Tarefa paper_repro.mrw_1992.tab1_nonoil_ls.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 share of output that is invested, holding the growth of the working-age population (plus technological progress and depreciation, assumed to total 0.05) fixed? Report the elasticity of income with respect to the investment share.

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

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

   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_c2cc16f5gemini-3.8-flashFim: STOP8.538 ms14641 de entrada · 106 de saída · 406 de raciocínio · 0 em cache

   Texto visível

   I will run an ordinary least squares regression of the log of 1985 GDP per working-age person on the log of the investment share, controlling for the log of population growth plus 0.05, restricted to non-oil countries.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on the Solow Growth Model Analysis** Alright, so the core of the request is to understand how income per working-age person in 1985, within the context of the Solow growth model, is influenced by the investment share, specifically for non-oil-producing countries. They want this analyzed while holding the combined growth rate of the working-age population, technological progress, and depreciation (which are collectively given as 0.05) constant. Crucially, they're asking for the *elasticity* of income with respect to the investment share. This means I need to determine how a percentage change in the investment share translates into a percentage change in income per capita. My first step, naturally, is to examine the provided dataset, `growthdj`. I need to identify the relevant variables. I see columns like `oil`, which is key for filtering for "non-oil-producing countries" (where `oil == 'no'`). Then there's `gdp85`, which represents real GDP per working-age person in 1985 – that's my dependent variable of interest. For the investment share, I see `s_share`, and the request implies I'll be working with its logarithm, which is conveniently provided as `ls`. Now, I recall the standard specification of the Solow model as presented in Mankiw, Romer, and Weil (1992), particularly Table I. The model is typically estimated as: $\ln(Y/L) = \beta_0 + \beta_1 \ln(I/GDP) + \beta_2 \ln(n + g + \delta) + \epsilon$ Looking at my dataset's columns, this translates directly to: * Dependent variable: `ly` (which is the logarithm of `gdp85`) * Independent variable 1: `ls` (the logarithm of the investment share, `s_share`) * Independent variable 2: `lngd` (the logarithm of `n + g + delta`). The request specifies holding `n + g + delta` constant at 0.05. This corresponds to the `lngd` term in the regression. The dataset helpfully provides `lngd` which is likely the log of that exact sum. The sample constraint is also clear: "non-oil-producing countries," which means I need to filter the data where the `oil` column is 'no'. To get the elasticity of income with respect to the investment share, I need to run an Ordinary Least Squares (OLS) regression. The coefficient $\beta_1$ on `ls` in the estimated equation will directly give me this elasticity because the dependent variable is $\ln(Y/L)$ and one of the independent variables is $\ln(I/GDP)$. A change in $\ln(I/GDP)$ directly reflects a percentage change in $I/GDP$, and a change in $\ln(Y/L)$ directly reflects a percentage change in $Y/L$. Therefore, $\beta_1 = \frac{\partial \ln(Y/L)}{\partial \ln(I/GDP)}$, which is precisely the definition of the elasticity. So, my plan is to: 1. Filter the `growthdj` dataset to include only rows where `oil == 'no'`. 2. Run an OLS regression using `ly` as the dependent variable, and `ls` and `lngd` as independent variables. 3. Extract the coefficient for `ls` from the regression output. This coefficient will be my reported elasticity. I'll use the `infer__ols` function to perform this regression. I need to be confident that `lngd` in the dataset accurately represents the log of (population growth + technological progress + depreciation) as stated in the problem. Given the context and the column name, this is a safe assumption.

   Chamadas pedidas

   • infer.ols

     {
       "controls": [
         "lngd"
       ],
       "dataset": "growthdj",
       "se": "HC1",
       "subset": "oil == 'no'",
       "x": [
         "ls"
       ],
       "y": "ly"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
10. 9Ferramenta
    infer.olsv0.1.0call_c293748bdecidido pelo modeloreexecutável

    Motivo: I will run an ordinary least squares regression of the log of 1985 GDP per working-age person on the log of the investment share, controlling for the log of population growth plus 0.05, restricted to non-oil countries.

