0b60799810b5d4e29b

    Resultado mostrado ao modelo
13. 12Turno do modelo
    call_fdb6a686gemini-3.8-flashFim: STOP9.330 ms16237 de entrada · 18 de saída · 714 de raciocínio · 12630 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on the Solow Growth Model Elasticity** I've just completed an analysis concerning the Solow growth model, specifically focusing on its predictions for income per working-age person. My primary goal was to determine how income in 1985 relates to the combined rate of working-age population growth, technological progress, and depreciation, assuming a fixed investment share. The question asks for the elasticity of income with respect to this sum. I ran an Ordinary Least Squares (OLS) regression using data from non-oil-producing countries, mirroring the setup from Mankiw, Romer, and Weil (1992). The dependent variable was the logarithm of GDP per working-age person in 1985 (`ly`), and the key independent variable of interest was the logarithm of the sum of population growth, technological progress, and depreciation (`lngd`), with the investment share (`ls`) also included as a control. The results are quite clear: I obtained a coefficient of -1.98977 for `lngd`. This value represents the elasticity I was looking for. In practical terms, this means that a 1% increase in the combined rate of $(n + g + \delta)$ is associated with a roughly 1.99% decrease in income per working-age person in 1985, provided the investment share remains constant. This aligns with the theoretical predictions of the Solow model, where higher population growth and depreciation rates, without a corresponding increase in the savings rate, would lead to lower steady-state income per capita. I need to be mindful of the limitations of this analysis, however. First, this is a cross-country regression, so the observed correlation doesn't necessarily imply causation. There's always the risk of omitted variable bias; unobserved factors like institutional quality or initial technological levels ($A(0)$) might be correlated with both the population growth rate and the investment share, influencing income per capita. Second, the analysis assumes a uniform rate of technological progress and depreciation ($g + \delta = 0.05$) across all 98 non-oil countries, which is a strong simplification. Finally, as with any cross-country data, there are inherent measurement errors in national accounts and demographic data that could affect the results. The question also specified the output format, including reporting the estimate with its uncertainty. I've noted the robust standard errors (HC1) are 0.545241, leading to a 95% confidence interval of [-3.07222, -0.907334]. I also noticed a detail about the language of the answer. The question was posed in English, and while it mentioned Portuguese decimal commas in the context of "the language of the question," the explicit instruction was to write the answer in the language of the question itself. Therefore, my summary and any subsequent reporting will be in English. I've also reviewed the `reporting-numbers` skill requirements to ensure I meet all formatting expectations.

    Chamadas pedidas

    • load_skill (meta-ferramenta)

