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Tarefa paper_repro.kessler_roth_2014.t18_2_twfe.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): did (two-way fixed effects)

Pergunta feita ao analista

Kessler and Roth (2014), as replicated in Huntington-Klein's The Effect (2021), chapter 18, Table 18.2 (a textbook replication, not a table of the paper): California switched the organ-donor question on its driver's licence form to active-choice phrasing in July 2011. Using the quarterly state panel from the fourth quarter of 2010 to the first quarter of 2012, estimate the difference-in-differences effect of active-choice phrasing on the share of people registering as organ donors by a two-way fixed effects regression with state and quarter fixed effects, where the treatment is being California in a quarter after the change, with standard errors clustered by state. Report the coefficient on the treatment.

Resposta

Estimativa (infer.twfe): coef.Treated = -0,02246.

Valores esperados e de onde vêm

kessler_roth_2014.t18_2_twfe.software

Fonte
linearmodels 7.0: the reference specification of kessler_roth_2014 t18_2_twfe (infer.twfe, figure 'coef.Treated') on an independent code path
Tipo
software
Localização
kessler_roth_2014: The Effect, Table 18.2 (difference-in-differences estimate of the effect of active-choice phrasing on organ donor rates), row 'Treatment'; a textbook replication of Kessler and Roth (2014)
Valor
-0,022459-0.022458974358974367 (se 0,00601590.0060159007295233465)
Conferido por
execução de software · PanelOLS(Rate, [Treated], entity_effects=True, time_effects=True) indexed by (State, Quarter_Num).fit(cov_type='clustered', cluster_entity=True, group_debias=True, debiased=True, count_effects=False, auto_df=False) on organ_donations (the file pinned at sha256 b758f11dd4f32c404877bc0b79a09b09d6e074318d2f91030060c9adb26fe56a, after its 3 declared transforms); n_obs = 162. The tool infer.twfe (magi_infer, numpy) does not call linearmodels. Note: infer.twfe demeans with numpy (magi_infer.did) and fits with statsmodels, not linearmodels. Written by scripts/paper_repro_reference.py.
Implementação
independente da ferramenta
Endereço
https://pypi.org/project/linearmodels/7.0/
  • #6 figura da resposta próxima: -0,022459-0.022458974358974367 · relativa 0.000001 · absoluta 6.015900729523346e-9
  • #7 figura da resposta próxima: 0,00601590.0060159007295233465 · relativa 0.000001 · absoluta 6.015900729523346e-9

kessler_roth_2014.t18_2_twfe.paper

Fonte
Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality, chapter 18 (Difference-in-Differences), Table 18.2: a textbook replication of Kessler, J. B. and Roth, A. E. (2014), Don't Take 'No' for an Answer: An Experiment with Actual Organ Donor Registrations (NBER Working Paper 20378).
Tipo
textbook
Localização
The Effect, Table 18.2 (difference-in-differences estimate of the effect of active-choice phrasing on organ donor rates), row 'Treatment', column 'Organ Donation Rate'
Página
online edition, chapter 18, section 18.2.1, Table 18.2
Trecho
Treatment −0.022*** (0.006)
Valor como publicado
−0.022
Erro-padrão como publicado
0.006
Valor
-0,022-0.022 (se 0,0060.006) organ donor registration rate (share) per unit of the treatment indicator
Conferido por
fonte primária · Read on 2026-10-15 from the free online edition of The Effect (theeffectbook.net, chapter 18 HTML, section 18.2.1). The table comes from the book's own code, shown beside it: R feols(Rate ~ Treated | State + Quarter, vcov = ~State) on causaldata's organ_donations, Treated = California in Q32011, Q42011 or Q12012 (Stata reghdfe ... vce(cluster state); Python linearmodels PanelOLS with entity and time effects, clustered by entity). The table notes say 'Standard errors clustered at the state level'; Num.Obs. 162. This is a textbook replication of Kessler and Roth (2014), not a table of the paper. Data facts checked before any estimate: 27 states by 6 quarters, 162 rows, no missing values.
sha256 da cópia lida
9bddd00aee9a5748158fb8a636ca1ccccf6c18e097e7cc041798462f4c8830b8 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://theeffectbook.net/ch-DifferenceinDifference.html
Licença
The Effect is copyrighted (CRC Press, 2021; free online edition); cited, not reproduced.
  • #8 figura da resposta próxima: -0,022-0.022 · published_cell d=3 · absoluta 0.000500001
  • #9 figura da resposta próxima: 0,0060.006 · 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_9a04d5da:coef.Treated (infer.twfe)
obrigatóriapassou—"call_9a04d5da:coef.Treated"——call_9a04d5da
2ferramenta chamada
answer call is infer.twfe
obrigatóriapassou—"infer.twfe"——call_9a04d5da
3argumento igual
infer.twfe.y = 'Rate'
obrigatóriapassou"Rate""Rate"——call_9a04d5da
4argumento igual
infer.twfe.treat = 'Treated'
obrigatóriapassou"Treated""Treated"——call_9a04d5da
5diagnóstico
162 vs 162
obrigatóriapassou162162162162default rel 1e-6 (abs floor 1e-6)—call_9a04d5da
6figura da resposta próxima
-0.02245897436 vs -0.022458974358974367
obrigatóriapassou-0,022459-0.022458974358974367-0,022459-0.022458974358974378relativa 0.000001 · absoluta 6.015900729523346e-9kessler_roth_2014.t18_2_twfe.softwarecall_9a04d5da
7figura da resposta próxima
0.00601590073 vs 0.0060159007295233465
obrigatóriapassou0,00601590.00601590072952334650,00601590.006015900729523345relativa 0.000001 · absoluta 6.015900729523346e-9kessler_roth_2014.t18_2_twfe.softwarecall_9a04d5da
8figura da resposta próxima
-0.02245897436 vs -0.022
registradapassou-0,022-0.022-0,022459-0.022458974358974378published_cell d=3 · absoluta 0.000500001kessler_roth_2014.t18_2_twfe.papercall_9a04d5da
9figura da resposta próxima
0.00601590073 vs 0.006
registradapassou0,0060.0060,00601590.006015900729523345published_cell d=3 (the SE as printed) · absoluta 0.000500001kessler_roth_2014.t18_2_twfe.papercall_9a04d5da

