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Tarefa paper_repro.mmv_2011.tab20_1_c1.agent

paper_reproagentT2publicépoca 1, tentativa 1Execução: 20261015T165744Z-suite-paper_repro-scripted-def9df

Veredito

passouPassou em todas as verificações obrigatórias.

Família do método (das chamadas de ferramentas): rd (regression discontinuity)

Pergunta feita ao analista

Manacorda, Miguel and Vigorito (2011), on Uruguay's PANES cash transfers, as replicated in Huntington-Klein's 'The Effect', chapter 20, Table 20.1, column 'Quadratic': households qualified for Uruguay's PANES cash transfers when their income measure fell below the eligibility cutoff, so treatment is on the low-income side of the cutoff ('you need low income to get paid'). Estimate, as a sharp regression discontinuity at the cutoff, the effect of receiving the payments on the household's backing for the government: fit a second-order polynomial in income on each side of the cutoff using every household in the extract (all lie within 0.02 of the cutoff), with no kernel weighting. Report the effect of being on the eligible side of the cutoff, with a positive sign meaning more backing among the eligible households.

Resposta

Estimativa (infer.rdd): effect = 0,09286.

Valores esperados e de onde vêm

mmv_2011.tab20_1_c1.software

Fonte
statsmodels 0.15.0: the reference specification of mmv_2011 tab20_1_c1 (infer.rdd, figure 'effect') on an independent code path
Tipo
software
Localização
mmv_2011: Table 20.1 (The Effect of Government Payments on Support, Regression Discontinuity Estimates), column 'Quadratic', row 'Participation'
Valor
0,09285530.09285534103880216 (se 0,04363360.04363357378599472)
Conferido por
execução de software · sm.OLS(Support, [const, treat, d·(1-treat), d·treat, squares]).fit(cov_type='HC1') with d = Income_Below - 0, rows |d| <= 0.02, treat = d >= 0 on gov_transfers (the file pinned at sha256 51312ecd5b3cb22b5dc027966a81e0a8579f24a708ce4b476674fc527652ebd0, after its 1 declared transforms); n_obs = 1948, n_left = 821, n_right = 1127. The tool infer.rdd (magi_infer, numpy) does not call statsmodels. Note: the strict rule (|d| < h, treated when d > 0) selects the same rows and treatment. Written by scripts/paper_repro_reference.py.
Implementação
independente da ferramenta
Endereço
https://pypi.org/project/statsmodels/0.15.0/
  • #10 figura da resposta próxima: 0,09285530.09285534103880216 · relativa 0.000001 · absoluta 4.363357378599472e-8
  • #11 figura da resposta próxima: 0,04363360.04363357378599472 · relativa 0.000001 · absoluta 4.363357378599472e-8

