Tarefa paper_repro.fujiwara_2015.tab2_pb_valid02_c1.design

Veredito

Pergunta feita ao analista

Once every municipality voted electronically, did municipalities just above 40,500 registered voters in 1996 (the threshold that assigned electronic voting in the 1998 elections) still differ from those just below? Compare valid votes as a share of turnout in the 2002 state legislature elections for municipalities near that threshold.

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
   munic— The author's municipality file for Fujiwara (2015), 'Voting Technology, Political Responsiveness, and Infant Health: Evidence from Brazil': one row per Brazilian municipality (5,281 rows). In the 1998 elections, municipalities with more than 40,500 registered voters in 1996 used electronic voting and the others paper ballots; in 1994 every municipality used paper ballots and in 2002 every one voted electronically. Columns: uf, state; voters96, number of registered voters in 1996; r_util98, r_util94, r_util02, valid votes as a share of turnout in the state legislature elections of 1998, 1994 and 2002; attend, turnout as a share of registered voters (1998); regist, registered voters as a share of population (1998); null_est_share and blank_est_share, null and blank votes as a share of turnout (state legislature, 1998); util_pres_share, util_gov_share, util_fed_share, util_sen_share, valid votes as a share of turnout in the presidential, gubernatorial, federal lower chamber and senate elections (1998); right, vote-weighted party ideology; right_meas, votes for parties with measured ideology over total valid votes; right_state, state average of right; illiter, illiteracy rate (percent); less4 and less8, shares without 4 and without 8 years of schooling; gini, Gini index; income, monthly income; latitude; longitude; urbanization, share of urban population; population91 and population00, population in 1991 and 2000 (thousands). The schooling, income, inequality and urbanization variables come from the 1991 census.
2. 2Chamada que gerou a resposta finalinfer.rdd(Regressão descontínua (RDD))citada na respostacoincide com a referência

   Ver chamada call_a5c6994a nos detalhes

   Table II, Panel B (placebo tests), column (1): local linear RD at 40,500 voters, rectangular kernel, the paper's IK bandwidth 17,204, passed as printed, robust errors (munic.do: reg y treat dep deptreat if bw<h, rob).

   Por que este é o caminho de referência: Table II, Panel B (placebo tests), column (1): local linear RD at 40,500 voters, rectangular kernel, the paper's IK bandwidth 17,204, passed as printed, robust errors (munic.do: reg y treat dep deptreat if bw<h, rob).
   Argumentos principais

   bandwidth:

   17.20417204

   cutoff:

   40.50040500

   dataset:

   "munic"

   kernel:

   "uniform"

   level:

   0,950.95

   polynomial:

   11

   running:

   "voters96"

   y:

   "r_util02"

   Resultado calculado

   • effect = 0,00498551 0.004985505508969281
     Jump in r_util02 at the cutoff of voters96
   Diagnósticos: bandwidth = 17.20417204 (half-width of the window around the cutoff (informada)) · bandwidth_method = "user" · n_left = 320320 (observations with positive weight below the cutoff) · n_right = 117117 (observations with positive weight at or above the cutoff) · kernel = "uniform" · polynomial = 11 · bandwidth_sensitive = false (the estimates at half and double the bandwidth fall inside the main interval) · n_obs = 5.2815281 (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: 0,004986
   Figuras citadas: call_a5c6994a: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.rdd

     Exige que o resultado venha da ferramenta infer.rdd.
   • ✓
     #3argumento igualy

     T3: the outcome the design question implies
   • ✓
     #4argumento igualrunning

     T3: the running the design question implies
   • ✓
     #5argumento igualcutoff

     T3: the cutoff the design question implies
   • ✓
     #6argumento igualy

     outcome (T3: recorded; a design question cannot state it)
   • ✓
     #7argumento igualrunning

     running variable: registered voters, 1996 (T3: recorded; a design question cannot state it)
   • ✓
     #8argumento igualcutoff

     the electronic-voting threshold (T3: recorded; a design question cannot state it)
   • ✓
     #9argumento igualbandwidth

     the paper's bandwidth for this column (T3: recorded; a design question cannot state it)
   • ✓
     #10argumento igualkernel

     rectangular kernel: unweighted observations (T3: recorded; a design question cannot state it)
   • ✓
     #11argumento igualpolynomial

     local linear (T3: recorded; a design question cannot state it)
   • ✓
     #12diagnósticon_left

