Tarefa paper_repro.angrist_evans_1998.tab7_c5_weeks.agent

Veredito

Pergunta feita ao analista

Angrist and Evans (1998), 'Children and Their Parents' Labor Supply', Table 7, column (5): in the 1980 Census sample of married women aged 21-35 with two or more children, estimate by two-stage least squares the effect of having more than 2 children on the mother's weeks worked in 1979, using as the instrument the indicator that the first two children are of the same sex. Include as covariates the mother's age and indicators for a first-born boy, a second-born boy, and Black, Hispanic and other race; the paper also includes the age at first birth, which this file does not have. Report the coefficient on having more than 2 children.

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Trajetória

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

1. 0Início

   Angrist and Evans (1998), 'Children and Their Parents' Labor Supply', Table 7, column (5): in the 1980 Census sample of married women aged 21-35 with two or more children, estimate by two-stage least squares the effect of having more than 2 children on the mother's weeks worked in 1979, using as the instrument the indicator that the first two children are of the same sex. Include as covariates the mother's age and indicators for a first-born boy, a second-born boy, and Black, Hispanic and other race; the paper also includes the age at first birth, which this file does not have. Report the coefficient on having more than 2 children.

   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_8ed293e5gemini-3.8-flashFim: STOP9.712 ms14489 de entrada · 141 de saída · 389 de raciocínio · 0 em cache

    Texto visível

    I will estimate the effect of having more than two children (`morekids`) on the mother's weeks worked (`weeksm1`) by two-stage least squares, using the same-sex sibling instrument (`samesex`) and controlling for maternal age, child gender indicators, and race indicators.

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process for Replicating Angrist & Evans (1998), Table 7, Column (5)** My primary objective is to precisely replicate the Two-Stage Least Squares (2SLS) specification presented in Angrist and Evans' (1998) Table 7, specifically column (5). I note that a direct replication isn't perfectly possible as the original specification likely included "age at first birth," which is absent in the provided dataset. However, I can proceed with the available variables to get as close as possible. Here's how I've broken down the task and my reasoning: 1. **Identify the Core Components of the 2SLS Model:** * **Dataset:** I'll be working with the `fertility` dataset. * **Outcome Variable (y):** My dependent variable of interest is `weeksm1`, representing the mother's weeks worked in 1979. This is what I aim to explain. * **Endogenous Regressor (endog):** The key variable whose causal effect I want to estimate, and which is potentially correlated with the error term, is `morekids` (mother having more than 2 children). * **Instrumental Variable (instruments):** To address the endogeneity of `morekids`, I need an instrument. The appropriate instrument here is `samesex` (whether the first two children are of the same sex). This variable is assumed to influence `morekids` but not directly affect `weeksm1` except through its effect on `morekids`. * **Exogenous Covariates (exog):** To control for other factors that might influence both the endogenous regressor and the outcome, I need to include the specified exogenous control variables. These are: `agem1` (mother's age), `boy1st` (first-born was a boy), `boy2nd` (second-born was a boy), `blackm` (mother is Black), `hispm` (mother is Hispanic), and `othracem` (mother is of other race). 2. **Consult the `infer__iv_2sls` Tool Parameters:** Before executing the command, I'm reviewing the expected parameters for the `infer__iv_2sls` function. I need to map my identified variables to these parameters: * `dataset`: This clearly maps to `"fertility"`. * `y`: This maps to `"weeksm1"`. * `endog`: This maps to the list `["morekids"]`. * `instruments`: This maps to the list `["samesex"]`. * `exog`: This maps to the list `["agem1", "boy1st", "boy2nd", "blackm", "hispm", "othracem"]`. 3. **Consider Standard Error Options (`se`):** The `infer__iv_2sls` tool offers a `se` parameter. The options are `"classical"` or `"HC1"` (with `"HC1"` being the default). I need to decide which is most appropriate. Angrist and Evans (1998) are known for reporting standard errors, and the standard practice in many econometric studies of this type is to use robust standard errors to account for potential heteroskedasticity. The `HC1` option provides a robust standard error estimate. I will tentatively plan to use the default `"HC1"` as it aligns with robust estimation practices, and I can always check the results against a `"classical"` specification if needed for comparison or specific replication requirements. 4. **Construct the Command:** Based on the parameter mapping and the decision on standard errors, I can now formulate the precise command to execute the 2SLS estimation. This systematic approach ensures that I'm correctly specifying the model according to the prompt's requirements and the available tool's capabilities, while also paying attention to potential nuances like standard error estimation.

