Tarefa paper_repro.angrist_evans_1998.tab6_c4.design

Veredito

Pergunta feita ao analista

Do parents whose first two children are of the same sex go on to have more children? Among married women aged 21-35 with two or more children in the 1980 Census, estimate how much more likely a mother is to have a third child when her first two children are both boys or both girls.

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

   Do parents whose first two children are of the same sex go on to have more children? Among married women aged 21-35 with two or more children in the 1980 Census, estimate how much more likely a mother is to have a third child when her first two children are both boys or both girls.

   fertility (agent)

   The 1980 Census sample of Angrist and Evans (1998), 'Children and Their Parents' Labor Supply', as distributed in the R package AER (data set Fertility, served by Rdatasets): married women aged 21-35 with two or more children, one row per woman (254,654 rows). Columns: gender1 and gender2, the sex of the first and the second child ('male' or 'female'); boy1st and boy2nd, 1 if the first (second) child is a boy; samesex, 1 if the first two children are of the same sex (two boys or two girls); morekids, 1 if the mother has more than 2 children; agem1, the mother's age in years; blackm, hispm and othracem, 1 if the mother is Black, Hispanic, or of another race (white is the reference); weeksm1, the number of weeks the mother worked in 1979. The file has no age at first birth.

   file ca9be592b79dddbc2f49ff80f45d0dbe31aac4afb57ff88dec57376c3f3e3452 · frame —

   1. derive {"args":{"column":"morekids","default":null,"keep_unmapped":false,"mapping":[{"source":"yes","target":1},{"source":"no","target":0}]},"name":"morekids","op":"map"}
   2. derive {"args":{"column":"afam","default":null,"keep_unmapped":false,"mapping":[{"source":"yes","target":1},{"source":"no","target":0}]},"name":"blackm","op":"map"}
   3. derive {"args":{"column":"hispanic","default":null,"keep_unmapped":false,"mapping":[{"source":"yes","target":1},{"source":"no","target":0}]},"name":"hispm","op":"map"}
   4. derive {"args":{"column":"other","default":null,"keep_unmapped":false,"mapping":[{"source":"yes","target":1},{"source":"no","target":0}]},"name":"othracem","op":"map"}
   5. derive {"args":{"column":"gender1","default":null,"keep_unmapped":false,"mapping":[{"source":"male","target":1},{"source":"female","target":0}]},"name":"boy1st","op":"map"}
   6. derive {"args":{"column":"gender2","default":null,"keep_unmapped":false,"mapping":[{"source":"male","target":1},{"source":"female","target":0}]},"name":"boy2nd","op":"map"}
   7. derive {"args":{"left":"boy1st","right":"boy2nd"},"name":"sex_gap","op":"diff"}
   8. derive {"args":{"cmp":"eq","column":"sex_gap","value":0},"name":"samesex","op":"indicator"}
   9. rename {"map":{"age":"agem1","work":"weeksm1"}}
   10. select {"columns":["gender1","gender2","boy1st","boy2nd","samesex","morekids","agem1","blackm","hispm","othracem","weeksm1"]}
2. 1Transformação
   fertility · derive {"args":{"column":"morekids","default":null,"keep_unmapped":false,"mapping":[{"source":"yes","target":1},{"source":"no","target":0}]},"name":"morekids","op":"map"}

   254654 → 254654 linhas

   Frame antes → depois: 14e3e85c163d → 5a972366e3e8
3. 2Transformação
   fertility · derive {"args":{"column":"afam","default":null,"keep_unmapped":false,"mapping":[{"source":"yes","target":1},{"source":"no","target":0}]},"name":"blackm","op":"map"}

   254654 → 254654 linhas

   Frame antes → depois: 5a972366e3e8 → 18ccebde6b6d
4. 3Transformação
   fertility · derive {"args":{"column":"hispanic","default":null,"keep_unmapped":false,"mapping":[{"source":"yes","target":1},{"source":"no","target":0}]},"name":"hispm","op":"map"}

   254654 → 254654 linhas

   Frame antes → depois: 18ccebde6b6d → 904f43a67031
5. 4Transformação
   fertility · derive {"args":{"column":"other","default":null,"keep_unmapped":false,"mapping":[{"source":"yes","target":1},{"source":"no","target":0}]},"name":"othracem","op":"map"}

   254654 → 254654 linhas

   Frame antes → depois: 904f43a67031 → 8aaea27cf87b
6. 5Transformação
   fertility · derive {"args":{"column":"gender1","default":null,"keep_unmapped":false,"mapping":[{"source":"male","target":1},{"source":"female","target":0}]},"name":"boy1st","op":"map"}

   254654 → 254654 linhas

   Frame antes → depois: 8aaea27cf87b → 666ccf9c0cdf
7. 6Transformação
   fertility · derive {"args":{"column":"gender2","default":null,"keep_unmapped":false,"mapping":[{"source":"male","target":1},{"source":"female","target":0}]},"name":"boy2nd","op":"map"}

   254654 → 254654 linhas

   Frame antes → depois: 666ccf9c0cdf → b2ccbd1a7c9a
8. 7Transformação
   fertility · derive {"args":{"left":"boy1st","right":"boy2nd"},"name":"sex_gap","op":"diff"}

   254654 → 254654 linhas

   Frame antes → depois: b2ccbd1a7c9a → b25bee20ec5a
9. 8Transformação
   fertility · derive {"args":{"cmp":"eq","column":"sex_gap","value":0},"name":"samesex","op":"indicator"}

   254654 → 254654 linhas

   Frame antes → depois: b25bee20ec5a → 935084af856b
10. 9Transformação
    fertility · rename {"map":{"age":"agem1","work":"weeksm1"}}

    254654 → 254654 linhas

    Frame antes → depois: 935084af856b → ccc82e5f5788
11. 10Transformação
    fertility · select {"columns":["gender1","gender2","boy1st","boy2nd","samesex","morekids","agem1","blackm","hispm","othracem","weeksm1"]}

    254654 → 254654 linhas

    Frame antes → depois: ccc82e5f5788 → c7a28c42496c
12. 11Turno do modelo
    call_8c4969bfgemini-3.8-flashFim: STOP8.075 ms14398 de entrada · 89 de saída · 383 de raciocínio · 0 em cache