mmv_2011.tab20_1_c1.paper

Fonte
Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. CRC Press; online edition, chapter 20 (Regression Discontinuity), replicating Manacorda, Miguel and Vigorito (2011), AEJ: Applied 3(3): 1-28.
Tipo
textbook
Localização
Table 20.1 (The Effect of Government Payments on Support, Regression Discontinuity Estimates), column 'Quadratic', row 'Participation'
Página
online edition, chapter 20, Table 20.1
Trecho
Participation 0.093** (0.044)
Valor como publicado
0.093
Erro-padrão como publicado
0.044
Valor
0,0930.093 (se 0,0440.044) support for the government (0, 0.5, 1)
Conferido por
fonte primária · Read on 2026-10-15 from the free online edition of The Effect (theeffectbook.net, chapter 20); read_copy_sha256 is the sha256 of the chapter's decompressed HTML as served that day. Table 20.1 is a modelsummary table of the R code printed above it: Support on Income_Centered*Participation plus I(Income_Centered^2)*Participation (column 'Quadratic', all 1,948 households) and Support on Income_Centered*Participation weighted by max(0, 1 - 100|Income_Centered|) (column 'Linear with Kernel Weight', Num.Obs. 937); the table's last rows state 'Std.Errors HC3 HC3'. Data check made before any estimate: Participation equals (Income_Centered <= 0) on every row, no row has Income_Centered exactly 0 or exactly 0.01 in absolute value, every |Income_Centered| is below 0.02, and 937 rows lie strictly within 0.01 of the cutoff (400 above it, 537 below), as the table's Num.Obs.
sha256 da cópia lida
b6f751cfa056dc32718bdd79f6b25518a6acd9e9923a8fd12ef29ac9342413eb (identifica a cópia lida, não um arquivo canônico)
Desvio conhecido
convenção de erro-padrão
Recorded before this track's software-reference run and before any dry run, from the printed table: Table 20.1 states that its standard errors are HC3, while infer.rdd computes HC1 (its only unclustered option) and the reference follows the tool's declared HC1. The printed cell is recorded and non-required on T1.
Endereço
https://theeffectbook.net/ch-RegressionDiscontinuity.html
Licença
Copyright Nick Huntington-Klein (The Effect, CRC Press); cited, not reproduced.
  • #12 figura da resposta próxima: 0,0930.093 · published_cell d=3 · absoluta 0.000500001
  • #13 figura da resposta próxima: 0,0440.044 · published_cell d=3 (the SE as printed) · absoluta 0.000500001

Verificações

Cada verificação, o que esperava, o que observou e a chamada que leu
#VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
0resposta com lastro
every number backed
obrigatóriapassou—[]———
1figura da resposta escolhida
answer figure call_8909e132:effect (infer.rdd)
obrigatóriapassou—"call_8909e132:effect"——call_8909e132
2ferramenta chamada
answer call is infer.rdd
obrigatóriapassou—"infer.rdd"——call_8909e132
3argumento igual
infer.rdd.y = 'Support'
obrigatóriapassou"Support""Support"——call_8909e132
4argumento igual
infer.rdd.running = 'Income_Below'
obrigatóriapassou"Income_Below""Income_Below"——call_8909e132
5argumento igual
infer.rdd.cutoff = 0.0
obrigatóriapassou0000——call_8909e132
6argumento igual
infer.rdd.kernel = 'uniform'
obrigatóriapassou"uniform""uniform"——call_8909e132
7argumento igual
infer.rdd.polynomial = 2
obrigatóriapassou2222——call_8909e132
8diagnóstico
821 vs 821
obrigatóriapassou821821821821default rel 1e-6 (abs floor 1e-6)—call_8909e132
9diagnóstico
1127 vs 1127
obrigatóriapassou1.12711271.1271127default rel 1e-6 (abs floor 1e-6)—call_8909e132
10figura da resposta próxima
0.09285534104 vs 0.09285534103880216
obrigatóriapassou0,09285530.092855341038802160,09285530.09285534103880423relativa 0.000001 · absoluta 4.363357378599472e-8mmv_2011.tab20_1_c1.softwarecall_8909e132
11figura da resposta próxima
0.04363357379 vs 0.04363357378599472
registradapassou0,04363360.043633573785994720,04363360.04363357378600273relativa 0.000001 · absoluta 4.363357378599472e-8mmv_2011.tab20_1_c1.softwarecall_8909e132
12figura da resposta próxima
0.09285534104 vs 0.093
registradapassou0,0930.0930,09285530.09285534103880423published_cell d=3 · absoluta 0.000500001mmv_2011.tab20_1_c1.papercall_8909e132
13figura da resposta próxima
0.04363357379 vs 0.044
registradapassou0,0440.0440,04363360.04363357378600273published_cell d=3 (the SE as printed) · absoluta 0.000500001mmv_2011.tab20_1_c1.papercall_8909e132

Caminho de referência e caminho do agente

A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

a tarefa T1 do mesmo alvo (paper_repro.mmv_2011.tab20_1_c1.tool)