     (T3: recorded; a design question cannot state it)
   • ✓
     #13diagnósticon_right

     (T3: recorded; a design question cannot state it)
   • ✓
     #14figura da resposta próxima

     software reference (T3: recorded; T3 pontua o desenho; a estimativa é registrada)
   • ✓
     #15figura da resposta próxima

     software reference, SE: recorded (T3)
   • ✓
     #16figura da resposta próxima

     published cell: recorded, never required on T2/T3
   • ✓
     #17figura 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

   Once every municipality voted electronically, did municipalities just above 40,500 registered voters in 1996 (the threshold that assigned electronic voting in the 1998 elections) still differ from those just below? Compare valid votes as a share of turnout in the 2002 state legislature elections for municipalities near that threshold.

   munic (agent)

   The author's municipality file for Fujiwara (2015), 'Voting Technology, Political Responsiveness, and Infant Health: Evidence from Brazil': one row per Brazilian municipality (5,281 rows). In the 1998 elections, municipalities with more than 40,500 registered voters in 1996 used electronic voting and the others paper ballots; in 1994 every municipality used paper ballots and in 2002 every one voted electronically. Columns: uf, state; voters96, number of registered voters in 1996; r_util98, r_util94, r_util02, valid votes as a share of turnout in the state legislature elections of 1998, 1994 and 2002; attend, turnout as a share of registered voters (1998); regist, registered voters as a share of population (1998); null_est_share and blank_est_share, null and blank votes as a share of turnout (state legislature, 1998); util_pres_share, util_gov_share, util_fed_share, util_sen_share, valid votes as a share of turnout in the presidential, gubernatorial, federal lower chamber and senate elections (1998); right, vote-weighted party ideology; right_meas, votes for parties with measured ideology over total valid votes; right_state, state average of right; illiter, illiteracy rate (percent); less4 and less8, shares without 4 and without 8 years of schooling; gini, Gini index; income, monthly income; latitude; longitude; urbanization, share of urban population; population91 and population00, population in 1991 and 2000 (thousands). The schooling, income, inequality and urbanization variables come from the 1991 census.

   file 5cd5b8796355e025807dc303263b1fbb16f442c3c7551f14ee3557768a310894 · frame —
2. 1Turno do modelo
   call_2ce2e2edreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Table II, Panel B (placebo tests), column (1): local linear RD at 40,500 voters, rectangular kernel, the paper's IK bandwidth 17,204, passed as printed, robust errors (munic.do: reg y treat dep deptreat if bw<h, rob).

   Chamadas pedidas

   • infer.rdd

     {
       "bandwidth": 17204,
       "cutoff": 40500,
       "dataset": "munic",
       "kernel": "uniform",
       "polynomial": 1,
       "running": "voters96",
       "y": "r_util02"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.rdd— Regressão descontínua (RDD)v0.1.0call_a5c6994adecidido pelo modeloreexecutável

   Motivo: Table II, Panel B (placebo tests), column (1): local linear RD at 40,500 voters, rectangular kernel, the paper's IK bandwidth 17,204, passed as printed, robust errors (munic.do: reg y treat dep deptreat if bw<h, rob).

