Tarefa crb_papers.q039

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Pergunta feita ao analista

Among cities with observed reform status at both censuses, how did adopting a civil service commission after 1930 and by 1940 affect the change from 1930 to 1940 in the share of blue-collar city jobs held by foreign-born white residents, compared with cities that had not adopted by 1940, adjusting for the city's foreign-born white population share?

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A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

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1. 0Início

   Among cities with observed reform status at both censuses, how did adopting a civil service commission after 1930 and by 1940 affect the change from 1930 to 1940 in the share of blue-collar city jobs held by foreign-born white residents, compared with cities that had not adopted by 1940, adjusting for the city's foreign-born white population share?

   ds_0020 (agent)

   | Variable Name | Description | | --- | --- | | treat | Binary indicator equal to 1 if the municipality had established a municipal civil service commission (merit-based personnel system) by the given census year; 0 if not yet established. Values are weakly increasing over time within a city. Sourced primarily from U.S. Civil Service Assembly municipal surveys (1937, 1940, 1943) and corroborated by state statutes; may be missing (blank/NA) where adoption year could not be determined. | | pop_prop | Proportion (0–1) of the city’s residents who are foreign-born whites in that census year. Constructed from full-count decennial U.S. Census microdata using birthplace (foreign-born) and enumerated race (white). Available for decennial years 1850–1940 except 1890 (destroyed census). | | govt_prop | Proportion (0–1) of blue-collar municipal government employees who are foreign-born whites in that census year. Computed from full-count decennial U.S. Census microdata by identifying residents employed in local public administration and classifying blue-collar occupations (e.g., craftsmen, mechanics, service workers, laborers); police and fire personnel are excluded due to distinct recruitment regimes. Counts reflect resident workers and assume city employment generally coincides with residence in this era. | | city | Municipality name and two-letter state abbreviation in the standardized format “city, st” (e.g., “aberdeen, sd”). Standardization facilitates consistent tracking across sources and decades; cities with the same name are distinguished by state. | | YEAR | Decennial census year for which measures are calculated (integers from 1850 to 1940; 1890 absent due to the lost census). | Dataset Context: This dataset follows U.S. municipalities at ten-year intervals from the mid-19th to early-20th centuries, a period spanning patronage-based municipal staffing through the widespread diffusion of merit-oriented civil service systems. Municipal adoption of civil service rules was recorded by the U.S. Civil Service Assembly in nationwide surveys conducted in 1937, 1940, and 1943 and supplemented by state-level statutes that, in many cases, mandated adoption using population thresholds. The “treat” indicator reflects whether a city had a functioning municipal civil service commission by a given census year; because commissions, once established, typically persisted, the indicator does not toggle back to zero for later years. Demographic and employment composition measures are derived from the full-count decennial U.S. Censuses (1850–1940, with 1890 unavailable). The census enumerated every resident and captured birthplace, race, industry, and occupation, allowing identification of local public administration workers and classification of blue-collar positions. Blue-collar categories include craftsmen, mechanics, service workers, and laborers; police and fire employees are excluded because their hiring, promotion, and protection rules often followed distinct legal frameworks. Because the census does not name employers, municipal employment is inferred from the industry code “local public administration,” and, consistent with transportation realities of the era, employees are assumed to work in the municipality where they reside. The civil service systems cataloged here typically introduced commission oversight of hiring, promotion, and termination in “classified” services, commonly relying on publicly advertised examinations and shortlists (e.g., a “rule of three”). “Unclassified” roles (e.g., department heads, certain temporary staff) fell outside commission rules, and the scope of coverage varied across cities and over time. City names are standardized to “city, st” to harmonize historical references across sources and decades. Shares are expressed as proportions between 0 and 1 and are calculated at the city–year level to align with the decennial timing of the underlying census enumerations.