    Texto visível

    I will describe the variables of interest in the `fertility` dataset to inspect summary statistics and ensure there are no missing values.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on the Fertility Dataset and the Question of Parental Decisions** Alright, so the user has posed an interesting question about parental decision-making regarding subsequent children, specifically focusing on whether the sex of the first two children influences the likelihood of having a third. The core of the query is to estimate the *additional* probability of a mother having a third child when her first two are of the same sex (both boys or both girls). My first step is to orient myself with the provided dataset, which is conveniently named `fertility`. I see it's quite substantial, with 254,654 rows. I'll quickly scan the available columns to see how they map to the question at hand. I see `gender1` and `gender2`, which are important, and also `boy1st` and `boy2nd`, which are more direct indicators. Crucially, there's a `samesex` variable, which is coded as 1 if the first two children are indeed of the same sex. This directly aligns with one of the key conditions in the user's question. On the outcome side, `morekids` is coded as 1 if the mother has more than two children. This is exactly what I need to assess the "having a third child" aspect. The other variables (`agem1`, `blackm`, `hispm`, `othracem`, `weeksm1`) provide demographic context, which might be useful for deeper analysis or controlling for confounding factors, but for the primary question, `samesex` and `morekids` are the immediate focus. Now, I need to carefully consider what the user is *really* asking. They want to know "how much more likely a mother is to have a third child when her first two children are both boys or both girls." This is about the *direct effect* of `samesex` on `morekids`. It's not about using `samesex` as an instrument for something else, although I recognize that connection. I recall Angrist and Evans' work where `samesex` is used as an instrumental variable for subsequent childbearing in the context of female labor supply. However, here, the focus is precisely on that first stage – the relationship between having same-sex first two children and the propensity to have a third. This leads me to think about the underlying assumptions. Can I treat `samesex` as a randomly assigned variable, or at least as good as randomly assigned? Biologically, the sex of a child is largely determined by chance events during conception. While there might be some very subtle biological predispositions, for practical purposes in this context, it's reasonable to consider the sex of the first two children as being quasi-randomly assigned. This is a key assumption that allows for a more straightforward interpretation of the relationship between `samesex` and `morekids`. Given this, the question becomes whether I can directly estimate this probability difference. The user's prompt specifically mentions `infer__ate` or `infer__ols`. This suggests that they are looking for a causal inference approach, and `infer__ate` (likely referring to an average treatment effect estimation) or `infer__ols` (ordinary least squares, which can estimate causal effects under strong assumptions like exogeneity) are appropriate tools. Since `samesex` is binary and appears to be as good as randomly assigned, I can think about this as estimating the difference in the mean of `morekids` between the `samesex=1` group and the `samesex=0` group. Before jumping into estimation, a quick sanity check using `data__describe` or similar descriptive statistics would be prudent. I'd want to see the prevalence of `samesex` and `morekids` in the dataset, and importantly, the cross-tabulation of `samesex` and `morekids` to get an initial feel for the magnitudes involved. This would inform my approach and help me anticipate the results.

    Chamadas pedidas

    • data.describe

      {
        "columns": [
          "samesex",
          "morekids",
          "agem1",
          "blackm",
          "hispm",
          "othracem",
          "boy1st",
          "boy2nd"
        ],
        "dataset": "fertility"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
13. 12Ferramenta
    data.describev0.1.0call_1234868fdecidido pelo modeloreexecutável

    Motivo: I will describe the variables of interest in the `fertility` dataset to inspect summary statistics and ensure there are no missing values.

    Argumentos

    {
      "columns": [
        "samesex",
        "morekids",
        "agem1",
        "blackm",
        "hispm",
        "othracem",
        "boy1st",
        "boy2nd"
      ],
      "dataset": "fertility"
    }

    Figuras call_1234868f

    Chave	Valor	Valor bruto	Erro-padrão	IC
    n_rows Rows in the dataset	254.654 rows	254654	—	—

    Tabelas (1)

    {
      "columns": [
        {
          "column": "samesex",
          "dtype": "int64",
          "max": 1,
          "mean": 0.5055683397865339,
          "min": 0,
          "n_missing": 0,
          "n_unique": 2,
          "sd": 0.4999699742967964
        },
        {
          "column": "morekids",
          "dtype": "int64",
          "max": 1,
          "mean": 0.38056343116542446,
          "min": 0,
          "n_missing": 0,
          "n_unique": 2,
          "sd": 0.48552634504764797
        },
        {
          "column": "agem1",
          "dtype": "int64",
          "max": 35,
          "mean": 30.39326694259662,
          "min": 21,
          "n_missing": 0,
          "n_unique": 15,
          "sd": 3.386446978088242
        },
        {
          "column": "blackm",
          "dtype": "int64",
          "max": 1,
          "mean": 0.05166225545249633,
          "min": 0,
          "n_missing": 0,
          "n_unique": 2,
          "sd": 0.22134466157176644
        },
        {
          "column": "hispm",
          "dtype": "int64",
          "max": 1,
          "mean": 0.07420657048387223,
          "min": 0,
          "n_missing": 0,
          "n_unique": 2,
          "sd": 0.2621072779599885
        },
        {
          "column": "othracem",
          "dtype": "int64",
          "max": 1,
          "mean": 0.05634311654244583,
          "min": 0,
          "n_missing": 0,
          "n_unique": 2,
          "sd": 0.23058356088203943
        },
        {
          "column": "boy1st",
          "dtype": "int64",
          "max": 1,
          "mean": 0.5143606619177393,
          "min": 0,
          "n_missing": 0,
          "n_unique": 2,
          "sd": 0.49979471016360283
        },
        {
          "column": "boy2nd",
          "dtype": "int64",
          "max": 1,
          "mean": 0.5125503624525827,
          "min": 0,
          "n_missing": 0,
          "n_unique": 2,
          "sd": 0.49984344500238426
        }
      ]
    }

    Digests dos dados

    fertility c7a28c42496c51fa4ea223123cad6242aaa393634242d66e383c351dd0f471ef

    sha256 do resultado

    baf375ed305f6e9d03840e7f55f62074d3f24df1f86289d0174b0bd955206a4e

    Resultado mostrado ao modelo
14. 13Turno do modelo
    call_c072faafgemini-3.8-flashFim: STOP7.621 ms15188 de entrada · 99 de saída · 414 de raciocínio · 0 em cache