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.kessler_roth_2014.t18_2_twfe.tool)

Referência: infer.twfeChamada do agente: infer.twfe (call_9a04d5da)igual
Argumentos da referência e do agente
ArgumentoReferênciaAgente
covariates—[]não fixado pela referência
dataset"organ_donations""organ_donations"igual
level—0,950.95não fixado pela referência
time"Quarter_Num""Quarter_Num"igual
treat"Treated""Treated"igual
unit"State""State"igual
y"Rate""Rate"igual

Trajetória

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

  1. 0Início

    Kessler and Roth (2014), as replicated in Huntington-Klein's The Effect (2021), chapter 18, Table 18.2 (a textbook replication, not a table of the paper): California switched the organ-donor question on its driver's licence form to active-choice phrasing in July 2011. Using the quarterly state panel from the fourth quarter of 2010 to the first quarter of 2012, estimate the difference-in-differences effect of active-choice phrasing on the share of people registering as organ donors by a two-way fixed effects regression with state and quarter fixed effects, where the treatment is being California in a quarter after the change, with standard errors clustered by state. Report the coefficient on the treatment.

    organ_donations (agent)

    The organ donation panel of Kessler and Roth (2014) as distributed in the causaldata package and used in Huntington-Klein's The Effect, chapter 18: one row per U.S. state and quarter, from the fourth quarter of 2010 to the first quarter of 2012 (27 states, 6 quarters). California changed the organ-donor question on its driver's licence form to active-choice phrasing in July 2011 (the third quarter of 2011); the other states did not change theirs. Columns: rownames, the row number; State, the state's name; Quarter, the quarter as text (Q42010, Q12011, Q22011, Q32011, Q42011, Q12012); Quarter_Num, the quarter as a number from 1 (Q42010) to 6 (Q12012); Rate, the organ donor registration rate (the share of people registering as donors); and the book's construction of the treatment: California, 1 for California; After, 1 for the quarters after the change (Quarter_Num above 3: Q32011, Q42011 and Q12012); Treated, California times After.

