Tarefa crb_papers.q032

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Pergunta feita ao analista

Did states that adopted direct primaries in 1908 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1906 to 1908 relative to states that had not yet adopted?

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

   Did states that adopted direct primaries in 1908 experience an increase in the log of the state-average daily candidate newspaper advertisements (week before the election) from 1906 to 1908 relative to states that had not yet adopted?

   ds_0017 (agent)

   | Variable Name | Description | | --- | --- | | (index) | Row index from the CSV export; carries no substantive information. | | state | Two-letter U.S. state postal abbreviation identifying the state associated with the observation. | | year | Calendar year in which the state held the referenced general election. Most elections occurred in November of even-numbered years, though some states held major general elections in other months or in odd-numbered years; the value here reflects the actual calendar year of that election. | | ln_x_cand_all | Log-transformed average number of newspaper advertisements per day placed by or on behalf of individual candidates in the seven days preceding the general election; constructed as ln(ads per newspaper-day + 1). Source counts come from hand-coding full newspaper issues, then averaging ad counts to the newspaper-year and aggregating to a state-year mean. Included offices typically comprise statewide executive posts, state legislatures, U.S. House, judicial, and many local offices; advertisements focused solely on parties are tracked separately, and candidate ads for U.S. Senate and the presidency are excluded from this summary measure. | | prim_sw | Indicator for whether, in that year, the state used state-administered direct primary elections to nominate candidates for statewide offices (1 = direct primaries in use; 0 = nomination by caucus/convention or other non-primary procedures). Reflects the institutional status in each state-year during the late 19th and early 20th centuries. | Dataset Context: This state-year panel assembles historical evidence on campaign communications during an era when newspapers were the dominant mass medium. Researchers reviewed issues from 90 local newspapers across 33 mostly non-Southern states between 1880 and 1930, focusing on the week immediately preceding each state’s general election. Political advertisements were hand-coded at the ad level, distinguishing candidate-focused content from party-only notices. To make newspapers comparable despite different publication schedules and occasional missing issues, counts were normalized to advertisements per newspaper-day, averaged to the newspaper-year, and then aggregated to a state-year mean. The offices captured in the primary advertising measure are those commonly elected across states throughout the period: statewide executives (e.g., governor, attorney general), state legislatures, the U.S. House, judicial posts, and many local offices. Candidate advertising for U.S. Senate and for the presidency was excluded from that main summary to keep the measure focused on offices most consistently affected by state-level electoral rules of the time. The log transformation adds one before taking logs so that years with zero observed advertising are retained on a continuous scale. The panel spans a period of substantial institutional change often associated with Progressive Era reforms. States transitioned at different times to practices such as direct primary elections for nominating statewide candidates, and the dataset records whether a state employed direct primaries in each year. Because election timing varied across states—some held major contests in odd-numbered years or in months other than November—the year variable records the actual calendar year of the pertinent general election. Overall, the dataset provides a harmonized view of newspaper-based campaign activity across places and time, facilitating comparisons of advertising intensity during a formative phase in U.S. electoral development.

   file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —

   ref_adopt (reference)

   file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —

   1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   3. filter {"query":"prim_sw == 1"}
   4. collapse {"aggs":[{"column":"year","fn":"min","name":"adopt_year"}],"by":["state"]}

   ref_panel (reference)

   file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —

   1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   3. filter {"query":"year in [1906, 1908]"}
   4. merge {"how":"left","on":["state"],"with":"ref_adopt"}
   5. merge {"how":"left","on":["state"],"with":"ref_years"}
   6. merge {"how":"inner","on":["state"],"with":"ref_pre"}
   7. merge {"how":"inner","on":["state"],"with":"ref_post"}
   8. filter {"query":"nyears == 2 and p_pre == 0 and ((adopt_year == 1908 and p_post == 1) or ((adopt_year != adopt_year or adopt_year > 1908) and p_post == 0))"}
   9. derive {"args":{"cmp":"eq","column":"p_post","value":1},"name":"Treated","op":"indicator"}
   10. derive {"args":{"cmp":"eq","column":"year","value":1908},"name":"Post","op":"indicator"}
   11. derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}

   ref_post (reference)

   file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —

   1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   3. filter {"query":"year == 1908"}
   4. select {"columns":["state","prim_sw"]}
   5. rename {"map":{"prim_sw":"p_post"}}

   ref_pre (reference)

   file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —

   1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   3. filter {"query":"year == 1906"}
   4. select {"columns":["state","prim_sw"]}
   5. rename {"map":{"prim_sw":"p_pre"}}

   ref_years (reference)

   file a1cd03905fff0a36614036650735de9568deb88305b615380f0334a23c7d923b · frame —

   1. select {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   2. dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}
   3. filter {"query":"year in [1906, 1908]"}
   4. collapse {"aggs":[{"column":"ln_x_cand_all","fn":"count","name":"n"}],"by":["state","year"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["state"]}
2. 1Transformação
   ref_adopt · select {"columns":["state","year","ln_x_cand_all","prim_sw"]}