    Texto visível

    I will estimate the average treatment effect of having the first two children of the same sex on the likelihood of having more children using `infer.ate`.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Estimating the Impact of Same-Sex Siblings on Subsequent Births** Alright, so I'm looking at this dataset and the core question is whether parents who've already had two children of the same sex are more likely to have a third. The data comes from the 1980 Census, specifically focusing on married women aged 21-35 who already have at least two children. The crucial variable, `samesex`, is described as being quasi-randomly assigned, based on the biological sex of the first two children. This immediately makes me think about causal inference methods. The outcome of interest, `morekids`, is a binary indicator of whether they go on to have a third child. So, the objective is to estimate the difference in probability of having a third child between mothers whose first two are same-sex versus those whose first two are different sexes. My initial instinct is to consider the `infer__ate` and `infer__ols` functions. Let's break down `infer__ate` first. Its description mentions estimating the "difference in means between randomly assigned treatment and control groups, estimated by OLS with optional baseline covariates, with robust or cluster-robust errors." It's explicitly stated for situations where "treatment was assigned at random (an experiment or a lottery) and everyone assigned was treated." This raises a question for me: does `samesex` truly fit the criteria for random assignment as described by `infer__ate`? While it's termed "quasi-random," I need to be sure it aligns with the assumptions of a randomized experiment for `infer__ate` to be the most appropriate tool. Given this, I'm pondering whether `infer__ols` or `infer__ate` is the better fit, or if perhaps both could be informative. I recall Angrist and Evans (1998) and their work on similar topics. I should check their Table 5 or Table 6 to see how they approached this. They apparently ran regressions of `morekids` on `samesex`, both with and without covariates like `boy1st`, `boy2nd`, `agem1`, and race dummies. This suggests that a simple OLS approach, potentially what `infer__ols` represents, is a viable path. However, looking closer at `infer__ate`, I see that the `covariates` argument can be passed "for precision." This is interesting. It suggests that `infer__ate` might still be applicable and even enhanced by including these baseline characteristics, even if the core assignment is considered quasi-random. I need to clarify whether `infer__ate` directly provides the Average Treatment Effect (ATE) or just group means. To that end, I think I should examine both `infer__ate` and `infer__ols` thoroughly. Perhaps I should even explore related skills, like those in `experiments-and-power` or `instrumental-variables`, to ensure I'm not missing a more nuanced approach, especially if the "quasi-random" assignment has subtle dependencies. And importantly, I should load the `reporting-numbers` skill to make sure I'm presenting my findings clearly and correctly, given the expertise of the intended audience. My next step will be to dive into the specifics of these functions and see how they handle the covariates and the nature of the `samesex` variable.

    Chamadas pedidas

    • infer.ate

      {
        "covariates": [
          "agem1",
          "blackm",
          "hispm",
          "othracem",
          "boy1st",
          "boy2nd"
        ],
        "dataset": "fertility",
        "treatment": "samesex",
        "y": "morekids"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
15. 14Ferramenta
    infer.atev0.1.0call_489903a6decidido pelo modeloreexecutável

    Motivo: I will estimate the average treatment effect of having the first two children of the same sex on the likelihood of having more children using `infer.ate`.

    Argumentos

    {
      "cluster": null,
      "covariates": [
        "agem1",
        "blackm",
        "hispm",
        "othracem",
        "boy1st",
        "boy2nd"
      ],
      "dataset": "fertility",
      "level": 0.95,
      "treatment": "samesex",
      "weights": null,
      "y": "morekids"
    }

    Figuras call_489903a6

    Chave	Valor	Valor bruto	Erro-padrão	IC
    ate Average effect of samesex on morekids	0,0685552	0.06855515835870375	0,001901770.0019017672484115276	0,0648278 – 0,0722826

    Diagnósticos

    • n_treated = 128.745128745
    • n_control = 125.909125909
    • se_type = "HC1"
    • n_clusters = —

    Digests dos dados

    fertility c7a28c42496c51fa4ea223123cad6242aaa393634242d66e383c351dd0f471ef

    sha256 do resultado

    472c08ea5cf0e62ff47597c5cf77382d56b6a0f0be86f4722477432ae6bd74b2

    Resultado mostrado ao modelo
16. 15Turno do modelo
    call_ea84c6adgemini-3.8-flashFim: STOP4.767 ms15809 de entrada · 56 de saída · 77 de raciocínio · 12555 em cache

    Texto visível

    I will estimate the difference in means without covariates using `infer.ate` to obtain the raw difference and check sensitivity.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process Regarding Causal Effect Estimation** So, I've just run an analysis using `infer__ate` and included covariates. The results show an Average Treatment Effect (ATE) of 0.0685552, with a standard error of 0.00190177, leading to a confidence interval between 0.0648278 and 0.0722826. This gives me a solid starting point, suggesting a statistically significant and reasonably precise estimate of the treatment's effect. However, my expert intuition immediately prompts me to question the necessity and impact of those covariates. While they often help to reduce variance and control for confounding, I need to ensure they are truly contributing to a more robust or interpretable estimate. My next logical step, therefore, is to see how this ATE changes if I were to run the `infer__ate` analysis *without* any covariates. This comparison will be crucial in understanding: 1. **The magnitude of the effect without controlling for potential confounders:** Is the observed effect primarily driven by the treatment itself, or is it a result of variables that are correlated with both the treatment and the outcome? 2. **The robustness of the estimate:** If the ATE is significantly different or has a much wider confidence interval when covariates are removed, it suggests that those covariates were indeed playing an important role in either stabilizing the estimate or accounting for important sources of variation. 3. **Model parsimony:** If the ATE and its precision remain largely unchanged without covariates, it might indicate that the included covariates were not essential for this particular estimation, potentially leading to a simpler, more interpretable model. In essence, I'm performing a sensitivity analysis. By stripping back the model to its most basic form (no covariates), I can gain a deeper understanding of the true effect of the treatment and the added value (or lack thereof) of the covariates I initially included. This is a fundamental step in any rigorous causal inference process – to interrogate the assumptions and contributions of each component of the model.

    Chamadas pedidas

    • infer.ate

      {
        "dataset": "fertility",
        "treatment": "samesex",
        "y": "morekids"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
17. 16Ferramenta
    infer.atev0.1.0call_426a64a7decidido pelo modeloreexecutável

    Motivo: I will estimate the difference in means without covariates using `infer.ate` to obtain the raw difference and check sensitivity.

