Tarefa crb_papers.q104

Veredito

Pergunta feita ao analista

Among gubernatorial elections decided by very small margins, what is the causal effect of a Democratic (vs. Republican) gubernatorial victory on the subsequent midterm change in the Democratic share of lower-house seats?

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   ds_0070— | Variable Name | Description | |----------------|------------------------------------------------------------------------------------------------------------------------------------------| | Unnamed: 0 | - | | dem_lhpct_chng | Change in the Democratic share of seats in the state lower house between the prior gubernatorial election year and the subsequent midterm election (percentage points, two‑party share). | | D_1 | Democratic gubernatorial vote margin from 50 percent in the previous gubernatorial election (percentage points; positive if the Democrat won, negative if the Republican won). | Data Description: This file is a pared‑down extract constructed to study midterm seat swings in U.S. state lower‑house elections following gubernatorial contests. Each row corresponds to a state midterm legislative election year, and variables record (a) how the Democratic two‑party seat share changed from the last cycle and (b) the Democratic win/lose margin in the immediately preceding gubernatorial election. The coverage used in the original study spans 1882–2010, focusing on lower chambers because most upper chambers are staggered and often small. Cases with nonpartisan legislatures or strong third‑party dominance were excluded, and the analysis centers on years when the governor was not on the ballot but the lower house was. The data were assembled from historical administrative sources: state legislative seat counts from Dubin (2007) with recent updates from the National Conference of State Legislatures, and gubernatorial election returns from ICPSR compilations and state election officials. The extract you have contains just the variables required to reproduce the core state‑level midterm seat‑change analysis.
   ref_window
   Transformação "select" aplicada à base "ref_window" (873 → 873 linhas). {"columns":["dem_lhpct_chng","D_1"]}
   Transformação "dropna" aplicada à base "ref_window" (873 → 873 linhas). {"columns":["dem_lhpct_chng","D_1"]}
   Transformação "filter" aplicada à base "ref_window" (873 → 288 linhas). {"query":"D_1 >= -3 and D_1 <= 3"}
2. 2Chamada que gerou a resposta finalinfer.rdd(Regressão descontínua (RDD))citada na respostacoincide com a referência

   Ver chamada call_fe607a26 nos detalhes

   rdrobust(y_w, x_w, c=0.0, p=1, kernel='triangular', h=h) with h = 3; the script prints the first (Conventional) entry of coef: the local linear jump at h

   Por que este é o caminho de referência: rdrobust(y_w, x_w, c=0.0, p=1, kernel='triangular', h=h) with h = 3; the script prints the first (Conventional) entry of coef: the local linear jump at h
   Argumentos principais

   bandwidth:

   33

   cutoff:

   00

   dataset:

   "ref_window"

   kernel:

   "triangular"

   level:

   0,950.95

   polynomial:

   11

   running:

   "D_1"

   y:

   "dem_lhpct_chng"

   Resultado calculado

   • effect = -4,30662 -4.306620864874279
     Jump in dem_lhpct_chng at the cutoff of D_1
   Diagnósticos: bandwidth = 33 (half-width of the window around the cutoff (informada)) · bandwidth_method = "user" · n_left = 135135 (observations with positive weight below the cutoff) · n_right = 153153 (observations with positive weight at or above the cutoff) · kernel = "triangular" · polynomial = 11 · bandwidth_sensitive = false (the estimates at half and double the bandwidth fall inside the main interval) · n_obs = 288288 (rows used to choose the bandwidth) · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: -4,307
   Figuras citadas: call_fe607a26:effect
   • ✓
     #0resposta com lastro

     every number traced to a tool call or the question
   • ✓
     #1figura da resposta próxima

     CRB estimate_within_solution_ci: the answer figure inside the gold 95% interval effect ± 1.96·|SE| of source crb_papers.q104.gold (closed, evaluate_result.py; no --auto-rescale)
   • ✓
     #2figura da resposta próxima

     replication cell: the gold effect to rel 1e-6 (abs floor 1e-6·SE); never required
   • ✗
     #3figura da resposta próxima

     replication cell: the gold SE to rel 1e-6; never required

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 gubernatorial elections decided by very small margins, what is the causal effect of a Democratic (vs. Republican) gubernatorial victory on the subsequent midterm change in the Democratic share of lower-house seats?

   ds_0070 (agent)

   | Variable Name | Description | |----------------|------------------------------------------------------------------------------------------------------------------------------------------| | Unnamed: 0 | - | | dem_lhpct_chng | Change in the Democratic share of seats in the state lower house between the prior gubernatorial election year and the subsequent midterm election (percentage points, two‑party share). | | D_1 | Democratic gubernatorial vote margin from 50 percent in the previous gubernatorial election (percentage points; positive if the Democrat won, negative if the Republican won). | Data Description: This file is a pared‑down extract constructed to study midterm seat swings in U.S. state lower‑house elections following gubernatorial contests. Each row corresponds to a state midterm legislative election year, and variables record (a) how the Democratic two‑party seat share changed from the last cycle and (b) the Democratic win/lose margin in the immediately preceding gubernatorial election. The coverage used in the original study spans 1882–2010, focusing on lower chambers because most upper chambers are staggered and often small. Cases with nonpartisan legislatures or strong third‑party dominance were excluded, and the analysis centers on years when the governor was not on the ballot but the lower house was. The data were assembled from historical administrative sources: state legislative seat counts from Dubin (2007) with recent updates from the National Conference of State Legislatures, and gubernatorial election returns from ICPSR compilations and state election officials. The extract you have contains just the variables required to reproduce the core state‑level midterm seat‑change analysis.