    Argumentos

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

    Figuras call_c293748b

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.ls Coefficient on ls in the regression of ly	1,42401	1.424014283369419	0,1319620.13196226184630017	1,16204 – 1,68599

    Diagnósticos

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

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 8.577647478110183,
          "ci_low": 2.2821187134531202,
          "coef": 5.429883095781651,
          "p": 0.0009107501109057622,
          "se": 1.585575011306027,
          "t": 3.424551381715517,
          "term": "Intercept"
        },
        {
          "ci_high": 1.6859924961246415,
          "ci_low": 1.1620360706141963,
          "coef": 1.424014283369419,
          "p": 3.409312626758513e-18,
          "se": 0.13196226184630017,
          "t": 10.7910721099037,
          "term": "ls"
        },
        {
          "ci_high": -0.9073336802845176,
          "ci_low": -3.072215288927856,
          "coef": -1.9897744846061867,
          "p": 0.0004296714962462017,
          "se": 0.5452412830469936,
          "t": -3.649346713966064,
          "term": "lngd"
        }
      ]
    }

    Digests dos dados

    growthdj e3f509733efe06358f9503c526bd45111b6a64feb75b292419aae56055b598f2

    sha256 do resultado

    ab409a934881c21507d0d8127b63ffc15c6485a3c8384678f1c7552cc6eaaeae

    Resultado mostrado ao modelo
11. 10Turno do modelo
    call_944bfd8cgemini-3.8-flashFim: STOP6.251 ms15468 de entrada · 82 de saída · 178 de raciocínio · 0 em cache

    Texto visível

    I will estimate the regression with classical standard errors to compare with the textbook Mankiw, Romer, and Weil (1992) specification.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the Mankiw, Romer, and Weil (1992) Results** My initial step was to meticulously examine the empirical findings presented in Table I of Mankiw, Romer, and Weil (1992). I specifically focused on the row labeled "Non-oil," as this segment of their analysis pertains to countries that are not significantly influenced by oil revenues, a crucial distinction for understanding broader determinants of growth. The dependent variable of interest was the natural logarithm of output per worker, ln(Y/L), as measured in 1985. This choice of variable is, of course, standard for capturing differences in income levels. The sample size for this particular specification was N = 98, representing the non-oil economies. My attention was then drawn to the estimated constant term: 5.48. I noted the standard error associated with this constant, which was 1.59. However, a question immediately arose in my mind regarding the standard error estimation method employed. It's critical for me, as an expert, to be certain about the underlying statistical assumptions. Therefore, I decided to explicitly check the results using "classical" standard errors. I wanted to ascertain if the standard error of 1.59 was derived using robust methods or if it was the more traditional, and potentially less reliable, classical estimate. To gain further insight, I decided to retrieve and review the reported coefficients and standard errors for the explanatory variables in the original MRW (1992) paper. I recalled that for ln(I/GDP), the coefficient was 1.42 with a standard error of 0.14. For ln(n+g+d), the coefficient was -1.97 with a standard error of 0.56. This recollection served as a baseline against which I would compare my own recalculations. The R-squared value of 0.59 indicated that the model explained a significant portion of the variation in ln(Y/L) for this sample. Finally, my immediate action was to re-run the regression with the `se="classical"` option enabled. My objective here was to empirically verify whether the standard errors generated by this specific option in my current analytical environment were identical or very close to the standard errors reported in the original MRW paper. This verification is not merely a matter of academic curiosity; it's essential for ensuring the reproducibility of key findings and for understanding the precise statistical foundations of the presented results, especially when interpreting their significance and robustness.

    Chamadas pedidas

    • infer.ols

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

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
12. 11Ferramenta
    infer.olsv0.1.0call_7dd1916cdecidido pelo modeloreexecutável

    Motivo: I will estimate the regression with classical standard errors to compare with the textbook Mankiw, Romer, and Weil (1992) specification.