      {
        "name": "reporting-numbers"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
14. 13Habilidade
    reporting-numbersTexto da habilidade
15. 14Turno do modelo
    call_025e0728gemini-3.8-flashFim: STOP11.402 ms17405 de entrada · 314 de saída · 717 de raciocínio · 12810 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process: Deconstructing the Solow Model and Elasticity** Okay, this request is quite clear. I need to analyze the results of a specific call, `call_2e1883a7`, in the context of the Solow growth model as described in the prompt. The core task is to report the elasticity of income per working-age person with respect to the sum of population growth, technological progress, and depreciation. First, I'll meticulously examine the provided numbers from `call_2e1883a7`: * **Estimate:** -1.98977. This is the key coefficient I'll be reporting as the elasticity. * **Standard Error (se):** 0.545241. This is important for understanding the precision of the estimate. * **Confidence Interval (ci):** [-3.07222, -0.907334]. This gives me the range within which the true elasticity likely lies with 95% confidence. * **P-value:** 0.000429671. This indicates that the observed relationship is statistically significant. * **Number of Observations (n_obs):** 98. This tells me the sample size of the analysis. * **R-squared (r2):** 0.600864801103209. This shows the proportion of variance in log income per capita explained by the model, suggesting a decent fit. * **Intercept:** 5.42988. This is the predicted log income per capita when all predictor variables are zero, though its direct interpretation might be limited in this context. * **Least Squares (ls) coefficient:** 1.42401. This is another coefficient from the model, likely for the investment share, but the prompt specifically asks for the elasticity related to $(n+g+\delta)$. Now, I need to tie these numbers back to the specific question asked: "In the Solow growth model, how does income per working-age person in 1985 vary across the non-oil-producing countries with the growth rate of the working-age population plus technological progress and depreciation (assumed to total 0.05), holding the investment share fixed? Report the elasticity of income with respect to that sum." The prompt explicitly states the model: $\ln(Y/L) = a + \beta_1 \ln(s) + \beta_2 \ln(n + g + \delta) + \epsilon$. It also clarifies that in this *log-log* specification, the coefficient $\beta_2$ directly represents the elasticity of income per working-age person with respect to $(n + g + \delta)$. This is crucial because it means the estimated coefficient of -1.98977 is precisely what I need to report as the elasticity. I need to ensure I use *only* numbers provided either in the `call_2e1883a7` results or the original prompt. I'll list them out to be sure: * 1985 (from the prompt) * 0.05 (from the prompt, the assumed total for $n+g+\delta$ which is held constant) * -1.98977 (the estimated coefficient for $\ln(n+g+\delta)$ from `call_2e1883a7:coef.lngd`) * 0.545241 (the standard error for that coefficient) * [-3.07222, -0.907334] (the 95% confidence interval for that coefficient) * 98 (the number of observations, `n_obs`, from `call_2e1883a7`) * 1.42401 (the coefficient for `ls`, the investment share, from `call_2e1883a7`) I also need to incorporate the specified caveats. These are important limitations of the analysis: 1. The OLS cross-sectional regression doesn't necessarily imply causality. There could be omitted variables like initial technology levels, institutional quality, or human capital that are influencing both income and the growth components, leading to potential omitted variable bias. 2. The assumption that $g + \delta$ is uniformly 0.05 across all countries is a simplifying one. In reality, these rates likely vary. Finally, I will structure the response carefully, adhering to all formatting instructions, and ensuring the language is English. I will also make sure to reference `call_2e1883a7:coef.lngd` as the source of the primary figure. This systematic breakdown ensures I'm addressing all parts of the prompt accurately and comprehensively, using the correct data and acknowledging the analytical context and limitations.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "In the textbook Solow growth model (Mankiw, Romer, and Weil, 1992), estimating the cross-country regression of log real GDP per working-age person in 1985 on the log of the investment share and the log of the sum of population growth, technological change, and depreciation (ln(n + g + δ)) across the 98 non-oil-producing countries yields an elasticity of income with respect to (n + g + δ) of -1.98977 (HC1 robust standard error of 0.545241; 95% CI [-3.07222, -0.907334]). \n\nHolding the investment share fixed (coefficient of 1.42401), a higher rate of population growth plus technological progress and depreciation is negatively associated with output per worker, with an elasticity close to -1.99.",
        "caveats": [
          "Cross-sectional OLS identifies an association rather than a causal effect due to potential unobserved country heterogeneity and omitted variable bias (e.g., differences in initial technology levels, institutions, or human capital).",
          "The specification imposes a uniform rate of technological progress and depreciation (g + δ = 0.05) across all countries."
        ],
        "figure_refs": [
          "call_2e1883a7:coef.lngd"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
16. 15Guarda
    resposta aceita

    Números encontrados

    1992 · 1985 · 98 · -1.98977 · 0.545241 · 95% · -3.07222 · -0.907334 · 1.42401 · -1.99 · 0.05

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
17. 16Resposta

    In the textbook Solow growth model (Mankiw, Romer, and Weil, 1992), estimating the cross-country regression of log real GDP per working-age person in 1985 on the log of the investment share and the log of the sum of population growth, technological change, and depreciation (ln(n + g + δ)) across the 98 non-oil-producing countries yields an elasticity of income with respect to (n + g + δ) of -1.98977 (HC1 robust standard error of 0.545241; 95% CI [-3.07222, -0.907334]). Holding the investment share fixed (coefficient of 1.42401), a higher rate of population growth plus technological progress and depreciation is negatively associated with output per worker, with an elasticity close to -1.99. Ressalvas: Cross-sectional OLS identifies an association rather than a causal effect due to potential unobserved country heterogeneity and omitted variable bias (e.g., differences in initial technology levels, institutions, or human capital). The specification imposes a uniform rate of technological progress and depreciation (g + δ = 0.05) across all countries.

    • Cross-sectional OLS identifies an association rather than a causal effect due to potential unobserved country heterogeneity and omitted variable bias (e.g., differences in initial technology levels, institutions, or human capital).
    • The specification imposes a uniform rate of technological progress and depreciation (g + δ = 0.05) across all countries.

    Figuras citadas: call_2e1883a7:coef.lngd · Parada: answered

    Figura da resposta: call_2e1883a7:coef.lngd = -1,98977-1.9897744846061867 (se 0,5452410.5452412830469935)
18. 17Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
19. 18Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_2e1883a7:coef.lngd (infer.ols)
20. 19Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.ols
21. 20Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'ly'
22. 21Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x[0] = 'lngd'
23. 22Pontuação
    #5argumento igualpassouregistradainfer.ols.y = 'ly'
24. 23Pontuação
    #6diagnósticopassouregistrada98 vs 98
25. 24Pontuação
    #7figura da resposta próximapassouregistrada-1.989774485 vs -1.9897744846061867
26. 25Pontuação
    #8figura da resposta próximafalhouregistrada0.545241283 vs 0.5633618948953987
27. 26Pontuação
    #9figura da resposta próximafalhouregistrada-1.989774485 vs -1.97
28. 27Pontuação
    #10figura da resposta próximafalhouregistrada0.545241283 vs 0.56

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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