Tarefa crb_papers.q028

Veredito

Pergunta feita ao analista

Among municipalities that switched to council decision-making in 2006, how large was the change in the ordinary naturalization rate from 2005 to 2007 compared with municipalities still using citizen referendums in 2007?

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

   Among municipalities that switched to council decision-making in 2006, how large was the change in the ordinary naturalization rate from 2005 to 2007 compared with municipalities still using citizen referendums in 2007?

   ds_0015 (agent)

   | Variable Name | Description | | --- | --- | | bfs | Swiss Federal Statistical Office (BFS) municipality code: a stable numeric identifier for each municipality in the panel. | | year | Calendar year of the observation (1991–2009). Institutional status is referenced as of January 1 of the given year, and annual rates are computed for the same year. | | nat_rate_ord | Ordinary naturalization rate (percent). Computed as the number of ordinary naturalizations recorded during the year divided by the stock of foreign residents who met statutory eligibility at the start of the year in the same municipality, multiplied by 100. Ordinary naturalizations refer to the standard pathway (as opposed to federally handled “facilitated” cases). | | indirect | Local decision procedure in place on January 1: 1 if applications were decided by the elected municipal council (representative/indirect democracy); 0 if applications were decided by citizens in a local vote (direct democracy). | Dataset Context: This dataset is an annual municipality–year panel for Switzerland that spans a period of institutional change in how local authorities decide on citizenship applications. Switzerland’s “triple citizenship” system assigns a central role to municipalities in evaluating and deciding ordinary naturalization requests. During the 1990s and 2000s, municipalities used different decision procedures: in some, citizens voted on applications (often at a citizens’ assembly and, historically in a few places, by ballot); in others, elected municipal councils rendered the decisions. A series of Swiss Federal Court rulings in 2003–2005 curtailed secret-ballot referendums for naturalizations and required municipalities to provide formal justifications for rejections. Several cantons subsequently updated their laws, and many municipalities revised or formalized their procedures, frequently shifting decision authority to municipal councils. The indicator in this file captures, for each municipality and year, which procedure was in force as of January 1. The panel combines two types of information. First, administrative registers from the Swiss Federal Statistical Office (PETRA: Foreign Population Structure and Migration Statistics) are used to derive annual ordinary naturalization rates. These rates are constructed as ordinary naturalizations completed within a year divided by the number of foreign residents who met the legal eligibility criteria at the start of that year, providing a comparable percentage measure across municipalities and over time. Second, the coding of local decision procedures relies on a nationwide survey of municipal head secretaries and systematic compilation of cantonal regulations and court decisions. The measurement convention aligns the institutional status to January 1 to enhance comparability across municipalities, recognizing that formal switches may occur during a calendar year. Ordinary naturalizations represent the standard route to citizenship and, in the study period, typically occurred after lengthy processing that could extend several years from initial application to final local vote. Facilitated naturalizations—handled centrally under federal law (for example, for certain spouses of Swiss citizens)—are not included in the numerator of the ordinary naturalization rate. The resulting dataset provides a consistent view of local institutions and annual naturalization activity across more than a decade and a half, covering the pre- and post-reform environment in which many municipalities updated their decision-making arrangements.

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   ref_first (reference)

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   1. select {"columns":["bfs","year","nat_rate_ord","indirect"]}
   2. dropna {"columns":["bfs","year","nat_rate_ord"]}
   3. filter {"query":"indirect == 1"}
   4. collapse {"aggs":[{"column":"year","fn":"min","name":"sw"}],"by":["bfs"]}

   ref_panel (reference)