    Argumentos

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

    Figuras call_7dd1916c

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.ls Coefficient on ls in the regression of ly	1,42401	1.424014283369419	0,1431060.14310581649732	1,13991 – 1,70812

    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": 2.285464121413495,
          "coef": 5.429883095781651,
          "p": 0.0008999523718049453,
          "se": 1.5838898803304136,
          "t": 3.4281948279440546,
          "term": "Intercept"
        },
        {
          "ci_high": 1.708115249178197,
          "ci_low": 1.1399133175606408,
          "coef": 1.424014283369419,
          "p": 2.104644525379704e-16,
          "se": 0.14310581649732,
          "t": 9.950778509384257,
          "term": "ls"
        },
        {
          "ci_high": -0.8713597174283383,
          "ci_low": -3.108189251784035,
          "coef": -1.9897744846061867,
          "p": 0.0006385352057009347,
          "se": 0.5633618948953983,
          "t": -3.531964981365373,
          "term": "lngd"
        }
      ]
    }

    Digests dos dados

    growthdj e3f509733efe06358f9503c526bd45111b6a64feb75b292419aae56055b598f2

    sha256 do resultado

    5d0ad0ba7dca3913dc9a7d33df76145429cc73d89b5e5eb9723f418fec8ed6db

    Resultado mostrado ao modelo
13. 12Turno do modelo
    call_f8fd1f2dgemini-3.8-flashFim: STOP10.935 ms16037 de entrada · 32 de saída · 984 de raciocínio · 12593 em cache

    Texto visível

    I will load the reporting-numbers skill to follow best reporting practices.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding the Solow Growth Model Analysis** I've been asked to analyze the relationship between the investment share and income per working-age person in 1985 within the Solow growth model, specifically for non-oil-producing countries. I need to hold population growth, technological progress, and depreciation constant at a combined rate of 0.05. Crucially, I also need to report the elasticity of income with respect to the investment share. First, I encountered a slight ambiguity in the instructions regarding the language of the response. The user's question is clearly in English, and the instructions state, "Write the answer in the language of the question." However, there's a parenthetical note: "(Portuguese: decimal comma)". My understanding is that "the language of the question" should take precedence. Since the question is in English, I should respond in English. The note about the decimal comma seems to be a default instruction that doesn't apply when the question itself is not in Portuguese. I'll proceed with standard English conventions for numbers, using a decimal point. Now, let's break down the core task. The user wants to understand how income per working-age person (which I'll represent as $Y/L$) varies with the investment share ($I/Y$). The Solow model, as often formulated and as implied by the variables I'm likely to be working with, posits a relationship where capital accumulation, driven by investment, is a key determinant of output. Specifically, in the context of the model and considering the data, I expect to use a log-log specification. This is because if the model is specified as $Y/L = A \cdot (I/Y)^\alpha \cdot (\dots)^{1-\alpha}$ (simplified), taking the natural logarithm of both sides yields $\ln(Y/L) = \ln A + \alpha \ln(I/Y) + \dots$. In this formulation, the coefficient on $\ln(I/Y)$ directly represents the elasticity of income per working-age person with respect to the investment share. I've been provided with the results of an OLS regression conducted on the non-oil-producing countries sample. The dependent variable is the log of 1985 GDP per working-age person (let's call this `ly`), and one of the key independent variables is the log of the investment share (let's call this `ls`). Another independent variable is the log of the combined growth rate of the working-age population, technological progress, and depreciation, which is fixed at 0.05 (let's call this `lngd`). This setup aligns perfectly with the theoretical underpinnings of the Solow model, particularly the augmented version by Mankiw, Romer, and Weil (1992), which incorporates human capital and population growth. The core of the user's question is about the elasticity. In a log-log regression, the coefficient of an independent variable directly measures the elasticity of the dependent variable with respect to that independent variable. Therefore, the coefficient on `ls` (the log of the investment share) will be my direct answer for the elasticity. Looking at the provided regression results: * The coefficient estimate for `ls` is **1.42401**. This is the elasticity I need to report. * I also need to consider the uncertainty surrounding this estimate. I have two sets of standard errors: * Robust (HC1) standard error: This gives a 95% confidence interval of [1.16204, 1.68599]. * Classical standard error: This gives a 95% confidence interval of [1.13991, 1.70812]. The robust standard errors are generally preferred as they are less sensitive to potential heteroskedasticity in the error terms. The interval from the robust standard errors is [1.16204, 1.68599]. The model also includes `lngd` (log of population growth + technology + depreciation) with a coefficient of -1.98977. This coefficient is negative, which is consistent with the Solow model's prediction that higher population growth rates (and associated factors like technological progress and depreciation) lead to lower steady-state income per capita, holding other factors constant. The constant term is 5.42988. The R-squared is 0.600864801103209, indicating that about 60% of the variation in log GDP per working-age person is explained by the included variables. The sample size is 98 observations. So, to summarize my findings: the elasticity of income per working-age person with respect to the investment share, within this specific Solow model framework for non-oil economies, is estimated to be 1.42401. This means that a 1% increase in the investment share is associated with approximately a 1.424% increase in income per working-age person, holding population growth, technological progress, and depreciation constant. The uncertainty around this estimate is captured by the confidence interval [1.16204, 1.68599].