    Chamadas pedidas

    • infer.iv_2sls

      {
        "dataset": "fertility",
        "endog": [
          "morekids"
        ],
        "exog": [
          "agem1",
          "boy1st",
          "boy2nd",
          "blackm",
          "hispm",
          "othracem"
        ],
        "instruments": [
          "samesex"
        ],
        "y": "weeksm1"
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
13. 12Ferramenta
    infer.iv_2slsv0.1.0call_15779e33decidido pelo modeloreexecutável

    Motivo: I will estimate the effect of having more than two children (`morekids`) on the mother's weeks worked (`weeksm1`) by two-stage least squares, using the same-sex sibling instrument (`samesex`) and controlling for maternal age, child gender indicators, and race indicators.

    Argumentos

    {
      "cluster": null,
      "dataset": "fertility",
      "endog": [
        "morekids"
      ],
      "exog": [
        "agem1",
        "boy1st",
        "boy2nd",
        "blackm",
        "hispm",
        "othracem"
      ],
      "instruments": [
        "samesex"
      ],
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "y": "weeksm1"
    }

    Figuras call_15779e33

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.morekids 2SLS effect of morekids on weeksm1	-5,74641	-5.746409761250555	1,237371.237366881074018	-8,17162 – -3,3212

    Diagnósticos

    • first_stage_F.morekids = 1.299,471299.467879629763
    • partial_r2.morekids = 0,005075080.0050750844496005865
    • weak_instrument = false
    • n_obs = 254.654254654
    • se_type = "HC1"
    • n_clusters = —

    Tabelas (3)

    {
      "coefficients": [
        {
          "ci_high": -3.927306893622839,
          "ci_low": -5.45932162213255,
          "coef": -4.693314257877694,
          "p": 3.2642715503317284e-33,
          "se": 0.3908254076948471,
          "t": -12.008723500244363,
          "term": "Intercept"
        },
        {
          "ci_high": 0.8746665649658196,
          "ci_low": 0.786344288858114,
          "coef": 0.8305054269119668,
          "p": 1.2454052468804427e-296,
          "se": 0.02253149981260909,
          "t": 36.8597489656325,
          "term": "agem1"
        },
        {
          "ci_high": 0.15520278910947324,
          "ci_low": -0.18003821997979472,
          "coef": -0.012417715435160728,
          "p": 0.8845535827347312,
          "se": 0.08552183057717556,
          "t": -0.14519936431850444,
          "term": "boy1st"
        },
        {
          "ci_high": -0.00032221120507430756,
          "ci_low": -0.3349625445017042,
          "coef": -0.16764237785338926,
          "p": 0.049560449632849474,
          "se": 0.08536859486920129,
          "t": -1.9637476534578662,
          "term": "boy2nd"
        },
        {
          "ci_high": 12.06768712820807,
          "ci_low": 11.161164212402811,
          "coef": 11.61442567030544,
          "p": 0,
          "se": 0.23125899611876088,
          "t": 50.22258967318599,
          "term": "blackm"
        },
        {
          "ci_high": 0.9005152244141729,
          "ci_low": -0.11767156328030948,
          "coef": 0.3914218305669317,
          "p": 0.1318256975679594,
          "se": 0.25974506576532597,
          "t": 1.5069461643616855,
          "term": "hispm"
        },
        {
          "ci_high": 2.543097130899236,
          "ci_low": 1.7160945258383116,
          "coef": 2.1295958283687737,
          "p": 5.923928919303805e-24,
          "se": 0.21097292622117747,
          "t": 10.094166424634844,
          "term": "othracem"
        },
        {
          "ci_high": -3.321203711372093,
          "ci_low": -8.171615811129017,
          "coef": -5.746409761250555,
          "p": 0.0000034179356204371333,
          "se": 1.237366881074018,
          "t": -4.644063009236798,
          "term": "morekids"
        }
      ],
      "first_stage": [
        {
          "ci_high": 0.07228257138947543,
          "ci_low": 0.06482774532801731,
          "coef": 0.06855515835874637,
          "endog": "morekids",
          "instrument": "samesex",
          "p": 7.717927098715722e-284,
          "se": 0.0019017672484113273,
          "t": 36.048132817522784
        }
      ],
      "reduced_form": [
        {
          "ci_high": -0.2260848480788981,
          "ci_low": -0.5618072142745139,
          "coef": -0.39394603117670596,
          "instrument": "samesex",
          "p": 0.000004231503490017462,
          "se": 0.08564462743012631,
          "t": -4.599775175601169
        }
      ]
    }