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   1. select {"columns":["bfs","year","nat_rate_ord","indirect"]}
   2. dropna {"columns":["bfs","year","nat_rate_ord"]}
   3. filter {"query":"year in [2005, 2007]"}
   4. merge {"how":"left","on":["bfs"],"with":"ref_first"}
   5. merge {"how":"left","on":["bfs"],"with":"ref_years"}
   6. filter {"query":"nyears == 2 and (sw == 2006 or sw > 2007 or sw != sw)"}
   7. derive {"args":{"cmp":"eq","column":"sw","value":2006},"name":"group_treated","op":"indicator"}
   8. derive {"args":{"column":"group_treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"group_treated","op":"map"}
   9. derive {"args":{"cmp":"eq","column":"year","value":2007},"name":"post","op":"indicator"}
   10. derive {"args":{"columns":["group_treated","post"]},"name":"g_post","op":"interaction"}

   ref_years (reference)

   file e9ce75e30cb08926da17f27cb91ecb80f964f077f9a592e052d672b61239e8f9 · frame —

   1. select {"columns":["bfs","year","nat_rate_ord","indirect"]}
   2. dropna {"columns":["bfs","year","nat_rate_ord"]}
   3. filter {"query":"year in [2005, 2007]"}
   4. collapse {"aggs":[{"column":"nat_rate_ord","fn":"count","name":"n"}],"by":["bfs","year"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["bfs"]}
2. 1Transformação
   ref_first · select {"columns":["bfs","year","nat_rate_ord","indirect"]}

   22971 → 22971 linhas

   Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
3. 2Transformação
   ref_first · dropna {"columns":["bfs","year","nat_rate_ord"]}

   22971 → 22971 linhas

   Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
4. 3Transformação
   ref_first · filter {"query":"indirect == 1"}

   22971 → 7836 linhas

   Frame antes → depois: b496d06ec0c7 → 8ab632303c72
5. 4Transformação
   ref_first · collapse {"aggs":[{"column":"year","fn":"min","name":"sw"}],"by":["bfs"]}

   7836 → 797 linhas

   Frame antes → depois: 8ab632303c72 → 8781603036ea
6. 5Transformação
   ref_years · select {"columns":["bfs","year","nat_rate_ord","indirect"]}

   22971 → 22971 linhas

   Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
7. 6Transformação
   ref_years · dropna {"columns":["bfs","year","nat_rate_ord"]}

   22971 → 22971 linhas

   Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
8. 7Transformação
   ref_years · filter {"query":"year in [2005, 2007]"}

   22971 → 2418 linhas

   Frame antes → depois: b496d06ec0c7 → 59f33c0fef3b
9. 8Transformação
   ref_years · collapse {"aggs":[{"column":"nat_rate_ord","fn":"count","name":"n"}],"by":["bfs","year"]}

   2418 → 2418 linhas

   Frame antes → depois: 59f33c0fef3b → c9d7fe6b04ae
10. 9Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["bfs"]}

    2418 → 1209 linhas

    Frame antes → depois: c9d7fe6b04ae → fe46190a8274
11. 10Transformação
    ref_panel · select {"columns":["bfs","year","nat_rate_ord","indirect"]}

    22971 → 22971 linhas

    Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
12. 11Transformação
    ref_panel · dropna {"columns":["bfs","year","nat_rate_ord"]}

    22971 → 22971 linhas

    Frame antes → depois: b496d06ec0c7 → b496d06ec0c7
13. 12Transformação
    ref_panel · filter {"query":"year in [2005, 2007]"}

    22971 → 2418 linhas

    Frame antes → depois: b496d06ec0c7 → 59f33c0fef3b
14. 13Transformação
    ref_panel · merge {"how":"left","on":["bfs"],"with":"ref_first"}

    2418 → 2418 linhas

    Frame antes → depois: 59f33c0fef3b → 5def0a657780
15. 14Transformação
    ref_panel · merge {"how":"left","on":["bfs"],"with":"ref_years"}

    2418 → 2418 linhas

    Frame antes → depois: 5def0a657780 → b6fe68da1e3d
16. 15Transformação
    ref_panel · filter {"query":"nyears == 2 and (sw == 2006 or sw > 2007 or sw != sw)"}

    2418 → 1514 linhas

    Frame antes → depois: b6fe68da1e3d → d43d26232dc3
17. 16Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"sw","value":2006},"name":"group_treated","op":"indicator"}