    Argumentos

    {
      "cluster": null,
      "covariates": [],
      "dataset": "fertility",
      "level": 0.95,
      "treatment": "samesex",
      "weights": null,
      "y": "morekids"
    }

    Figuras call_426a64a7

    Chave	Valor	Valor bruto	Erro-padrão	IC
    ate Average effect of samesex on morekids	0,0675253	0.06752525745015382	0,0019190.001919004619014083	0,0637641 – 0,0712864

    Diagnósticos

    • n_treated = 128.745128745
    • n_control = 125.909125909
    • se_type = "HC1"
    • n_clusters = —

    Digests dos dados

    fertility c7a28c42496c51fa4ea223123cad6242aaa393634242d66e383c351dd0f471ef

    sha256 do resultado

    1647bc8764ec0294c07c922d8ea91819950a579649f44947f1bf43a89864a430

    Resultado mostrado ao modelo
18. 17Turno do modelo
    call_a5d25daagemini-3.8-flashFim: STOP5.511 ms16067 de entrada · 70 de saída · 236 de raciocínio · 12546 em cache

    Texto visível

    I will run an OLS regression using `infer.ols` to get the baseline share (intercept) and the exact coefficient on `samesex`.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Analyzing Treatment Effects** Okay, so my initial exploration into the Average Treatment Effect (ATE) in `infer.ate` without any covariates yielded a result of approximately 6.75 percentage points (0.0675253), with a standard error (SE) of 0.001919 and a 95% confidence interval (CI) ranging from 0.0637641 to 0.0712864. This gives me a baseline understanding of the treatment's effect in its purest form, as it were. Next, I incorporated demographic controls – specifically age, race, and indicators for having a first or second child of a particular sex (`boy1st`, `boy2nd`). After this adjustment, the ATE shifted slightly to 0.0685552, or roughly 6.86 percentage points. The SE here was 0.00190177, and the 95% CI was [0.0648278, 0.0722826]. This indicates that while the ATE itself nudged up a bit, the precision of the estimate remained quite similar. However, a nagging question arose: what were the actual baseline means for the treated and control groups *before* any modeling? I needed to anchor these ATE estimates to something more tangible. My immediate thought was to run `infer.did_2x2` or `infer.ols`, or even just directly inspect the group means. Then, a more direct path emerged: `infer.ols` with `samesex` as the regressor. This function conveniently provides both the intercept and the coefficient for `samesex` directly, which would allow me to ascertain those baseline group means. So, my plan solidified: I'll check the results of `infer.ols` both without any additional controls and then again with the same demographic controls I used in `infer.ate`. This will give me a clear picture of how the ATE changes and, crucially, what the underlying group means are in each scenario.

    Chamadas pedidas

    • infer.ols

      {
        "dataset": "fertility",
        "se": "HC1",
        "x": [
          "samesex"
        ],
        "y": "morekids"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
19. 18Ferramenta
    infer.olsv0.1.0call_6d793133decidido pelo modeloreexecutável

    Motivo: I will run an OLS regression using `infer.ols` to get the baseline share (intercept) and the exact coefficient on `samesex`.

    Argumentos

    {
      "cluster": null,
      "controls": [],
      "dataset": "fertility",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "samesex"
      ],
      "y": "morekids"
    }

    Figuras call_6d793133

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.samesex Coefficient on samesex in the regression of morekids	0,0675253	0.06752525745019586	0,0019190.001919004619013623	0,0637641 – 0,0712865

    Diagnósticos

    • n_obs = 254.654254654
    • k = 22
    • r2 = 0,004834980.004834980694870095
    • df_resid = 254.652254652
    • se_type = "HC1"
    • n_clusters = —

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.3490531037503895,
          "ci_low": 0.34379649397495177,
          "coef": 0.34642479886267064,
          "p": 0,
          "se": 0.001340990148839299,
          "t": 258.33508110594283,
          "term": "Intercept"
        },
        {
          "ci_high": 0.07128645526665572,
          "ci_low": 0.06376405963373599,
          "coef": 0.06752525745019586,
          "p": 1.3882575556131429e-270,
          "se": 0.001919004619013623,
          "t": 35.1876471693456,
          "term": "samesex"
        }
      ]
    }

    Digests dos dados

    fertility c7a28c42496c51fa4ea223123cad6242aaa393634242d66e383c351dd0f471ef

    sha256 do resultado

    8703db0f2701b8ed2f001b80bb965c3d4b4246124823f0698f024c986cf25b59

    Resultado mostrado ao modelo
20. 19Turno do modelo
    call_88393339gemini-3.8-flashFim: STOP16.456 ms16663 de entrada · 115 de saída · 131 de raciocínio · 12563 em cache

    Texto visível

    I will estimate the regression with demographic controls (`agem1`, `blackm`, `hispm`, `othracem`, `boy1st`, `boy2nd`) using `infer.ols`.

    Resumo do raciocínio, como declarado pelo modelo

    **My Analytical Journey: Deconstructing the Impact of Same-Sex Siblings on Fertility Decisions** My current focus is on dissecting the probabilistic outcomes of having additional children, specifically examining the influence of having a same-sex sibling. I'm beginning by exploring a simplified model, `infer.ols` without any control variables. In this baseline scenario, I've observed the following: * The **intercept**, which I'm interpreting as the baseline probability of desiring more children when the *samesex* variable (indicating whether the first two children are of the same sex) is zero, stands at approximately 0.346425. This translates to a 34.64% probability. This provides my initial anchor point – the expected outcome in the absence of this specific factor. * The **coefficient for `samesex`** is 0.0675253. This is a crucial piece of information. It suggests that, holding all else constant (or in this case, nothing else constant as there are no controls), the probability of having more children increases by 6.75 percentage points when the first two children are of the same sex, compared to when they are of different sexes. This is my initial, unvarnished estimate of the effect. However, I recognize that this unadulterated view, while informative, likely oversimplifies the complex interplay of factors influencing fertility decisions. To gain a more robust understanding, I need to introduce the confounding variables that could be driving this observed association. Therefore, my next critical step is to re-run the `infer.ols` analysis, this time incorporating the specific set of control variables that have been historically utilized in similar research by Angrist & Evans (1998). These controls are: `agem1` (mother's age at first birth), `blackm` (indicator for Black mother), `hispm` (indicator for Hispanic mother), `othracem` (indicator for other races), `boy1st` (indicator for first child being a boy), and `boy2nd` (indicator for second child being a boy). By introducing these, I aim to isolate the effect of `samesex` more effectively and reduce potential bias stemming from unobserved common causes.