   Argumentos

   {
     "bandwidth": 17204,
     "cluster": null,
     "cutoff": 40500,
     "dataset": "munic",
     "kernel": "uniform",
     "level": 0.95,
     "polynomial": 1,
     "running": "voters96",
     "subset": null,
     "y": "r_util02"
   }

   Figuras call_a5c6994a

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in r_util02 at the cutoff of voters96	0,00498551	0.004985505508969281	0,005148760.005148763147683567	-0,00510588 – 0,0150769

   Diagnósticos

   • bandwidth = 17.20417204 (half-width of the window around the cutoff (informada))
   • bandwidth_method = "user"
   • n_left = 320320 (observations with positive weight below the cutoff)
   • n_right = 117117 (observations with positive weight at or above the cutoff)
   • kernel = "uniform"
   • polynomial = 11
   • bandwidth_sensitive = false (the estimates at half and double the bandwidth fall inside the main interval)
   • n_obs = 5.2815281 (rows used to choose the bandwidth)
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))

   Derivação matemática e pressupostos

   effect

   1. Ajuste linear ponderado (kernel uniforme) de cada lado do limiar, com janela h (informada); o efeito é a diferença entre os dois ajustes no limiar.→ 0,00498551 (0.004985505508969281)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,00514876 (0.005148763147683567)
   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": 8602,
         "ci_high": 0.026581435895538998,
         "ci_low": -0.002076273289057082,
         "estimate": 0.012252581303240959,
         "multiplier": 0.5,
         "n_left": 125,
         "n_right": 59,
         "se": 0.007310774435307086
       },
       {
         "bandwidth": 17204,
         "ci_high": 0.015076895843356157,
         "ci_low": -0.005105884825417595,
         "estimate": 0.004985505508969281,
         "multiplier": 1,
         "n_left": 320,
         "n_right": 117,
         "se": 0.005148763147683567
       },
       {
         "bandwidth": 34408,
         "ci_high": 0.012027587947156464,
         "ci_low": -0.0019307340380893953,
         "estimate": 0.005048426954533535,
         "multiplier": 2,
         "n_left": 2527,
         "n_right": 172,
         "se": 0.0035608618564798443
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   munic 4849fb53b02d5e1e529b6e802d6e90e871843cf5f89da8ad229cd022bbd01f2e

   sha256 do resultado

   b86d9d4ac9d084f73604ff5a79e6854f625cbb036e041dabbcf976bc4f0203f1

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_34dfe3e2referenceFim: 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 = 0,004986.",
       "caveats": [],
       "figure_refs": [
         "call_a5c6994a:effect"
       ]
     }

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

   Números encontrados

   0,004986

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.rdd): effect = 0,004986.

   Figuras citadas: call_a5c6994a:effect · Parada: answered

   Figura da resposta: call_a5c6994a:effect = 0,004985510.004985505508969281 (se 0,005148760.005148763147683567)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_a5c6994a:effect (infer.rdd)
9. 8Pontuação
   #2ferramenta chamadapassouobrigatóriaanswer call is infer.rdd
10. 9Pontuação
    #3argumento igualpassouobrigatóriainfer.rdd.y = 'r_util02'
11. 10Pontuação
    #4argumento igualpassouobrigatóriainfer.rdd.running = 'voters96'
12. 11Pontuação
    #5argumento igualpassouobrigatóriainfer.rdd.cutoff = 40500.0
13. 12Pontuação
    #6argumento igualpassouregistradainfer.rdd.y = 'r_util02'
14. 13Pontuação
    #7argumento igualpassouregistradainfer.rdd.running = 'voters96'
15. 14Pontuação
    #8argumento igualpassouregistradainfer.rdd.cutoff = 40500.0
16. 15Pontuação
    #9argumento igualpassouregistradainfer.rdd.bandwidth = 17204.0
17. 16Pontuação
    #10argumento igualpassouregistradainfer.rdd.kernel = 'uniform'
18. 17Pontuação
    #11argumento igualpassouregistradainfer.rdd.polynomial = 1
19. 18Pontuação
    #12diagnósticopassouregistrada320 vs 320
20. 19Pontuação
    #13diagnósticopassouregistrada117 vs 117
21. 20Pontuação
    #14figura da resposta próximapassouregistrada0.004985505509 vs 0.0049855055089780415
22. 21Pontuação
    #15figura da resposta próximapassouregistrada0.005148763148 vs 0.005148763147693013
23. 22Pontuação
    #16figura da resposta próximapassouregistrada0.004985505509 vs 0.005
24. 23Pontuação
    #17figura da resposta próximapassouregistrada0.005148763148 vs 0.005

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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