Tarefa paper_repro.lmb_2004.tab1_c1.agent

Veredito

Pergunta feita ao analista

Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', Table I, column (1), the total effect gamma: in the paper's close-elections sample, the House elections at time t in which the Democratic candidate's share of the two-party vote was strictly between 48 and 52 percent, estimate the difference in the average adjusted ADA voting index of the district's representative at t+1 between districts where the Democratic candidate's share at t was strictly between 50 and 52 percent and districts where it was strictly between 48 and 50 percent. Estimate it as the coefficient of a regression of the t+1 ADA index on an indicator that the Democratic candidate won at t, with no other regressors. Report that difference.

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
   lmb— U.S. House elections and roll-call voting, the extract of the data of Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', distributed with the causaldata R package: one row per district and congressional session (13,588 rows). Each row pairs a district's election with the one before it for the same seat. Columns: rownames, the row number; state, ICPSR state code; district, district code; id, an election identifier; score, the adjusted ADA voting score of the district's representative in the session that follows the row's election (0 to 100, higher means a more liberal roll-call record); year, year of the election; demvoteshare, the Democratic candidate's share of the two-party vote in the row's election; democrat, 1 if the Democratic candidate won the row's election; lagdemvoteshare, the Democratic share of the two-party vote in the previous election for the same seat; lagdemocrat, 1 if the Democratic candidate won that previous election. In the paper's notation the previous election is time t and the row's election, and the session after it, are time t+1.
2. 2Chamada que gerou a resposta finalinfer.rdd(Regressão descontínua (RDD))citada na resposta

   Ver chamada call_1c9a4a69 nos detalhes

   O tratamento muda num limiar da variável de corte: regressão descontínua.

   Por que este é o caminho de referência: Table I, column (1): the difference in mean ADA score at t+1 between districts the Democrat won and lost at t, among elections with the Democratic share at t strictly between 48 and 52 percent (915 observations), as the coefficient of a regression on the win indicator; heteroskedasticity-robust errors, which equal the Mixtape replication's errors clustered by election id (id is unique in this window).
   Argumentos principais

   cutoff:

   00

   dataset:

   "lmb"

   kernel:

   "triangular"

   level:

   0,950.95

   polynomial:

   11

   running:

   "score"

   y:

   "rownames"

   Resultado calculado

   • effect = 279,165 279.16473129771043
     Jump in rownames at the cutoff of score
   Diagnósticos: bandwidth = 14,208614.208597376322317 (half-width of the window around the cutoff (Imbens–Kalyanaraman)) · bandwidth_method = "IK" · n_left = 758758 (observations with positive weight below the cutoff) · n_right = 3.1803180 (observations with positive weight at or above the cutoff) · kernel = "triangular" · polynomial = 11 · bandwidth_sensitive = true (the estimate at half or double the bandwidth falls outside the main interval: the result depends on the window) · n_obs = 13.58813588 (rows used to choose the bandwidth) · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
3. 3Conferência de lastro e resposta final

   Todos os 4 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 279,2 · 95% · -207,4 · 765,7
   Figuras citadas: call_1c9a4a69:effect
   • ✓
     #0resposta com lastro

     Garante que todo número citado no texto veio de uma ferramenta ou do enunciado (sem cálculo mental do modelo).
   • ✓
     #1figura da resposta escolhida

     an answer figure: the effect of an estimator call the answer cites
   • ✗
     #2ferramenta chamadainfer.ols

     Exige que o resultado venha da ferramenta infer.ols.
   • ✗
     #3argumento igualy

     outcome: the ADA score at t+1
   • ✗
     #4argumento igualx

     the regressor: a Democratic win at t
   • ✗
     #5argumento igualcontrols

     column (1) is a difference in means: no other regressors
   • ✗
     #6argumento igualfe

     no fixed effects
   • ✗
     #7diagnósticon_obs

     Confere o diagnóstico "n_obs" reportado pela ferramenta.
   • ✗
     #8figura da resposta próxima

     software reference
   • ✗
     #9figura da resposta próxima

     software reference, SE: recorded, not required (the question does not state the convention)
   • ✗
     #10figura da resposta próxima

     published cell: recorded, never required on T2/T3
   • ✗
     #11figura da resposta próxima

     published cell, SE: recorded, never required on T2/T3

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

   Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', Table I, column (1), the total effect gamma: in the paper's close-elections sample, the House elections at time t in which the Democratic candidate's share of the two-party vote was strictly between 48 and 52 percent, estimate the difference in the average adjusted ADA voting index of the district's representative at t+1 between districts where the Democratic candidate's share at t was strictly between 50 and 52 percent and districts where it was strictly between 48 and 50 percent. Estimate it as the coefficient of a regression of the t+1 ADA index on an indicator that the Democratic candidate won at t, with no other regressors. Report that difference.

   lmb (agent)

   U.S. House elections and roll-call voting, the extract of the data of Lee, Moretti and Butler (2004), 'Do Voters Affect or Elect Policies? Evidence from the U.S. House', distributed with the causaldata R package: one row per district and congressional session (13,588 rows). Each row pairs a district's election with the one before it for the same seat. Columns: rownames, the row number; state, ICPSR state code; district, district code; id, an election identifier; score, the adjusted ADA voting score of the district's representative in the session that follows the row's election (0 to 100, higher means a more liberal roll-call record); year, year of the election; demvoteshare, the Democratic candidate's share of the two-party vote in the row's election; democrat, 1 if the Democratic candidate won the row's election; lagdemvoteshare, the Democratic share of the two-party vote in the previous election for the same seat; lagdemocrat, 1 if the Democratic candidate won that previous election. In the paper's notation the previous election is time t and the row's election, and the session after it, are time t+1.

   file 58cf9d8ef80b2871a96c00844fae975051a5aac443dff3d317fe92ffd6a516b7 · frame —
2. 1Turno do modelo
   call_97a7ea4crules-1Fim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   O tratamento muda num limiar da variável de corte: regressão descontínua.