    file b758f11dd4f32c404877bc0b79a09b09d6e074318d2f91030060c9adb26fe56a · frame —
    1. derive {"args":{"cmp":"eq","column":"State","value":"California"},"name":"California","op":"indicator"}
    2. derive {"args":{"cmp":"gt","column":"Quarter_Num","value":3},"name":"After","op":"indicator"}
    3. derive {"args":{"columns":["California","After"]},"name":"Treated","op":"product"}
  2. 1Transformação
    organ_donations · derive {"args":{"cmp":"eq","column":"State","value":"California"},"name":"California","op":"indicator"}
    162 → 162 linhas
    Frame antes → depois: 66a839324c71 → 20bef437811a
  3. 2Transformação
    organ_donations · derive {"args":{"cmp":"gt","column":"Quarter_Num","value":3},"name":"After","op":"indicator"}
    162 → 162 linhas
    Frame antes → depois: 20bef437811a → fa0dbe02ae97
  4. 3Transformação
    organ_donations · derive {"args":{"columns":["California","After"]},"name":"Treated","op":"product"}
    162 → 162 linhas
    Frame antes → depois: fa0dbe02ae97 → 2c3e5c0fb179
  5. 4Turno do modelo
    call_e3e35afareferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Texto visível

    The Effect, Table 18.2: two-way fixed effects regression of the donor registration rate on Treated with state and quarter fixed effects, errors clustered by state (feols(Rate ~ Treated | State + Quarter, vcov = ~State)).

    Chamadas pedidas
    • infer.twfe
      {
        "dataset": "organ_donations",
        "time": "Quarter_Num",
        "treat": "Treated",
        "unit": "State",
        "y": "Rate"
      }
  6. 5Ferramenta
    infer.twfev0.1.0call_9a04d5dadecidido pelo modeloreexecutável

    Motivo: The Effect, Table 18.2: two-way fixed effects regression of the donor registration rate on Treated with state and quarter fixed effects, errors clustered by state (feols(Rate ~ Treated | State + Quarter, vcov = ~State)).

    Argumentos
    {
      "covariates": [],
      "dataset": "organ_donations",
      "level": 0.95,
      "time": "Quarter_Num",
      "treat": "Treated",
      "unit": "State",
      "y": "Rate"
    }
    Figuras call_9a04d5da
    ChaveValorValor brutoErro-padrãoIC
    coef.Treated
    TWFE coefficient on Treated for Rate
    -0,022459 -0.0224589743589743780,00601590.006015900729523345-0,0342499 – -0,010668
    Diagnósticos
    • staggered = false
    • n_adoption_dates = 11
    • treatment_reversals = false
    • n_units = 2727
    • n_periods = 66
    • n_obs = 162162
    • se_type = "cluster"
    • n_clusters = 2727
    Digests dos dados
    organ_donations 2c3e5c0fb1799ed60b282e41e0f6e4dda1b1a1f9704c871775e013e3a18c8c59
    sha256 do resultado
    b937ff2cbe16b5ff92fbdace7d7d05372556ebddbe2dbf2fe87acd87b2942dbd
  7. 6Turno do modelo
    call_80409208referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
    Chamadas pedidas
    • submit_answer (meta-ferramenta)
      {
        "answer": "Estimativa (infer.twfe): coef.Treated = -0,02246.",
        "caveats": [],
        "figure_refs": [
          "call_9a04d5da:coef.Treated"
        ]
      }
  8. 7Guarda
    resposta aceita
    Números encontrados
    -0,02246
    Sem lastro
    —
    Não citados
    —
    1 nova(s) tentativa(s) restante(s)
  9. 8Resposta

    Estimativa (infer.twfe): coef.Treated = -0,02246.

    Figuras citadas: call_9a04d5da:coef.Treated · Parada: answered
    Figura da resposta: call_9a04d5da:coef.Treated = -0,022459-0.022458974358974378 (se 0,00601590.006015900729523345)
  10. 9Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
  11. 10Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_9a04d5da:coef.Treated (infer.twfe)
  12. 11Pontuação
    #2ferramenta chamadapassouobrigatóriaanswer call is infer.twfe
  13. 12Pontuação
    #3argumento igualpassouobrigatóriainfer.twfe.y = 'Rate'
  14. 13Pontuação
    #4argumento igualpassouobrigatóriainfer.twfe.treat = 'Treated'
  15. 14Pontuação
    #5diagnósticopassouobrigatória162 vs 162
  16. 15Pontuação
    #6figura da resposta próximapassouobrigatória-0.02245897436 vs -0.022458974358974367
  17. 16Pontuação
    #7figura da resposta próximapassouobrigatória0.00601590073 vs 0.0060159007295233465
  18. 17Pontuação
    #8figura da resposta próximapassouregistrada-0.02245897436 vs -0.022
  19. 18Pontuação
    #9figura da resposta próximapassouregistrada0.00601590073 vs 0.006

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Época 1passoupassoupassoupassoupassoupassoupassoupassoupassoupassou

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