    Chamadas pedidas

    • infer.ols

      {
        "controls": [
          "agem1",
          "blackm",
          "hispm",
          "othracem",
          "boy1st",
          "boy2nd"
        ],
        "dataset": "fertility",
        "se": "HC1",
        "x": [
          "samesex"
        ],
        "y": "morekids"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
21. 20Ferramenta
    infer.olsv0.1.0call_29f4fe9ddecidido pelo modeloreexecutável

    Motivo: I will estimate the regression with demographic controls (`agem1`, `blackm`, `hispm`, `othracem`, `boy1st`, `boy2nd`) using `infer.ols`.

    Argumentos

    {
      "cluster": null,
      "controls": [
        "agem1",
        "blackm",
        "hispm",
        "othracem",
        "boy1st",
        "boy2nd"
      ],
      "dataset": "fertility",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "samesex"
      ],
      "y": "morekids"
    }

    Figuras call_29f4fe9d

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.samesex Coefficient on samesex in the regression of morekids	0,0685552	0.06855515835874637	0,001901770.00190176724841178	0,0648277 – 0,0722826

    Diagnósticos

    • n_obs = 254.654254654
    • k = 88
    • r2 = 0,02440880.024408817251458603
    • df_resid = 254.646254646
    • se_type = "HC1"
    • n_clusters = —

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": -0.11268611687822477,
          "ci_low": -0.14625890599637598,
          "coef": -0.12947251143730037,
          "p": 1.3142510520235943e-51,
          "se": 0.008564603688450379,
          "t": -15.117163169137397,
          "term": "Intercept"
        },
        {
          "ci_high": 0.07228257138947632,
          "ci_low": 0.06482774532801643,
          "coef": 0.06855515835874637,
          "p": 7.717927101092256e-284,
          "se": 0.00190176724841178,
          "t": 36.0481328175142,
          "term": "samesex"
        },
        {
          "ci_high": 0.015939017810901,
          "ci_low": 0.014857220360930715,
          "coef": 0.015398119085915856,
          "p": 0,
          "se": 0.0002759724965824321,
          "t": 55.79584660283887,
          "term": "agem1"
        },
        {
          "ci_high": 0.10910736620107334,
          "ci_low": 0.0916919309033562,
          "coef": 0.10039964855221477,
          "p": 5.806534387858866e-113,
          "se": 0.004442773606383371,
          "t": 22.598416540505394,
          "term": "blackm"
        },
        {
          "ci_high": 0.15917892863991823,
          "ci_low": 0.14304662207984237,
          "coef": 0.1511127753598803,
          "p": 2.19736089601683e-294,
          "se": 0.004115440387791247,
          "t": 36.718494528111094,
          "term": "hispm"
        },
        {
          "ci_high": 0.03667043199743441,
          "ci_low": 0.01849032648975138,
          "coef": 0.027580379243592894,
          "p": 2.7377492300294766e-9,
          "se": 0.004637845194796062,
          "t": 5.946808935007085,
          "term": "othracem"
        },
        {
          "ci_high": -0.006902069747944045,
          "ci_low": -0.014357331580381886,
          "coef": -0.010629700664162965,
          "p": 2.2854294409640686e-8,
          "se": 0.0019018784159919278,
          "t": -5.589053734867182,
          "term": "boy1st"
        },
        {
          "ci_high": -0.006240953285495625,
          "ci_low": -0.013696274030841976,
          "coef": -0.0099686136581688,
          "p": 1.5947609083225318e-7,
          "se": 0.0019018934449998469,
          "t": -5.24141543490603,
          "term": "boy2nd"
        }
      ]
    }

    Digests dos dados

    fertility c7a28c42496c51fa4ea223123cad6242aaa393634242d66e383c351dd0f471ef

    sha256 do resultado

    41ba4fdbfac34a839c67223f433b34dfbe64e5d8aed32240788955108457c06f

    Resultado mostrado ao modelo
22. 21Turno do modelo
    call_4b5969fbgemini-3.8-flashFim: STOP10.990 ms17590 de entrada · 36 de saída · 751 de raciocínio · 15713 em cache

    Texto visível

    I will load the `reporting-numbers` skill to review the formatting and reporting rules.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Reporting Results** I'm trying to figure out the best way to present the impact of something, specifically thinking about percentage point effects and relative percentage increases. The core rule I'm grappling with is that every number I use in my answer must come directly from a tool call in this session or from the initial prompt. No mental math or deriving numbers indirectly is allowed. My first idea was to use `calc.combine` to get the percentage point effect. I saw coefficients like 0.0675 or 0.0686. However, this brings me back to the strict rule: if I'm calculating a ratio, difference, sum, product, percent change, or share of already computed figures, I *must* use `calc.combine`. Similarly, if I need to convert a log-point coefficient to a percentage or an effect in standard deviations, `core.scale_effect` is the tool. Then I started thinking about calculating the *relative* increase in percentage. My initial thought for this was to divide the coefficient by the intercept, or the treated group by the control group. I was specifically wondering if I could calculate this for the "having a third child" scenario. I identified `call_6d793133:coef.samesex` as a potential coefficient figure. The immediate hurdle here is understanding what counts as a "figure." When I looked at the output of `infer.ols`, I saw `coef.samesex` listed under `figures`, but the intercept wasn't there; it was in `tables.coefficients`. This raised a critical question: can `calc.combine` actually read values from `tables`? I reviewed the description of `calc.combine` and it explicitly states it takes a "Figure reference '<call_id>:<key>' from an earlier call in this run." This implies it *only* works with items listed under `figures`. I then investigated other tools, like `data.describe`, to see if they provided means as figures. `data.describe` only offered `n_rows` as a figure, with means residing in `tables.columns`. This further confirmed my suspicion that directly accessing means for a ratio calculation might be problematic if they aren't output as figures. At this point, I had a moment of realization: perhaps I didn't *need* to calculate a relative percentage increase at all if I could directly report the percentage points from the available figures. I then went back and reviewed the figures I had access to: * From `call_426a64a7`: `ate` with value 0.0675253, se, and ci. * From `call_489903a6`: `ate` with value 0.0685552, se, and ci. * From `call_6d793133`: `coef.samesex` with value 0.0675253, se, and ci. * From `call_29f4fe9d`: `coef.samesex` with value 0.0685552, se, and ci. All these values, represented as `ate` or `coef.samesex`, are directly available as figures. This means I can likely report the percentage point effect directly without needing to perform further calculations or worrying about accessing intercepts from tables. My final step in this line of thought was to consider if there was a more streamlined way to handle reporting numbers in general. This led me to the idea of loading the `reporting-numbers` skill, hoping it might simplify this process or offer additional functionalities related to presenting numerical results effectively and compliantly with the rules.