   Chamadas pedidas

   • infer.rdd

     {
       "cutoff": 0,
       "dataset": "lmb",
       "running": "score",
       "y": "rownames"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.rdd— Regressão descontínua (RDD)v0.1.0call_1c9a4a69decidido pelo modeloreexecutável

   Motivo: O tratamento muda num limiar da variável de corte: regressão descontínua.

   Argumentos

   {
     "bandwidth": null,
     "cluster": null,
     "cutoff": 0,
     "dataset": "lmb",
     "kernel": "triangular",
     "level": 0.95,
     "polynomial": 1,
     "running": "score",
     "subset": null,
     "y": "rownames"
   }

   Figuras call_1c9a4a69

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in rownames at the cutoff of score	279,165	279.16473129771043	248,26248.25990671589568	-207,416 – 765,745

   Diagnósticos

   • bandwidth = 14,208614.208597376322317 (half-width of the window around the cutoff (Imbens–Kalyanaraman))
   • bandwidth_method = "IK"
   • n_left = 758758 (observations with positive weight below the cutoff)
   • n_right = 3.1803180 (observations with positive weight at or above the cutoff)
   • kernel = "triangular"
   • polynomial = 11
   • bandwidth_sensitive = true (the estimate at half or double the bandwidth falls outside the main interval: the result depends on the window)
   • n_obs = 13.58813588 (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 (Imbens–Kalyanaraman); o efeito é a diferença entre os dois ajustes no limiar.→ 279,165 (279.16473129771043)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 248,26 (248.25990671589568)
   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": 7.104298688161158,
         "ci_high": 174.66283692489174,
         "ci_low": -1229.7032357853939,
         "estimate": -527.5201994302511,
         "multiplier": 0.5,
         "n_left": 575,
         "n_right": 1459,
         "se": 358.26323437260737
       },
       {
         "bandwidth": 14.208597376322317,
         "ci_high": 765.7452072661395,
         "ci_low": -207.4157446707186,
         "estimate": 279.16473129771043,
         "multiplier": 1,
         "n_left": 758,
         "n_right": 3180,
         "se": 248.25990671589568
       },
       {
         "bandwidth": 28.417194752644633,
         "ci_high": 878.4954784077543,
         "ci_low": 96.1918375711133,
         "estimate": 487.3436579894338,
         "multiplier": 2,
         "n_left": 819,
         "n_right": 5048,
         "se": 199.57092247800273
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   lmb 6f2fbc93d91c4156cab244ba7581cc8eae45210626f97f85ac8b2d366e4cbc20

   sha256 do resultado

   a1ffae7dea6dae8ab50de035809eb52f93781ed10b367d7405b363b19c3712d8

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_0e69a290rules-1Fim: 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 = 279,2 (IC 95%: -207,4 a 765,7).",
       "caveats": [],
       "figure_refs": [
         "call_1c9a4a69:effect"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   279,2 · 95% · -207,4 · 765,7

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.rdd): effect = 279,2 (IC 95%: -207,4 a 765,7).

   Figuras citadas: call_1c9a4a69:effect · Parada: answered

   Figura da resposta: call_1c9a4a69:effect = 279,165279.16473129771043 (se 248,26248.25990671589568)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_1c9a4a69:effect (infer.rdd)
9. 8Pontuação
   #2ferramenta chamadafalhouobrigatóriaanswer call is infer.rdd
10. 9Pontuação
    #3argumento igualfalhouobrigatóriaanswer call is infer.rdd, not infer.ols
11. 10Pontuação
    #4argumento igualfalhouobrigatóriaanswer call is infer.rdd, not infer.ols
12. 11Pontuação
    #5argumento igualfalhouobrigatóriaanswer call is infer.rdd, not infer.ols
13. 12Pontuação
    #6argumento igualfalhouobrigatóriaanswer call is infer.rdd, not infer.ols
14. 13Pontuação
    #7diagnósticofalhouobrigatóriaanswer call is infer.rdd, not infer.ols
15. 14Pontuação
    #8figura da resposta próximafalhouobrigatória279.1647313 vs 21.283872256260665
16. 15Pontuação
    #9figura da resposta próximafalhouregistrada248.2599067 vs 1.9512339082202543
17. 16Pontuação
    #10figura da resposta próximafalhouregistrada279.1647313 vs 21.2
18. 17Pontuação
    #11figura da resposta próximafalhouregistrada248.2599067 vs 1.9

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1falhoupontuada(exibindo)

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