    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_a0577841gemini-3.8-flashFim: STOP10.902 ms17489 de entrada · 427 de saída · 564 de raciocínio · 12822 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on the Regression Analysis** Alright, let's break down my thinking as I worked through this regression output. My primary goal was to extract the key findings accurately and present them in a clear, first-person narrative, as if I were thinking aloud about the results. First, I focused on the core instruction: "Write numbers as the tool stated them (you may round to fewer digits; Portuguese answers use a decimal comma). Give the interval with its level when the tool reported one." Since the question was in English, I knew my output needed to be in English and adhere to standard decimal notation. Then came the crucial step of verifying the figures. I saw that the tool provided two potential sources for the coefficient estimate: `call_c293748b:coef.ls` and `call_7dd1916c:coef.ls`. A quick glance revealed that both pointed to the same central estimate: `1.42401`. This consistency was reassuring. I decided to cite `call_c293748b:coef.ls` as it was explicitly marked as "robust," which I generally prefer in econometrics, but I also wanted to acknowledge the classical standard error and confidence interval for completeness. Next, I meticulously reviewed the caveats. This is where the real analytical work happens. I recognized the inherent limitations of a cross-sectional OLS analysis applied to countries. The key concern that immediately jumped out was the assumption of orthogonality between the error term (representing technological differences and country-specific shocks) and the regressors (investment rate and population growth). I know this assumption can easily be violated due to omitted variable bias – think about factors like human capital, institutional quality, or even geographical endowments that influence both investment and growth but aren't explicitly in the model. Then there's the potential for reverse causality, where growth might influence investment, not just the other way around. I also recalled the seminal work by Mankiw, Romer, and Weil (1992). Their contribution about augmenting the Solow model with human capital was fresh in my mind. They highlighted that without schooling, the standard Solow model predicted an implausibly high capital share ($\alpha \approx 0.60$) to fit the data. This underscores the importance of considering broader factors beyond just physical capital and labor. With these foundational checks complete, I began assembling the response. I knew I had to state the estimated elasticity clearly. So, I wrote down: "The estimated elasticity is 1.42401." Then, I added the associated statistical information, ensuring I specified the standard error and confidence interval. I opted to present the robust standard error first: "(SE: 0.131962 with HC1 robust standard errors, 95% CI: [1.16204, 1.68599])" and then followed with the classical one: "or classical SE: 0.143106, 95% CI: [1.13991, 1.70812]." This provides a comprehensive view of the precision of the estimate. I also made sure to include other vital pieces of information from the output: the number of observations ("98 non-oil countries"), the R-squared ("$R^2$: 0.600864801103209"), the coefficient on $\ln(n + g + \delta)$ ("-1.98977"), and the intercept ("5.42988"). These provide context and allow for a fuller interpretation of the model's fit and specific parameter estimates. Finally, I did a quick mental sweep to ensure everything was clear, logical, and directly addressed the initial instructions. Yes, all figures were written as reported (with appropriate decimal notation), the language was English as per the question, and the confidence intervals were included with their levels. The robustness of the estimate and the caveats were also carefully considered. I felt confident that the summary I was about to produce would be a "fulsome" account of my thought process.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "In the cross-country regression of the textbook Solow growth model (Mankiw, Romer, and Weil, 1992) for the 98 non-oil-producing countries, the estimated elasticity of 1985 real GDP per working-age person with respect to the investment share is 1.42401 (heteroskedasticity-robust standard error of 0.131962; 95% CI [1.16204, 1.68599]; classical standard error of 0.143106, 95% CI [1.13991, 1.70812]).\n\nThis estimate comes from an ordinary least squares regression of the log of 1985 GDP per working-age person on the log of the investment share and the log of the effective population growth rate (popgrowth + 0.05), with an intercept of 5.42988 and an R² of 0.600864801103209. Holding population growth, depreciation, and technological growth constant, higher investment rates are strongly positively associated with income per working-age person.",
        "caveats": [
          "Cross-country OLS assumes that country-specific technology levels and unobserved factors are uncorrelated with saving and population growth rates; omitted factors such as institutions, human capital, or geographic traits could bias the estimate.",
          "Under the textbook Solow model, an elasticity of approximately 1.42 implies an implied physical capital share of output around 0.6, which is higher than the standard national accounts capital share (about one third); MRW address this by augmenting the model with human capital."
        ],
        "figure_refs": [
          "call_c293748b:coef.ls",
          "call_7dd1916c:coef.ls"
        ]
      }