    1514 → 1514 linhas

    Frame antes → depois: d43d26232dc3 → d4e107a25045
18. 17Transformação
    ref_panel · derive {"args":{"column":"group_treated","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"group_treated","op":"map"}

    1514 → 1514 linhas

    Frame antes → depois: d4e107a25045 → 584fb26d5660
19. 18Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2007},"name":"post","op":"indicator"}

    1514 → 1514 linhas

    Frame antes → depois: 584fb26d5660 → 95ba4b395043
20. 19Transformação
    ref_panel · derive {"args":{"columns":["group_treated","post"]},"name":"g_post","op":"interaction"}

    1514 → 1514 linhas

    Frame antes → depois: 95ba4b395043 → a648f405292a
21. 20Turno do modelo
    call_c015611breferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y, add_constant([group_treated, post, g_post])).fit(cov_type='cluster', groups=bfs) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "bfs",
        "controls": [
          "group_treated",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "g_post"
        ],
        "y": "nat_rate_ord"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
22. 21Ferramenta
    infer.olsv0.1.0call_65f021cedecidido pelo modeloreexecutável

    Motivo: sm.OLS(y, add_constant([group_treated, post, g_post])).fit(cov_type='cluster', groups=bfs) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "bfs",
      "controls": [
        "group_treated",
        "post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "g_post"
      ],
      "y": "nat_rate_ord"
    }

    Figuras call_65f021ce

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.g_post Coefficient on g_post in the regression of nat_rate_ord	1,23663	1.2366347868124075	0,6189760.6189762112257456	0,0215183 – 2,45175

    Diagnósticos

    • n_obs = 1.5141514
    • k = 44
    • r2 = 0,00960040.009600396125138744
    • df_resid = 1.5101510
    • se_type = "cluster"
    • n_clusters = 757757

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 3.5332604703963817,
          "ci_low": 2.740882487028694,
          "coef": 3.1370714787125378,
          "p": 1.7425303619470652e-47,
          "se": 0.20181733404602986,
          "t": 15.54411316322831,
          "term": "Intercept"
        },
        {
          "ci_high": 2.4517512294163035,
          "ci_low": 0.021518344208511175,
          "coef": 1.2366347868124075,
          "p": 0.046088454160030774,
          "se": 0.6189762112257456,
          "t": 1.9978712661081521,
          "term": "g_post"
        },
        {
          "ci_high": 1.35362095997206,
          "ci_low": -0.5838333418285101,
          "coef": 0.38489380907177484,
          "p": 0.4356468454972044,
          "se": 0.4934663383295467,
          "t": 0.7799798672685452,
          "term": "group_treated"
        },
        {
          "ci_high": 0.18167791076922124,
          "ci_low": -0.7334669910514132,
          "coef": -0.275894540141096,
          "p": 0.23691950500613224,
          "se": 0.2330858608240172,
          "t": -1.1836605582412392,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel a648f405292a274c8c0c61dfa0f60ee4de7932b1cec42b0e099fcf19cc64311d

    sha256 do resultado

    4319c69128987e1c1b49608d45a946b643b2e1d56d55a10f3b75389ae4fefb52

    Resultado mostrado ao modelo
23. 22Turno do modelo
    call_56c7d139referenceFim: 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.g_post = 1,237. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_65f021ce:coef.g_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
24. 23Guarda
    resposta aceita

    Números encontrados

    1,237

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
25. 24Resposta

    Estimativa (infer.ols): coef.g_post = 1,237. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_65f021ce:coef.g_post · Parada: answered

    Figura da resposta: call_65f021ce:coef.g_post = 1,236631.2366347868124075 (se 0,6189760.6189762112257456)
26. 25Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
27. 26Pontuação
    #1figura da resposta próximapassouobrigatória1.236634787 in [0.02344141281, 2.449828161]?
28. 27Pontuação
    #2figura da resposta próximapassouregistrada1.236634787 vs 1.236634786812406
29. 28Pontuação
    #3figura da resposta próximapassouregistrada0.6189762112 vs 0.6189762112257445

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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