   899 → 899 linhas

   Frame antes → depois: f94064123b5f → f94064123b5f
3. 2Transformação
   ref_adopt · dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}

   899 → 854 linhas

   Frame antes → depois: f94064123b5f → 3d47cfb535a1
4. 3Transformação
   ref_adopt · filter {"query":"prim_sw == 1"}

   854 → 308 linhas

   Frame antes → depois: 3d47cfb535a1 → 0a32a9f67b1a
5. 4Transformação
   ref_adopt · collapse {"aggs":[{"column":"year","fn":"min","name":"adopt_year"}],"by":["state"]}

   308 → 30 linhas

   Frame antes → depois: 0a32a9f67b1a → 09e5af2e0106
6. 5Transformação
   ref_years · select {"columns":["state","year","ln_x_cand_all","prim_sw"]}

   899 → 899 linhas

   Frame antes → depois: f94064123b5f → f94064123b5f
7. 6Transformação
   ref_years · dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}

   899 → 854 linhas

   Frame antes → depois: f94064123b5f → 3d47cfb535a1
8. 7Transformação
   ref_years · filter {"query":"year in [1906, 1908]"}

   854 → 64 linhas

   Frame antes → depois: 3d47cfb535a1 → 51d921091e0c
9. 8Transformação
   ref_years · collapse {"aggs":[{"column":"ln_x_cand_all","fn":"count","name":"n"}],"by":["state","year"]}

   64 → 64 linhas

   Frame antes → depois: 51d921091e0c → 5f43ce995d1d
10. 9Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["state"]}

    64 → 33 linhas

    Frame antes → depois: 5f43ce995d1d → 4be30cd277d1
11. 10Transformação
    ref_pre · select {"columns":["state","year","ln_x_cand_all","prim_sw"]}

    899 → 899 linhas

    Frame antes → depois: f94064123b5f → f94064123b5f
12. 11Transformação
    ref_pre · dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}

    899 → 854 linhas

    Frame antes → depois: f94064123b5f → 3d47cfb535a1
13. 12Transformação
    ref_pre · filter {"query":"year == 1906"}

    854 → 31 linhas

    Frame antes → depois: 3d47cfb535a1 → 699b690a60e9
14. 13Transformação
    ref_pre · select {"columns":["state","prim_sw"]}

    31 → 31 linhas

    Frame antes → depois: 699b690a60e9 → 27983908161a
15. 14Transformação
    ref_pre · rename {"map":{"prim_sw":"p_pre"}}

    31 → 31 linhas

    Frame antes → depois: 27983908161a → 317d484d8416
16. 15Transformação
    ref_post · select {"columns":["state","year","ln_x_cand_all","prim_sw"]}

    899 → 899 linhas

    Frame antes → depois: f94064123b5f → f94064123b5f
17. 16Transformação
    ref_post · dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}

    899 → 854 linhas

    Frame antes → depois: f94064123b5f → 3d47cfb535a1
18. 17Transformação
    ref_post · filter {"query":"year == 1908"}

    854 → 33 linhas

    Frame antes → depois: 3d47cfb535a1 → c4f0cab2de86
19. 18Transformação
    ref_post · select {"columns":["state","prim_sw"]}

    33 → 33 linhas

    Frame antes → depois: c4f0cab2de86 → 78073baf1402
20. 19Transformação
    ref_post · rename {"map":{"prim_sw":"p_post"}}

    33 → 33 linhas

    Frame antes → depois: 78073baf1402 → 28a65026c564
21. 20Transformação
    ref_panel · select {"columns":["state","year","ln_x_cand_all","prim_sw"]}

    899 → 899 linhas

    Frame antes → depois: f94064123b5f → f94064123b5f
22. 21Transformação
    ref_panel · dropna {"columns":["state","year","ln_x_cand_all","prim_sw"]}

    899 → 854 linhas

    Frame antes → depois: f94064123b5f → 3d47cfb535a1
23. 22Transformação
    ref_panel · filter {"query":"year in [1906, 1908]"}

    854 → 64 linhas

    Frame antes → depois: 3d47cfb535a1 → 51d921091e0c
24. 23Transformação
    ref_panel · merge {"how":"left","on":["state"],"with":"ref_adopt"}

    64 → 64 linhas

    Frame antes → depois: 51d921091e0c → 2f14ccebfd25
25. 24Transformação
    ref_panel · merge {"how":"left","on":["state"],"with":"ref_years"}

    64 → 64 linhas

    Frame antes → depois: 2f14ccebfd25 → dee953d29d29
26. 25Transformação
    ref_panel · merge {"how":"inner","on":["state"],"with":"ref_pre"}