   file fb21d9bfd9e8815223675810e217e1db86e4bbf0d90e6a351772c71a5d17b441 · frame —

   ref_fty (reference)

   file fb21d9bfd9e8815223675810e217e1db86e4bbf0d90e6a351772c71a5d17b441 · frame —

   1. select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}
   2. rename {"map":{"YEAR":"year"}}
   3. dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}
   4. filter {"query":"treat == 1"}
   5. collapse {"aggs":[{"column":"year","fn":"min","name":"fty"}],"by":["city"]}

   ref_panel (reference)

   file fb21d9bfd9e8815223675810e217e1db86e4bbf0d90e6a351772c71a5d17b441 · frame —

   1. select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}
   2. rename {"map":{"YEAR":"year"}}
   3. dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}
   4. filter {"query":"year in [1930, 1940]"}
   5. merge {"how":"left","on":["city"],"with":"ref_years"}
   6. filter {"query":"nyears == 2"}
   7. merge {"how":"inner","on":["city"],"with":"ref_pre"}
   8. merge {"how":"inner","on":["city"],"with":"ref_post"}
   9. merge {"how":"left","on":["city"],"with":"ref_fty"}
   10. filter {"query":"t_pre == 0 and (t_post == 1 or (t_post == 0 and (fty != fty or fty > 1940)))"}
   11. derive {"args":{"cmp":"eq","column":"t_post","value":1},"name":"Treated","op":"indicator"}
   12. derive {"args":{"cmp":"eq","column":"year","value":1940},"name":"Post","op":"indicator"}
   13. derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}

   ref_post (reference)

   file fb21d9bfd9e8815223675810e217e1db86e4bbf0d90e6a351772c71a5d17b441 · frame —

   1. select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}
   2. rename {"map":{"YEAR":"year"}}
   3. dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}
   4. filter {"query":"year == 1940"}
   5. collapse {"aggs":[{"column":"treat","fn":"max","name":"t_post"}],"by":["city"]}

   ref_pre (reference)

   file fb21d9bfd9e8815223675810e217e1db86e4bbf0d90e6a351772c71a5d17b441 · frame —

   1. select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}
   2. rename {"map":{"YEAR":"year"}}
   3. dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}
   4. filter {"query":"year == 1930"}
   5. collapse {"aggs":[{"column":"treat","fn":"max","name":"t_pre"}],"by":["city"]}

   ref_years (reference)

   file fb21d9bfd9e8815223675810e217e1db86e4bbf0d90e6a351772c71a5d17b441 · frame —

   1. select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}
   2. rename {"map":{"YEAR":"year"}}
   3. dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}
   4. filter {"query":"year in [1930, 1940]"}
   5. collapse {"aggs":[{"column":"treat","fn":"count","name":"n"}],"by":["city","year"]}
   6. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["city"]}
2. 1Transformação
   ref_fty · select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}

   4408 → 4408 linhas

   Frame antes → depois: cc7d39e21613 → 60f04f92f2b8
3. 2Transformação
   ref_fty · rename {"map":{"YEAR":"year"}}

   4408 → 4408 linhas

   Frame antes → depois: 60f04f92f2b8 → 8e4754dadcb6
4. 3Transformação
   ref_fty · dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}

   4408 → 1604 linhas

   Frame antes → depois: 8e4754dadcb6 → a154ac026892
5. 4Transformação
   ref_fty · filter {"query":"treat == 1"}

   1604 → 781 linhas

   Frame antes → depois: a154ac026892 → 0bba33ca7b15
6. 5Transformação
   ref_fty · collapse {"aggs":[{"column":"year","fn":"min","name":"fty"}],"by":["city"]}

   781 → 274 linhas

   Frame antes → depois: 0bba33ca7b15 → 132e501a14af
7. 6Transformação
   ref_years · select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}

   4408 → 4408 linhas

   Frame antes → depois: cc7d39e21613 → 60f04f92f2b8
8. 7Transformação
   ref_years · rename {"map":{"YEAR":"year"}}

   4408 → 4408 linhas

   Frame antes → depois: 60f04f92f2b8 → 8e4754dadcb6
9. 8Transformação
   ref_years · dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}

   4408 → 1604 linhas

   Frame antes → depois: 8e4754dadcb6 → a154ac026892
10. 9Transformação
    ref_years · filter {"query":"year in [1930, 1940]"}

    1604 → 583 linhas

    Frame antes → depois: a154ac026892 → 6db58c114a30
11. 10Transformação
    ref_years · collapse {"aggs":[{"column":"treat","fn":"count","name":"n"}],"by":["city","year"]}

    583 → 583 linhas

    Frame antes → depois: 6db58c114a30 → 438ef635c468
12. 11Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["city"]}