   file 01092928e739dab8806446231e17fda1cdda1ae702086f7a5eb1dc65e69b17d0 · frame —

   ref_window (reference)

   file 01092928e739dab8806446231e17fda1cdda1ae702086f7a5eb1dc65e69b17d0 · frame —

   1. select {"columns":["dem_lhpct_chng","D_1"]}
   2. dropna {"columns":["dem_lhpct_chng","D_1"]}
   3. filter {"query":"D_1 >= -3 and D_1 <= 3"}
2. 1Transformação
   ref_window · select {"columns":["dem_lhpct_chng","D_1"]}

   873 → 873 linhas

   Frame antes → depois: c926fb8fe6ff → c926fb8fe6ff
3. 2Transformação
   ref_window · dropna {"columns":["dem_lhpct_chng","D_1"]}

   873 → 873 linhas

   Frame antes → depois: c926fb8fe6ff → c926fb8fe6ff
4. 3Transformação
   ref_window · filter {"query":"D_1 >= -3 and D_1 <= 3"}

   873 → 288 linhas

   Frame antes → depois: c926fb8fe6ff → 8c5d56cb4e2f
5. 4Turno do modelo
   call_896d3412referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   rdrobust(y_w, x_w, c=0.0, p=1, kernel='triangular', h=h) with h = 3; the script prints the first (Conventional) entry of coef: the local linear jump at h

   Chamadas pedidas

   • infer.rdd

     {
       "bandwidth": 3,
       "cutoff": 0,
       "dataset": "ref_window",
       "kernel": "triangular",
       "polynomial": 1,
       "running": "D_1",
       "y": "dem_lhpct_chng"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
6. 5Ferramenta
   infer.rdd— Regressão descontínua (RDD)v0.1.0call_fe607a26decidido pelo modeloreexecutável

   Motivo: rdrobust(y_w, x_w, c=0.0, p=1, kernel='triangular', h=h) with h = 3; the script prints the first (Conventional) entry of coef: the local linear jump at h

   Argumentos

   {
     "bandwidth": 3,
     "cluster": null,
     "cutoff": 0,
     "dataset": "ref_window",
     "kernel": "triangular",
     "level": 0.95,
     "polynomial": 1,
     "running": "D_1",
     "subset": null,
     "y": "dem_lhpct_chng"
   }

   Figuras call_fe607a26

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in dem_lhpct_chng at the cutoff of D_1	-4,30662	-4.306620864874279	3,308613.3086084550749453	-10,7914 – 2,17813

   Diagnósticos

   • bandwidth = 33 (half-width of the window around the cutoff (informada))
   • bandwidth_method = "user"
   • n_left = 135135 (observations with positive weight below the cutoff)
   • n_right = 153153 (observations with positive weight at or above the cutoff)
   • kernel = "triangular"
   • polynomial = 11
   • bandwidth_sensitive = false (the estimates at half and double the bandwidth fall inside the main interval)
   • n_obs = 288288 (rows used to choose the bandwidth)
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))

   Derivação matemática e pressupostos

   effect

   1. Ajuste linear ponderado (kernel triangular) de cada lado do limiar, com janela h (informada); o efeito é a diferença entre os dois ajustes no limiar.→ -4,30662 (-4.306620864874279)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 3,30861 (3.3086084550749453)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição normal vezes o erro-padrão.IC = τ ± c · ep

   Pressupostos do estimador

   • units cannot precisely control the running variable around the cutoff
   • every other determinant of the outcome is continuous at the cutoff

   Tabelas (1)

   {
     "sensitivity": [
       {
         "bandwidth": 1.5,
         "ci_high": 3.6553192418019576,
         "ci_low": -14.130563523874493,
         "estimate": -5.237622141036268,
         "multiplier": 0.5,
         "n_left": 68,
         "n_right": 84,
         "se": 4.537298365166204
       },
       {
         "bandwidth": 3,
         "ci_high": 2.1781325460173226,
         "ci_low": -10.791374275765882,
         "estimate": -4.306620864874279,
         "multiplier": 1,
         "n_left": 135,
         "n_right": 153,
         "se": 3.3086084550749453
       },
       {
         "bandwidth": 6,
         "ci_high": 1.4668752621785188,
         "ci_low": -10.775463519995682,
         "estimate": -4.654294128908582,
         "multiplier": 2,
         "n_left": 135,
         "n_right": 153,
         "se": 3.123102995447929
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   ref_window 8c5d56cb4e2f7481a9bbb43f52633434e32a955dbbaf2393fc615b11c6bfd0e0

   sha256 do resultado

   7f0baf1aa9c9022050c8b139139e12210927ff886dae2415380f1b92f419b37b

   Resultado mostrado ao modelo
7. 6Turno do modelo
   call_86096b0ereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.rdd): effect = -4,307. Reference path transcribed from the item's own estimation script.",
       "caveats": [],
       "figure_refs": [
         "call_fe607a26:effect"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
8. 7Guarda
   resposta aceita

   Números encontrados

   -4,307

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
9. 8Resposta

   Estimativa (infer.rdd): effect = -4,307. Reference path transcribed from the item's own estimation script.

   Figuras citadas: call_fe607a26:effect · Parada: answered

   Figura da resposta: call_fe607a26:effect = -4,30662-4.306620864874279 (se 3,308613.3086084550749453)
10. 9Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
11. 10Pontuação
    #1figura da resposta próximapassouobrigatória-4.306620865 in [-10.96064324, 2.347401511]?
12. 11Pontuação
    #2figura da resposta próximapassouregistrada-4.306620865 vs -4.306620864874278
13. 12Pontuação
    #3figura da resposta próximafalhouregistrada3.308608455 vs 3.3949093756820687

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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