Tarefa paper_repro.fujiwara_2015.tab2_pb_valid02_c3.agent

Veredito

Pergunta feita ao analista

Fujiwara, T. (2015), 'Voting Technology, Political Responsiveness, and Infant Health: Evidence from Brazil', Table II, Panel B (placebo tests), column (3): estimate the jump in valid votes as a share of turnout in the 2002 state legislature elections, when every municipality voted electronically at the threshold of 40,500 registered voters in 1996, above which municipalities voted electronically in the 1998 elections, as a sharp regression discontinuity. This is a placebo: the 2002 election had no discontinuous assignment. Use a local linear regression on each side of the threshold with a rectangular kernel (unweighted observations), a bandwidth of 5,000 registered voters, and heteroskedasticity-robust standard errors. Report the jump at the 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_de42747f nos detalhes

   Table II, Panel B (placebo tests), column (3): local linear RD at 40,500 voters, rectangular kernel, bandwidth 5,000, 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 (3): local linear RD at 40,500 voters, rectangular kernel, bandwidth 5,000, robust errors (munic.do: reg y treat dep deptreat if bw<h, rob).
   Argumentos principais

   bandwidth:

   5.0005000

   cutoff:

   40.50040500

   dataset:

   "munic"

   kernel:

   "uniform"

   level:

   0,950.95

   polynomial:

   11

   running:

   "voters96"

   y:

   "r_util02"

   Resultado calculado

   • effect = 0,00877587 0.008775869231923777
     Jump in r_util02 at the cutoff of voters96
   Diagnósticos: bandwidth = 5.0005000 (half-width of the window around the cutoff (informada)) · bandwidth_method = "user" · n_left = 7575 (observations with positive weight below the cutoff) · n_right = 4141 (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: 0,008776
   Figuras citadas: call_de42747f: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

     outcome
   • ✓
     #4argumento igualrunning

     running variable: registered voters, 1996
   • ✓
     #5argumento igualcutoff

     the electronic-voting threshold
   • ✓
     #6argumento igualbandwidth

     the paper's bandwidth for this column
   • ✓
     #7argumento igualkernel

     rectangular kernel: unweighted observations
   • ✓
     #8argumento igualpolynomial

     local linear
   • ✓
     #9diagnósticon_left

     Confere o diagnóstico "n_left" reportado pela ferramenta.
   • ✓
     #10diagnósticon_right

     Confere o diagnóstico "n_right" reportado pela ferramenta.
   • ✓
     #11figura da resposta próxima

     software reference
   • ✓
     #12figura da resposta próxima

     software reference, SE (the question states the convention)
   • ✓
     #13figura da resposta próxima

     published cell: recorded, never required on T2/T3
   • ✓
     #14figura 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

   Fujiwara, T. (2015), 'Voting Technology, Political Responsiveness, and Infant Health: Evidence from Brazil', Table II, Panel B (placebo tests), column (3): estimate the jump in valid votes as a share of turnout in the 2002 state legislature elections, when every municipality voted electronically at the threshold of 40,500 registered voters in 1996, above which municipalities voted electronically in the 1998 elections, as a sharp regression discontinuity. This is a placebo: the 2002 election had no discontinuous assignment. Use a local linear regression on each side of the threshold with a rectangular kernel (unweighted observations), a bandwidth of 5,000 registered voters, and heteroskedasticity-robust standard errors. Report the jump at the 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_3454fd13referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Table II, Panel B (placebo tests), column (3): local linear RD at 40,500 voters, rectangular kernel, bandwidth 5,000, robust errors (munic.do: reg y treat dep deptreat if bw<h, rob).

   Chamadas pedidas

   • infer.rdd

     {
       "bandwidth": 5000,
       "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_de42747fdecidido pelo modeloreexecutável

   Motivo: Table II, Panel B (placebo tests), column (3): local linear RD at 40,500 voters, rectangular kernel, bandwidth 5,000, robust errors (munic.do: reg y treat dep deptreat if bw<h, rob).

   Argumentos

   {
     "bandwidth": 5000,
     "cluster": null,
     "cutoff": 40500,
     "dataset": "munic",
     "kernel": "uniform",
     "level": 0.95,
     "polynomial": 1,
     "running": "voters96",
     "subset": null,
     "y": "r_util02"
   }

   Figuras call_de42747f

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Jump in r_util02 at the cutoff of voters96	0,00877587	0.008775869231923777	0,009675660.009675663405402189	-0,0101881 – 0,0277398

   Diagnósticos

   • bandwidth = 5.0005000 (half-width of the window around the cutoff (informada))
   • bandwidth_method = "user"
   • n_left = 7575 (observations with positive weight below the cutoff)
   • n_right = 4141 (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.→ 0,00877587 (0.008775869231923777)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,00967566 (0.009675663405402189)
   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": 2500,
         "ci_high": 0.034536574423728136,
         "ci_low": -0.015498242521131338,
         "estimate": 0.009519165951298397,
         "multiplier": 0.5,
         "n_left": 43,
         "n_right": 18,
         "se": 0.012764218460014501
       },
       {
         "bandwidth": 5000,
         "ci_high": 0.02773982103304424,
         "ci_low": -0.010188082569196684,
         "estimate": 0.008775869231923777,
         "multiplier": 1,
         "n_left": 75,
         "n_right": 41,
         "se": 0.009675663405402189
       },
       {
         "bandwidth": 10000,
         "ci_high": 0.020505812849139788,
         "ci_low": -0.004757263923387784,
         "estimate": 0.007874274462876002,
         "multiplier": 2,
         "n_left": 151,
         "n_right": 78,
         "se": 0.006444780866332111
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   munic 4849fb53b02d5e1e529b6e802d6e90e871843cf5f89da8ad229cd022bbd01f2e

   sha256 do resultado

   4b197971b22822a79aec6076bb0b3d868ad371c8d0a79e8b0b9e99e8a5f35b0f

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_a234bf68referenceFim: 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 = 0,008776.",
       "caveats": [],
       "figure_refs": [
         "call_de42747f:effect"
       ]
     }

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

   Números encontrados

   0,008776

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.rdd): effect = 0,008776.

   Figuras citadas: call_de42747f:effect · Parada: answered

   Figura da resposta: call_de42747f:effect = 0,008775870.008775869231923777 (se 0,009675660.009675663405402189)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_de42747f: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 igualpassouobrigatóriainfer.rdd.bandwidth = 5000.0
14. 13Pontuação
    #7argumento igualpassouobrigatóriainfer.rdd.kernel = 'uniform'
15. 14Pontuação
    #8argumento igualpassouobrigatóriainfer.rdd.polynomial = 1
16. 15Pontuação
    #9diagnósticopassouobrigatória75 vs 75
17. 16Pontuação
    #10diagnósticopassouobrigatória41 vs 41
18. 17Pontuação
    #11figura da resposta próximapassouobrigatória0.008775869232 vs 0.008775869231919198
19. 18Pontuação
    #12figura da resposta próximapassouobrigatória0.009675663405 vs 0.009675663405398726
20. 19Pontuação
    #13figura da resposta próximapassouregistrada0.008775869232 vs 0.009
21. 20Pontuação
    #14figura da resposta próximapassouregistrada0.009675663405 vs 0.01

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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