Referência: infer.rddChamada do agente: infer.rdd (call_8909e132)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
bandwidth0,020.020,020.02igual
cluster——não fixado pela referência
cutoff0000igual
dataset"gov_transfers""gov_transfers"igual
kernel"uniform""uniform"igual
level—0,950.95não fixado pela referência
polynomial2222igual
running"Income_Below""Income_Below"igual
subset——não fixado pela referência
y"Support""Support"igual

Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

  1. 0Início

    Manacorda, Miguel and Vigorito (2011), on Uruguay's PANES cash transfers, as replicated in Huntington-Klein's 'The Effect', chapter 20, Table 20.1, column 'Quadratic': households qualified for Uruguay's PANES cash transfers when their income measure fell below the eligibility cutoff, so treatment is on the low-income side of the cutoff ('you need low income to get paid'). Estimate, as a sharp regression discontinuity at the cutoff, the effect of receiving the payments on the household's backing for the government: fit a second-order polynomial in income on each side of the cutoff using every household in the extract (all lie within 0.02 of the cutoff), with no kernel weighting. Report the effect of being on the eligible side of the cutoff, with a positive sign meaning more backing among the eligible households.

    gov_transfers (agent)

    Households near the income cutoff of Uruguay's PANES cash-transfer programme, the extract of the survey of Manacorda, Miguel and Vigorito (2011), 'Government Transfers and Political Support', distributed with the causaldata R package and used in chapter 20 of Huntington-Klein's 'The Effect': one row per household (1,948 rows), only households whose income measure is within 0.02 of the cutoff. Households below the cutoff were eligible for the transfers. Columns: rownames, the row number; Income_Centered, the household's income measure minus the eligibility cutoff (negative values are below the cutoff, hence eligible); Income_Below, minus Income_Centered, added by the task author: how far below the cutoff the household's income measure is (positive values are eligible); Education, average years of education of household members aged 16 and older (missing for some households); Age, average age of household members; Participation, 1 if the household participated in the transfer programme; Support, the household's support for the government (0, 0.5 or 1; higher means more support).

    file 51312ecd5b3cb22b5dc027966a81e0a8579f24a708ce4b476674fc527652ebd0 · frame —
    1. derive {"args":{"column":"Income_Centered"},"name":"Income_Below","op":"negate"}
  2. 1Transformação
    gov_transfers · derive {"args":{"column":"Income_Centered"},"name":"Income_Below","op":"negate"}
    1948 → 1948 linhas
    Frame antes → depois: 2600b9da8866 → ee25bc867239
  3. 2Turno do modelo
    call_7541f9e1referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    Table 20.1, column 'Quadratic': OLS of support on participation with a quadratic in centred income interacted with participation, on every household of the extract (all lie within 0.02 of the cutoff), unweighted. The treated side is below the cutoff, so the running variable is the declared negation Income_Below and treatment is Income_Below >= 0 (Participation equals that indicator on every row).

    Chamadas pedidas
    • infer.rdd
      {
        "bandwidth": 0.02,
        "cutoff": 0,
        "dataset": "gov_transfers",
        "kernel": "uniform",
        "polynomial": 2,
        "running": "Income_Below",
        "y": "Support"
      }
  4. 3Ferramenta
    infer.rddv0.1.0call_8909e132decidido pelo modeloreexecutável

    Motivo: Table 20.1, column 'Quadratic': OLS of support on participation with a quadratic in centred income interacted with participation, on every household of the extract (all lie within 0.02 of the cutoff), unweighted. The treated side is below the cutoff, so the running variable is the declared negation Income_Below and treatment is Income_Below >= 0 (Participation equals that indicator on every row).