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

    Números encontrados

    1992 · 98 · 1985 · 1.42401 · 0.131962 · 95% · 1.16204 · 1.68599 · 0.143106 · 95% · 1.13991 · 1.70812 · 1985 · 0.05 · 5.42988 · 0.600864801103209 · 1.42 · 0.6

    Sem lastro

    —

    Não citados

    —

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

    In the cross-country regression of the textbook Solow growth model (Mankiw, Romer, and Weil, 1992) for the 98 non-oil-producing countries, the estimated elasticity of 1985 real GDP per working-age person with respect to the investment share is 1.42401 (heteroskedasticity-robust standard error of 0.131962; 95% CI [1.16204, 1.68599]; classical standard error of 0.143106, 95% CI [1.13991, 1.70812]). This estimate comes from an ordinary least squares regression of the log of 1985 GDP per working-age person on the log of the investment share and the log of the effective population growth rate (popgrowth + 0.05), with an intercept of 5.42988 and an R² of 0.600864801103209. Holding population growth, depreciation, and technological growth constant, higher investment rates are strongly positively associated with income per working-age person. Ressalvas: Cross-country OLS assumes that country-specific technology levels and unobserved factors are uncorrelated with saving and population growth rates; omitted factors such as institutions, human capital, or geographic traits could bias the estimate. Under the textbook Solow model, an elasticity of approximately 1.42 implies an implied physical capital share of output around 0.6, which is higher than the standard national accounts capital share (about one third); MRW address this by augmenting the model with human capital.

    • Cross-country OLS assumes that country-specific technology levels and unobserved factors are uncorrelated with saving and population growth rates; omitted factors such as institutions, human capital, or geographic traits could bias the estimate.
    • Under the textbook Solow model, an elasticity of approximately 1.42 implies an implied physical capital share of output around 0.6, which is higher than the standard national accounts capital share (about one third); MRW address this by augmenting the model with human capital.

    Figuras citadas: call_c293748b:coef.ls, call_7dd1916c:coef.ls · Parada: answered

    Figura da resposta: call_c293748b:coef.ls = 1,424011.424014283369419 (se 0,1319620.13196226184630017)
18. 17Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
19. 18Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_c293748b:coef.ls (infer.ols)
20. 19Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.ols
21. 20Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'ly'
22. 21Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x[0] = 'ls'
23. 22Pontuação
    #5argumento igualpassouregistradainfer.ols.y = 'ly'
24. 23Pontuação
    #6diagnósticopassouregistrada98 vs 98
25. 24Pontuação
    #7figura da resposta próximapassouregistrada1.424014283 vs 1.4240142833694192
26. 25Pontuação
    #8figura da resposta próximafalhouregistrada0.1319622618 vs 0.14310581649732004
27. 26Pontuação
    #9figura da resposta próximapassouregistrada1.424014283 vs 1.42
28. 27Pontuação
    #10figura da resposta próximafalhouregistrada0.1319622618 vs 0.14

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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