    Chamadas pedidas

    • load_skill (meta-ferramenta)

      {
        "name": "reporting-numbers"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
23. 22Habilidade
    reporting-numbersTexto da habilidade
24. 23Turno do modelo
    call_9ff79eefgemini-3.8-flashFim: STOP5.137 ms18813 de entrada · 76 de saída · 233 de raciocínio · 15934 em cache

    Texto visível

    I will calculate the effect as a percentage of the control group baseline mean using `core.scale_effect`.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Scaling Effect Sizes** Okay, so I've been looking at this `core.scale_effect` function and realized it's quite versatile. It seems I can use it to transform a raw coefficient into either a percentage of the baseline or standard deviation units. This is a crucial step for interpreting effect sizes in a meaningful way. My immediate next thought was to dive into the specifics of `core.scale_effect`. I checked the documentation and saw the transformations it supports: `log_points_to_percent` (calculated as 100 * (exp(b) - 1)), `sd_units` (calculated as b / sd), and `percent_of_baseline` (calculated as 100 * b / baseline). This last option, `percent_of_baseline`, is where my attention really focused. To use it effectively, I immediately recognized that I need a clear definition of what "baseline" means in this context. Is it a specific control group mean, or the overall mean of the outcome variable? I then recalled looking at `infer.ols` results earlier. Without any controls included, the intercept — which represents the control mean — was reported as 0.346425. Alternatively, I also had access to the overall mean of the data, which I'd previously obtained using `data.describe`, and that value was 0.380563. The critical question then became: which of these baseline values should I use with `core.scale_effect`? I needed to confirm if the `baseline` parameter in `core.scale_effect` could accept a specific numerical value. A quick check of its specification confirmed that it can: `baseline: {type: NUMBER}`. This is excellent news, as it means I have direct control over which baseline I want to reference. Therefore, my next logical step is to test the `percent_of_baseline` transformation using the control mean of 0.346425 as my defined baseline. I'll proceed with calling `core.scale_effect` with this specific baseline value to see how the effect sizes translate.

    Chamadas pedidas

    • core.scale_effect

      {
        "baseline": 0.346425,
        "coefficient": "call_6d793133:coef.samesex",
        "transform": "percent_of_baseline"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
25. 24Ferramenta
    core.scale_effectv0.1.0call_12cd3626decidido pelo modeloreexecutável

    Motivo: I will calculate the effect as a percentage of the control group baseline mean using `core.scale_effect`.

    Argumentos

    {
      "baseline": 0.346425,
      "coefficient": "call_6d793133:coef.samesex",
      "sd": null,
      "transform": "percent_of_baseline"
    }

    Figuras call_12cd3626

    Chave	Valor	Valor bruto	Erro-padrão	IC
    effect Effect as a percent of the baseline mean: Coefficient on samesex in the regression of morekids	19,492 % of baseline	19.49202784158068	0,5539450.5539451884285554	18,4063 – 20,5777

    Diagnósticos

    • transform = "percent_of_baseline"
    • baseline = 0,3464250.346425

    Digests dos dados

    sha256 do resultado

    4452a243026f8ce407230ec9a123fbb3901af81acb5c8656a7ad192608f0fa0d

    Resultado mostrado ao modelo
26. 25Turno do modelo
    call_e8cd93e6gemini-3.8-flashFim: STOP16.669 ms19230 de entrada · 568 de saída · 1346 de raciocínio · 15959 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on the Effect of Same-Sex First Two Children on Subsequent Births** Alright, so I've been digging into this analysis, and the core finding is quite striking: it seems parents whose first two children are of the same sex *are* indeed more likely to have another child. I've just processed the output from `core.scale_effect`, and it's showing a significant increase of `19.492% of baseline`. Let me just break down the exact numbers I'm seeing and how I'm arriving at this conclusion. First, I looked at the simple model, the one without any demographic controls. Here, the estimated coefficient (or Average Treatment Effect, ATE, in this context) for having the first two children be the same sex is `0.0675253`. This comes with a standard error of `0.001919`, and a 95% confidence interval of `[0.0637641, 0.0712865]`. This coefficient represents the absolute increase in the probability of having a third child. Now, to understand this in relation to the baseline behavior, I examined the intercept. The intercept, which in this model represents the baseline probability for children of different sex (effectively, the reference group), is `0.346425`. When I express the effect of same-sex first two children as a percentage of this baseline, I get that `19.492%` figure, with a confidence interval of `[18.4063, 20.5777]`. This tells me the relative increase in the likelihood of having a third child. Then, I ran the analysis with demographic controls. I included variables like `agem1` (mother's age at first birth), `blackm`, `hispm`, `othracem` (race indicators), and `boy1st`, `boy2nd` (indicators for the sex of the first two children). With these controls, the coefficient (ATE) shifts slightly to `0.0685552`, with a standard error of `0.00190177` and a 95% confidence interval of `[0.0648277, 0.0722826]`. So, even after accounting for these demographic factors, the effect remains robust and statistically significant. The question specifically asks about married women aged 21-35 with two or more children in the 1980 Census, and this is precisely the population I'm analyzing. The question wants to know *how much more likely* a mother is to have a third child under these conditions, and my results are directly addressing that. I've also cross-referenced these numbers with the Portuguese summary provided, and they align perfectly. For instance, the simple estimate of `0.0675` (or 6.75 percentage points) and the relative increase of `19.492%` are exactly what I've calculated. The controlled estimate of `0.0686` (or 6.86 percentage points) also matches. I've also thoroughly checked all the figure references that need to be submitted: `call_6d793133:coef.samesex`, `call_12cd3626:effect`, `call_29f4fe9d:coef.samesex`, and `call_1234868f:n_rows`. I've also noted that `call_426a64a7:ate` and `call_489903a6:ate` provide the same core coefficients, but the references I've chosen are clean and directly tied to the calculated effects and controls. Finally, I've considered the caveats that are crucial for interpretation. The causal identification relies on the near-random biological assignment of sex for the first two children. The sample is specific to married women aged 21-35 with at least two children in the 1980 US Census, meaning external validity to other demographics, time periods, or institutional settings is limited. And importantly, the underlying mechanism likely reflects a parental preference for sex diversity, meaning parents might be trying to balance their children's sexes. Everything seems to be verified and grounded in the provided outputs. I'm ready to submit my answer.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Sim. Mães cujos dois primeiros filhos são do mesmo sexo têm uma probabilidade significativamente maior de ter um terceiro filho.\n\nNa amostra do Censo de 1980 (254.654 observações de mulheres casadas de 21 a 35 anos com dois ou mais filhos):\n\n1. **Estimativa sem controles adicionais**:\n   - Ter os dois primeiros filhos do mesmo sexo (`samesex`) aumenta a probabilidade de ter mais filhos (`morekids`) em **0,0675** (aproximadamente 6,75 pontos percentuais), com erro-padrão de **0,0019** e intervalo de confiança de 95% de **[0,0638; 0,0713]**.\n   - Frente à taxa base do grupo de controle cujos dois primeiros filhos têm sexos diferentes (**0,3464** ou 34,64%), isso representa um aumento relativo de **19,492%** (IC 95%: **[18,4063%; 20,5777%]**).\n\n2. **Estimativa com controles demográficos** (idade da mãe, raça e sexo de cada um dos dois primeiros filhos):\n   - O acréscimo estimado na probabilidade é de **0,0686** (aproximadamente 6,86 pontos percentuais), com erro-padrão de **0,0019** e intervalo de confiança de 95% de **[0,0648; 0,0723]**.\n\nEssa relação decorre da preferência parental por ter filhos de ambos os sexos, servindo como a primeira etapa quase-experimental clássica de Angrist e Evans (1998).",
        "caveats": [
          "A identificação causal apoia-se na quase-aleatoriedade biológica da determinação do sexo dos dois primeiros filhos.",
          "A validade externa restringe-se a mães casadas de 21 a 35 anos com dois ou mais filhos nos Estados Unidos em 1980, podendo variar em outros contextos culturais, temporais ou demográficos."
        ],
        "figure_refs": [
          "call_1234868f:n_rows",
          "call_6d793133:coef.samesex",
          "call_12cd3626:effect",
          "call_29f4fe9d:coef.samesex"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
27. 26Guarda
    resposta recusada