    Digests dos dados

    fertility c7a28c42496c51fa4ea223123cad6242aaa393634242d66e383c351dd0f471ef

    sha256 do resultado

    25d0c61b21ea95dd0f74089de494ee4017447a3a2b0182010b1623891f471d52

    Resultado mostrado ao modelo
14. 13Turno do modelo
    call_4758f59dgemini-3.8-flashFim: STOP19.458 ms15853 de entrada · 378 de saída · 1765 de raciocínio · 0 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Estimating the Impact of More Children on Maternal Labor Supply** Alright, let's break down this analysis. I've been asked to examine the effect of having more than two children on a mother's weeks worked, using a specific dataset and methodology inspired by Angrist and Evans (1998). My task is to extract a key coefficient, its associated uncertainty, the research design that identifies it, and any limitations that might affect the conclusions. First, I need to identify the core estimate. The output from `coef.morekids` directly provides this: a coefficient of -5.74641. This suggests that, on average, having more than two children is associated with a decrease of approximately 5.75 weeks worked. Next, the uncertainty surrounding this estimate is crucial. The standard error is reported as 1.23737, which is essential for constructing confidence intervals and assessing statistical significance. The 95% confidence interval is given as [-8.17162, -3.3212]. This interval is entirely negative, indicating a statistically significant negative effect at the conventional 5% level. Now, I need to understand the research design that allows us to draw a causal inference here. The prompt clearly states the method: two-stage least squares (2SLS). The instrumental variable (IV) used is an indicator for whether the first two children are of the same sex. This is a clever strategy because sibling sex composition is plausibly exogenous and correlated with the decision to have a third child, but it should not directly affect a mother's labor supply otherwise. The strength of this instrument is also important; the first-stage Wald F-statistic is a key diagnostic for this. A value of 1299.46787962976 indicates a very strong first stage, which is excellent for the validity of the 2SLS estimate. The first-stage coefficient itself, 0.0685552, tells me how strongly the instrument (same-sex first two children) predicts having more than two children. I also need to be mindful of the specific covariates included, as these define the model. The prompt mentions mother's age and indicators for first-born boy, second-born boy, and race (Black, Hispanic, other). These are controlled for in the estimation. However, it's noted that the original Angrist and Evans (1998) specification in Table 7, column (5) also included the age at first birth. My current dataset does *not* have this variable. This is a potential limitation because age at first birth could also influence both fertility decisions and labor supply. Finally, I must consider the nuances of the interpretation of the 2SLS estimate. The prompt guides me to remember that this estimate represents a Local Average Treatment Effect (LATE). This means the coefficient tells me the effect of having more than two children *specifically for those individuals whose decision to have a third child is influenced by the sex composition of their first two children* (the "compliers"). It doesn't necessarily reflect the average effect for all mothers in the sample. Given these points, I can now formulate the structured answer required. I need to present the estimate with its uncertainty, describe the design, and outline the caveats. I also need to ensure I adhere to formatting rules, particularly regarding the language (English, as the prompt was in English) and writing numbers exactly as they were provided by the tools. I'll reference the specific tool output using `figure_refs` and list the caveats identified. The instruction about Portuguese decimal commas is a reminder to be context-aware, but since the question is in English, I will use standard English decimal notation. I will use the exact numbers as provided by the tools, without any rounding beyond what the tool output itself represents.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Using two-stage least squares (2SLS) on the 1980 Census sample of married women aged 21-35 with two or more children (n = 254654), instrumenting the indicator for having more than two children (`morekids`) with the same-sex sibling indicator (`samesex`) and controlling for maternal age (`agem1`), child sex (`boy1st`, `boy2nd`), and race indicators (`blackm`, `hispm`, `othracem`), the estimated effect of having more than two children on the mother's weeks worked in 1979 (`weeksm1`) is -5.74641 weeks (robust standard error 1.23737; 95% CI [-8.17162, -3.3212]). The first-stage excluded instrument is strong, with a coefficient of 0.0685552 (standard error 0.00190177) and a first-stage F-statistic of 1299.46787962976.",
        "caveats": [
          "The dataset omits mother's age at first birth, which is included in the full specification of Angrist and Evans (1998, Table 7, column 5).",
          "The 2SLS estimate identifies a Local Average Treatment Effect (LATE) for compliers—mothers whose decision to have a third child is induced by having two children of the same sex—which may not generalize to the wider population."
        ],
        "figure_refs": [
          "call_15779e33:coef.morekids"
        ]
      }