    583 → 293 linhas

    Frame antes → depois: 438ef635c468 → 38e6d0a9bd23
13. 12Transformação
    ref_pre · select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}

    4408 → 4408 linhas

    Frame antes → depois: cc7d39e21613 → 60f04f92f2b8
14. 13Transformação
    ref_pre · rename {"map":{"YEAR":"year"}}

    4408 → 4408 linhas

    Frame antes → depois: 60f04f92f2b8 → 8e4754dadcb6
15. 14Transformação
    ref_pre · dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}

    4408 → 1604 linhas

    Frame antes → depois: 8e4754dadcb6 → a154ac026892
16. 15Transformação
    ref_pre · filter {"query":"year == 1930"}

    1604 → 293 linhas

    Frame antes → depois: a154ac026892 → 085c7871adff
17. 16Transformação
    ref_pre · collapse {"aggs":[{"column":"treat","fn":"max","name":"t_pre"}],"by":["city"]}

    293 → 293 linhas

    Frame antes → depois: 085c7871adff → 4fbb7b353915
18. 17Transformação
    ref_post · select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}

    4408 → 4408 linhas

    Frame antes → depois: cc7d39e21613 → 60f04f92f2b8
19. 18Transformação
    ref_post · rename {"map":{"YEAR":"year"}}

    4408 → 4408 linhas

    Frame antes → depois: 60f04f92f2b8 → 8e4754dadcb6
20. 19Transformação
    ref_post · dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}

    4408 → 1604 linhas

    Frame antes → depois: 8e4754dadcb6 → a154ac026892
21. 20Transformação
    ref_post · filter {"query":"year == 1940"}

    1604 → 290 linhas

    Frame antes → depois: a154ac026892 → 0c1e1c417aee
22. 21Transformação
    ref_post · collapse {"aggs":[{"column":"treat","fn":"max","name":"t_post"}],"by":["city"]}

    290 → 290 linhas

    Frame antes → depois: 0c1e1c417aee → 75fb905c1366
23. 22Transformação
    ref_panel · select {"columns":["city","YEAR","treat","govt_prop","pop_prop"]}

    4408 → 4408 linhas

    Frame antes → depois: cc7d39e21613 → 60f04f92f2b8
24. 23Transformação
    ref_panel · rename {"map":{"YEAR":"year"}}

    4408 → 4408 linhas

    Frame antes → depois: 60f04f92f2b8 → 8e4754dadcb6
25. 24Transformação
    ref_panel · dropna {"columns":["city","year","treat","govt_prop","pop_prop"]}

    4408 → 1604 linhas

    Frame antes → depois: 8e4754dadcb6 → a154ac026892
26. 25Transformação
    ref_panel · filter {"query":"year in [1930, 1940]"}

    1604 → 583 linhas

    Frame antes → depois: a154ac026892 → 6db58c114a30
27. 26Transformação
    ref_panel · merge {"how":"left","on":["city"],"with":"ref_years"}

    583 → 583 linhas

    Frame antes → depois: 6db58c114a30 → ed5f512e17b2
28. 27Transformação
    ref_panel · filter {"query":"nyears == 2"}

    583 → 580 linhas

    Frame antes → depois: ed5f512e17b2 → bdcffa66860e
29. 28Transformação
    ref_panel · merge {"how":"inner","on":["city"],"with":"ref_pre"}

    580 → 580 linhas

    Frame antes → depois: bdcffa66860e → f7b25e6ba34c
30. 29Transformação
    ref_panel · merge {"how":"inner","on":["city"],"with":"ref_post"}

    580 → 580 linhas

    Frame antes → depois: f7b25e6ba34c → f92a53b7eaa9
31. 30Transformação
    ref_panel · merge {"how":"left","on":["city"],"with":"ref_fty"}

    580 → 580 linhas

    Frame antes → depois: f92a53b7eaa9 → 2dbdf558ac12
32. 31Transformação
    ref_panel · filter {"query":"t_pre == 0 and (t_post == 1 or (t_post == 0 and (fty != fty or fty > 1940)))"}

    580 → 182 linhas

    Frame antes → depois: 2dbdf558ac12 → 9440860bc705
33. 32Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"t_post","value":1},"name":"Treated","op":"indicator"}