    Números encontrados

    1980 · 254.654 · 21 · 35 · 1 · 0,0675 · 6,75 · 0,0019 · 95% · 0,0638 · 0,0713 · 0,3464 · 34,64% · 19,492% · 95% · 18,4063% · 20,5777% · 2 · 0,0686 · 6,86 · 0,0019 · 95% · 0,0648 · 0,0723 · 1998 · 21 · 35 · 1980

    Sem lastro

    6,75 · 34,64% · 6,86

    Não citados

    —

    0 nova(s) tentativa(s) restante(s)
28. 27Turno do modelo
    call_ea10ea16gemini-3.8-flashFim: STOP13.603 ms21251 de entrada · 547 de saída · 899 de raciocínio · 19518 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Number Handling** Okay, I need to be exceptionally precise with the numbers in this analysis. The system has a strict rule: it's rejecting anything that looks like a simple mental arithmetic conversion, like 6,75, 34,64%, or 6,86. The directive is clear – numbers must *either* originate directly from tool calls within this specific run *or* be explicitly stated in the original question. If I need to perform any arithmetic on figures, I must use the `calc.combine` tool and ensure I cite the source figure correctly. This means I'm restricted to using the exact numerical values returned by the tools. I can't take a percentage like "6.75%" and just present it as 0.0675, or use a figure like "34.64%" in my output without it being a direct output from a tool. The numbers I *must* strictly adhere to are the raw outputs, such as 0,0675253, 0,346425, 0,0685552, 19,492, and 254654, among others. No more intuitive percentage conversions that aren't directly supported by the tool outputs. So, my next step is to meticulously check every single number appearing in the text against the outputs from the tool calls I've made. I need to ensure that only numbers directly derived from these tool calls or the initial question are permitted. Let's break down the numbers from the tool calls I have: * From `call_1234868f`: `n_rows` is reported as 254654. This is a direct, allowed number. * From `call_6d793133`: * The `coef.samesex` value is 0.0675253. This is an exact, allowed number. * The standard error (`se`) for this coefficient is 0.001919. Also an allowed number. * The confidence interval (`ci`) lower bound is 0.0637641, and the upper bound is 0.0712865. Both are directly from the tool. * The `intercept` coefficient is 0.346425. This is another direct output. * From `call_12cd3626`: * The `effect` value is 19.492. This is given, and I've noted it's presented as a percentage (19,492%), which is fine if that's how the tool presented it. * Its standard error (`se`) is 0.553945. * Its confidence interval (`ci`) ranges from 18.4063% to 20.5777%. Again, these percentage figures are from the tool. * From `call_29f4fe9d`: * The `coef.samesex` value is 0.0685552. This is a direct, allowed number. * The standard error (`se`) for this coefficient is 0.00190177. Allowed. * The confidence interval (`ci`) lower bound is 0.0648277, and the upper bound is 0.0722826. These are directly from the tool outputs. Now, let's cross-reference these with any numbers that came directly from the question: * 1980 * 21 * 35 And let's list *all* the numbers I've confirmed are allowed based on these criteria: * 1980 (from question) * 21 (from question) * 35 (from question) * 254654 (from `call_1234868f:n_rows`) * 0,0675253 (from `call_6d793133:coef.samesex`) * 0,001919 (from `call_6d793133:coef.samesex`) * 0,0637641 (from `call_6d793133:coef.samesex`) * 0,0712865 (from `call_6d793133:coef.samesex`) * 0,346425 (from `call_6d793133` table cell Intercept coef) * 19,492% (from `call_12cd3626:effect`) - the percentage format is as reported by the tool. * 18,4063% (from `call_12cd3626:effect`) * 20,5777% (from `call_12cd3626:effect`) * 0,0685552 (from `call_29f4fe9d:coef.samesex`) * 0,00190177 (from `call_29f4fe9d:coef.samesex`) * 0,0648277 (from `call_29f4fe9d:coef.samesex`) * 0,0722826 (from `call_29f4fe9d:coef.samesex`) * 95% (this is a standard confidence level, typically understood and allowed without needing a specific tool call). I've successfully identified all the allowed numerical values and, importantly, confirmed that any numbers that were derived through mental arithmetic or weren't directly from the tools or question have been excluded. This meticulous verification ensures I adhere to the system's constraints. Now, I can proceed with generating the answer, using *only* these validated numbers and calling `submit_answer`.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Sim. Mães cujos dois primeiros filhos são do mesmo sexo têm maior probabilidade de ter um terceiro filho.\n\nNa amostra de 254654 mulheres casadas de 21 a 35 anos com dois ou mais filhos no Censo de 1980:\n\n1. **Estimativa simples (sem covariadas adicionais)**:\n   - Ter os dois primeiros filhos do mesmo sexo aumenta a probabilidade de ter mais filhos em **0,0675253** (erro-padrão de **0,001919**; IC 95%: **[0,0637641; 0,0712865]**).\n   - Em relação à probabilidade média do grupo de controle (filhos de sexos distintos, cujo intercepto é **0,346425**), esse aumento equivale a **19,492%** da média base (IC 95%: **[18,4063%; 20,5777%]**).\n\n2. **Estimativa com controles demográficos** (idade da mãe, raça e sexo do primeiro e do segundo filho):\n   - O aumento estimado na probabilidade é de **0,0685552** (erro-padrão de **0,00190177**; IC 95%: **[0,0648277; 0,0722826]**).\n\nEsse efeito reflete a preferência dos pais por variedade na composição por sexo dos filhos, servindo como o clássico instrumento quase-experimental de Angrist e Evans (1998).",
        "caveats": [
          "A identificação causal apoia-se na quase-aleatoriedade biológica do sexo dos filhos ao nascimento.",
          "Os resultados aplicam-se à amostra de mulheres casadas de 21 a 35 anos com dois ou mais filhos no Censo dos EUA de 1980, podendo não se generalizar diretamente a outros contextos institucionais ou períodos."
        ],
        "figure_refs": [
          "call_1234868f:n_rows",
          "call_6d793133:coef.samesex",
          "call_12cd3626:effect",
          "call_29f4fe9d:coef.samesex"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
29. 28Guarda
    resposta aceita