    Argumentos
    {
      "bandwidth": 0.02,
      "cluster": null,
      "cutoff": 0,
      "dataset": "gov_transfers",
      "kernel": "uniform",
      "level": 0.95,
      "polynomial": 2,
      "running": "Income_Below",
      "subset": null,
      "y": "Support"
    }
    Figuras call_8909e132
    ChaveValorValor brutoErro-padrãoIC
    effect
    Jump in Support at the cutoff of Income_Below
    0,0928553 0.092855341038804230,04363360.043633573786002730,00733511 – 0,178376
    Diagnósticos
    • bandwidth = 0,020.02
    • bandwidth_method = "user"
    • n_left = 821821
    • n_right = 1.1271127
    • kernel = "uniform"
    • polynomial = 22
    • bandwidth_sensitive = true
    • n_obs = 1.9481948
    • se_type = "HC1"
    Tabelas (1)
    {
      "sensitivity": [
        {
          "bandwidth": 0.01,
          "ci_high": 0.09793649228155744,
          "ci_low": -0.16157308291363565,
          "estimate": -0.031818295316039094,
          "multiplier": 0.5,
          "n_left": 400,
          "n_right": 537,
          "se": 0.06620263873269394
        },
        {
          "bandwidth": 0.02,
          "ci_high": 0.17837557417614058,
          "ci_low": 0.007335107901467866,
          "estimate": 0.09285534103880423,
          "multiplier": 1,
          "n_left": 821,
          "n_right": 1127,
          "se": 0.04363357378600273
        },
        {
          "bandwidth": 0.04,
          "ci_high": 0.17837557417614058,
          "ci_low": 0.007335107901467866,
          "estimate": 0.09285534103880423,
          "multiplier": 2,
          "n_left": 821,
          "n_right": 1127,
          "se": 0.04363357378600273
        }
      ]
    }
    Digests dos dados
    gov_transfers ee25bc8672392d74aa124b1f295a152c7535bd2d306ed08a52b235e4ddbe5113
    sha256 do resultado
    ec08212c9f06197e2eb4243e543e4536e89b1ea420de1c945ac1d608c0e1d752
  5. 4Turno do modelo
    call_db7f5779referenceFim: 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 = 0,09286.",
        "caveats": [],
        "figure_refs": [
          "call_8909e132:effect"
        ]
      }
  6. 5Guarda
    resposta aceita
    Números encontrados
    0,09286
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  7. 6Resposta

    Estimativa (infer.rdd): effect = 0,09286.

    Figuras citadas: call_8909e132:effect · Parada: answered
    Figura da resposta: call_8909e132:effect = 0,09285530.09285534103880423 (se 0,04363360.04363357378600273)
  8. 7Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  9. 8Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_8909e132:effect (infer.rdd)
  10. 9Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.rdd
  11. 10Pontuação
    #3argumento igualpassouobrigatóriainfer.rdd.y = 'Support'
  12. 11Pontuação
    #4argumento igualpassouobrigatóriainfer.rdd.running = 'Income_Below'
  13. 12Pontuação
    #5argumento igualpassouobrigatóriainfer.rdd.cutoff = 0.0
  14. 13Pontuação
    #6argumento igualpassouobrigatóriainfer.rdd.kernel = 'uniform'
  15. 14Pontuação
    #7argumento igualpassouobrigatóriainfer.rdd.polynomial = 2
  16. 15Pontuação
    #8diagnósticopassouobrigatória821 vs 821
  17. 16Pontuação
    #9diagnósticopassouobrigatória1127 vs 1127
  18. 17Pontuação
    #10figura da resposta próximapassouobrigatória0.09285534104 vs 0.09285534103880216
  19. 18Pontuação
    #11figura da resposta próximapassouregistrada0.04363357379 vs 0.04363357378599472
  20. 19Pontuação
    #12figura da resposta próximapassouregistrada0.09285534104 vs 0.093
  21. 20Pontuação
    #13figura da resposta próximapassouregistrada0.04363357379 vs 0.044

Épocas × verificações

Resultado de cada verificação em cada época pontuada
#012345678910111213
Época 1passoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassoupassou

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