    182 → 182 linhas

    Frame antes → depois: 9440860bc705 → bec0293320bf
34. 33Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":1940},"name":"Post","op":"indicator"}

    182 → 182 linhas

    Frame antes → depois: bec0293320bf → eddd7a4b3e30
35. 34Transformação
    ref_panel · derive {"args":{"columns":["Treated","Post"]},"name":"Treated_Post","op":"interaction"}

    182 → 182 linhas

    Frame antes → depois: eddd7a4b3e30 → 158371a3ec53
36. 35Turno do modelo
    call_b42b5876referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('govt_prop ~ Treated * Post + pop_prop').fit(cov_type='cluster', groups=city) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "city",
        "controls": [
          "Treated",
          "Post",
          "pop_prop"
        ],
        "dataset": "ref_panel",
        "x": [
          "Treated_Post"
        ],
        "y": "govt_prop"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
37. 36Ferramenta
    infer.olsv0.1.0call_3729da8ddecidido pelo modeloreexecutável

    Motivo: smf.ols('govt_prop ~ Treated * Post + pop_prop').fit(cov_type='cluster', groups=city) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

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

    Figuras call_3729da8d

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.Treated_Post Coefficient on Treated_Post in the regression of govt_prop	0,0141794	0.014179411903156438	0,03114520.031145196886261942	-0,047696 – 0,0760548

    Diagnósticos

    • n_obs = 182182
    • k = 55
    • r2 = 0,6046950.6046948876322779
    • df_resid = 177177
    • se_type = "cluster"
    • n_clusters = 9191

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.11498379084326872,
          "ci_low": -0.004966749675327717,
          "coef": 0.0550085205839705,
          "p": 0.07175330173143507,
          "se": 0.030188774774378657,
          "t": 1.8221514783255288,
          "term": "Intercept"
        },
        {
          "ci_high": 0.07605478162229931,
          "ci_low": -0.04769595781598643,
          "coef": 0.014179411903156438,
          "p": 0.6500128514218789,
          "se": 0.031145196886261942,
          "t": 0.45526801307237635,
          "term": "Treated_Post"
        },
        {
          "ci_high": 0.001906089942806992,
          "ci_low": -0.12048197725504559,
          "coef": -0.0592879436561193,
          "p": 0.05741384646822674,
          "se": 0.030802243822608537,
          "t": -1.9247930117546355,
          "term": "Treated"
        },
        {
          "ci_high": 0.02782624088125852,
          "ci_low": -0.08252476447700391,
          "coef": -0.027349261797872698,
          "p": 0.3273867994739465,
          "se": 0.027772793957275766,
          "t": -0.9847501061630815,
          "term": "Post"
        },
        {
          "ci_high": 1.5464123526051585,
          "ci_low": 1.0839242022732951,
          "coef": 1.3151682774392268,
          "p": 5.977896543268197e-19,
          "se": 0.11639756307745047,
          "t": 11.298933093333915,
          "term": "pop_prop"
        }
      ]
    }

    Digests dos dados

    ref_panel 158371a3ec5351936f967f5e664fc5d62c898fda2cd3934ce77263904471e334

    sha256 do resultado

    3b7f46923d37f18628bb9dd9a20d9b19feaffe8f01e9482a389c46be4da378d6

    Resultado mostrado ao modelo
38. 37Turno do modelo
    call_00a351a5referenceFim: 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 = 0,01418. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_3729da8d:coef.Treated_Post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
39. 38Guarda
    resposta aceita

    Números encontrados

    0,01418

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
40. 39Resposta

    Estimativa (infer.ols): coef.Treated_Post = 0,01418. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_3729da8d:coef.Treated_Post · Parada: answered

    Figura da resposta: call_3729da8d:coef.Treated_Post = 0,01417940.014179411903156438 (se 0,03114520.031145196886261942)
41. 40Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
42. 41Pontuação
    #1figura da resposta próximapassouobrigatória0.0141794119 in [-0.04686517399, 0.0752239978]?
43. 42Pontuação
    #2figura da resposta próximapassouregistrada0.0141794119 vs 0.014179411903156684
44. 43Pontuação
    #3figura da resposta próximapassouregistrada0.03114519689 vs 0.031145196886261866

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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