    Números encontrados

    254654 · 21 · 35 · 1980 · 1 · 0,0675253 · 0,001919 · 95% · 0,0637641 · 0,0712865 · 0,346425 · 19,492% · 95% · 18,4063% · 20,5777% · 2 · 0,0685552 · 0,00190177 · 95% · 0,0648277 · 0,0722826 · 1998 · 21 · 35 · 1980

    Sem lastro

    —

    Não citados

    —

    0 nova(s) tentativa(s) restante(s)
30. 29Resposta

    Sim. Mães cujos dois primeiros filhos são do mesmo sexo têm maior probabilidade de ter um terceiro filho. Na amostra de 254654 mulheres casadas de 21 a 35 anos com dois ou mais filhos no Censo de 1980: 1. **Estimativa simples (sem covariadas adicionais)**: - Ter os dois primeiros filhos do mesmo sexo aumenta a probabilidade de ter mais filhos em **0,0675253** (erro-padrão de **0,001919**; IC 95%: **[0,0637641; 0,0712865]**). - Em relação à probabilidade média do grupo de controle (filhos de sexos distintos, cujo intercepto é **0,346425**), esse aumento equivale a **19,492%** da média base (IC 95%: **[18,4063%; 20,5777%]**). 2. **Estimativa com controles demográficos** (idade da mãe, raça e sexo do primeiro e do segundo filho): - O aumento estimado na probabilidade é de **0,0685552** (erro-padrão de **0,00190177**; IC 95%: **[0,0648277; 0,0722826]**). Esse efeito reflete a preferência dos pais por variedade na composição por sexo dos filhos, servindo como o clássico instrumento quase-experimental de Angrist e Evans (1998). Ressalvas: A identificação causal apoia-se na quase-aleatoriedade biológica do sexo dos filhos ao nascimento. Os resultados aplicam-se à amostra de mulheres casadas de 21 a 35 anos com dois ou mais filhos no Censo dos EUA de 1980, podendo não se generalizar diretamente a outros contextos institucionais ou períodos.

    • A identificação causal apoia-se na quase-aleatoriedade biológica do sexo dos filhos ao nascimento.
    • Os resultados aplicam-se à amostra de mulheres casadas de 21 a 35 anos com dois ou mais filhos no Censo dos EUA de 1980, podendo não se generalizar diretamente a outros contextos institucionais ou períodos.

    Figuras citadas: call_1234868f:n_rows, call_6d793133:coef.samesex, call_12cd3626:effect, call_29f4fe9d:coef.samesex · Parada: answered

    Figura da resposta: call_6d793133:coef.samesex = 0,06752530.06752525745019586 (se 0,0019190.001919004619013623)
31. 30Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
32. 31Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_6d793133:coef.samesex (infer.ols)
33. 32Pontuação
    #2uma entrepassouobrigatóriatool_called(infer.ols): ok | tool_called(infer.ate): answer call is infer.ols | tool_called(infer.neyman): answer call is infer.ols
34. 33Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'morekids'
35. 34Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x[0] = 'samesex'
36. 35Pontuação
    #5argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.ate
37. 36Pontuação
    #6argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.ate
38. 37Pontuação
    #7argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.neyman
39. 38Pontuação
    #8argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.neyman
40. 39Pontuação
    #9argumento igualpassouregistradainfer.ols.y = 'morekids'
41. 40Pontuação
    #10argumento igualpassouregistradainfer.ols.x = ['samesex']
42. 41Pontuação
    #11argumento igualpassouregistradainfer.ols.controls = []
43. 42Pontuação
    #12diagnósticopassouregistrada254654 vs 254654
44. 43Pontuação
    #13figura da resposta próximapassouregistrada0.06752525745 vs 0.06752525745015382
45. 44Pontuação
    #14figura da resposta próximafalhouregistrada0.001919004619 vs 0.0019197420674756107
46. 45Pontuação
    #15figura da resposta próximapassouregistrada0.06752525745 vs 0.0675
47. 46Pontuação
    #16figura da resposta próximapassouregistrada0.001919004619 vs 0.0019

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)

Downloads desta tarefa