    64 → 62 linhas

    Frame antes → depois: dee953d29d29 → 392ecf176355
27. 26Transformação
    ref_panel · merge {"how":"inner","on":["state"],"with":"ref_post"}

    62 → 62 linhas

    Frame antes → depois: 392ecf176355 → 1b18b869ae1d
28. 27Transformação
    ref_panel · filter {"query":"nyears == 2 and p_pre == 0 and ((adopt_year == 1908 and p_post == 1) or ((adopt_year != adopt_year or adopt_year > 1908) and p_post == 0))"}

    62 → 58 linhas

    Frame antes → depois: 1b18b869ae1d → c0d4538c6fc1
29. 28Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"p_post","value":1},"name":"Treated","op":"indicator"}

    58 → 58 linhas

    Frame antes → depois: c0d4538c6fc1 → 10e582ca44e2
30. 29Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":1908},"name":"Post","op":"indicator"}

    58 → 58 linhas

    Frame antes → depois: 10e582ca44e2 → 33fb89724fcb
31. 30Transformação
    ref_panel · derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}

    58 → 58 linhas

    Frame antes → depois: 33fb89724fcb → 686fb3d7f251
32. 31Turno do modelo
    call_ddcca4c7referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('Y ~ Treated + Post + Treated:Post').fit(cov_type='cluster', groups=state, use_t=True) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "state",
        "controls": [
          "Treated",
          "Post"
        ],
        "dataset": "ref_panel",
        "x": [
          "Treated_Post"
        ],
        "y": "ln_x_cand_all"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
33. 32Ferramenta
    infer.olsv0.1.0call_27dbdfaadecidido pelo modeloreexecutável

    Motivo: smf.ols('Y ~ Treated + Post + Treated:Post').fit(cov_type='cluster', groups=state, use_t=True) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "state",
      "controls": [
        "Treated",
        "Post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "Treated_Post"
      ],
      "y": "ln_x_cand_all"
    }

    Figuras call_27dbdfaa

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.Treated_Post Coefficient on Treated_Post in the regression of ln_x_cand_all	0,195425	0.1954246464717601	0,166480.16647987316254842	-0,145594 – 0,536443

    Diagnósticos

    • n_obs = 5858
    • k = 44
    • r2 = 0,03763920.03763919450899278
    • df_resid = 5454
    • se_type = "cluster"
    • n_clusters = 2929

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.6382510104866885,
          "ci_low": 0.16639514943468509,
          "coef": 0.40232307996068684,
          "p": 0.001604978791942211,
          "se": 0.11517628781514211,
          "t": 3.4931068503129326,
          "term": "Intercept"
        },
        {
          "ci_high": 0.5364432076230908,
          "ci_low": -0.1455939146795706,
          "coef": 0.1954246464717601,
          "p": 0.2503380390502275,
          "se": 0.16647987316254842,
          "t": 1.1738634992888928,
          "term": "Treated_Post"
        },
        {
          "ci_high": 0.26844399717808504,
          "ci_low": -0.439598129690084,
          "coef": -0.08557706625599948,
          "p": 0.6243515856524174,
          "se": 0.17282748932608039,
          "t": -0.4951588812040073,
          "term": "Treated"
        },
        {
          "ci_high": 0.2700839703245484,
          "ci_low": -0.05244240597237665,
          "coef": 0.10882078217608587,
          "p": 0.1778167092284717,
          "se": 0.07872614035466105,
          "t": 1.3822700018805512,
          "term": "Post"
        }
      ]
    }

    Digests dos dados

    ref_panel 686fb3d7f251101ed52cc2397529b0fe19a10f21cd450a938041a90097a565ee

    sha256 do resultado

    42da2cfcf254079d67381d145b3c5bc0d2af80558bf170585d874f49c6b90767

    Resultado mostrado ao modelo
34. 33Turno do modelo
    call_348de981referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Estimativa (infer.ols): coef.Treated_Post = 0,1954. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_27dbdfaa:coef.Treated_Post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
35. 34Guarda
    resposta aceita

    Números encontrados

    0,1954

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
36. 35Resposta

    Estimativa (infer.ols): coef.Treated_Post = 0,1954. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_27dbdfaa:coef.Treated_Post · Parada: answered

    Figura da resposta: call_27dbdfaa:coef.Treated_Post = 0,1954250.1954246464717601 (se 0,166480.16647987316254842)
37. 36Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
38. 37Pontuação
    #1figura da resposta próximapassouobrigatória0.1954246465 in [-0.1308759049, 0.5217251979]?
39. 38Pontuação
    #2figura da resposta próximapassouregistrada0.1954246465 vs 0.19542464647176022
40. 39Pontuação
    #3figura da resposta próximapassouregistrada0.1664798732 vs 0.16647987316254834

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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