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

    Números encontrados

    1980 · 21 · 35 · 254654 · 1979 · -5.74641 · 1.23737 · 95% · -8.17162 · -3.3212 · 0.0685552 · 0.00190177 · 1299.46787962976 · 1998 · 7 · 5

    Sem lastro

    —

    Não citados

    —

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

    Using two-stage least squares (2SLS) on the 1980 Census sample of married women aged 21-35 with two or more children (n = 254654), instrumenting the indicator for having more than two children (`morekids`) with the same-sex sibling indicator (`samesex`) and controlling for maternal age (`agem1`), child sex (`boy1st`, `boy2nd`), and race indicators (`blackm`, `hispm`, `othracem`), the estimated effect of having more than two children on the mother's weeks worked in 1979 (`weeksm1`) is -5.74641 weeks (robust standard error 1.23737; 95% CI [-8.17162, -3.3212]). The first-stage excluded instrument is strong, with a coefficient of 0.0685552 (standard error 0.00190177) and a first-stage F-statistic of 1299.46787962976. Ressalvas: The dataset omits mother's age at first birth, which is included in the full specification of Angrist and Evans (1998, Table 7, column 5). The 2SLS estimate identifies a Local Average Treatment Effect (LATE) for compliers—mothers whose decision to have a third child is induced by having two children of the same sex—which may not generalize to the wider population.

    • The dataset omits mother's age at first birth, which is included in the full specification of Angrist and Evans (1998, Table 7, column 5).
    • The 2SLS estimate identifies a Local Average Treatment Effect (LATE) for compliers—mothers whose decision to have a third child is induced by having two children of the same sex—which may not generalize to the wider population.

    Figuras citadas: call_15779e33:coef.morekids · Parada: answered

    Figura da resposta: call_15779e33:coef.morekids = -5,74641-5.746409761250555 (se 1,237371.237366881074018)
17. 16Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
18. 17Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_15779e33:coef.morekids (infer.iv_2sls)
19. 18Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.iv_2sls
20. 19Pontuação
    #3argumento igualpassouobrigatóriainfer.iv_2sls.y = 'weeksm1'
21. 20Pontuação
    #4argumento igualpassouobrigatóriainfer.iv_2sls.endog = ['morekids']
22. 21Pontuação
    #5argumento igualpassouobrigatóriainfer.iv_2sls.instruments = ['samesex']
23. 22Pontuação
    #6argumento igualpassouobrigatóriainfer.iv_2sls.exog = ['agem1', 'boy1st', 'boy2nd', 'blackm', 'hispm', 'othracem']
24. 23Pontuação
    #7diagnósticopassouobrigatória254654 vs 254654
25. 24Pontuação
    #8figura da resposta próximapassouobrigatória-5.746409761 vs -5.746409761252153
26. 25Pontuação
    #9figura da resposta próximafalhouregistrada1.237366881 vs 1.2372527665238475
27. 26Pontuação
    #10figura da resposta próximafalhouregistrada-5.746409761 vs -5.4
28. 27Pontuação
    #11figura da resposta próximafalhouregistrada1.237366881 vs 1.2

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