Tarefa paper_repro.kessler_roth_2014.t18_2_twfe.agent
paper_reproagentT2publicépoca 1, tentativa 1Execução: 20261015T165744Z-suite-paper_repro-scripted-def9dfVeredito
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.
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: 0,0060159
0.0060159007295233465· 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: 0,006
0.006· published_cell d=3 (the SE as printed) · absoluta 0.000500001
Verificações
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta escolhida answer figure call_9a04d5da:coef.Treated (infer.twfe) | obrigatória | passou | — | "call_9a04d5da:coef.Treated" | — | — | call_9a04d5da |
| 2 | ferramenta chamada answer call is infer.twfe | obrigatória | passou | — | "infer.twfe" | — | — | call_9a04d5da |
| 3 | argumento igual infer.twfe.y = 'Rate' | obrigatória | passou | "Rate" | "Rate" | — | — | call_9a04d5da |
| 4 | argumento igual infer.twfe.treat = 'Treated' | obrigatória | passou | "Treated" | "Treated" | — | — | call_9a04d5da |
| 5 | diagnóstico 162 vs 162 | obrigatória | passou | 162162 | 162162 | default rel 1e-6 (abs floor 1e-6) | — | call_9a04d5da |
| 6 | figura da resposta próxima -0.02245897436 vs -0.022458974358974367 | obrigatória | passou | -0,022459-0.022458974358974367 | -0,022459-0.022458974358974378 | relativa 0.000001 · absoluta 6.015900729523346e-9 | kessler_roth_2014.t18_2_twfe.software | call_9a04d5da |
| 7 | figura da resposta próxima 0.00601590073 vs 0.0060159007295233465 | obrigatória | passou | 0,00601590.0060159007295233465 | 0,00601590.006015900729523345 | relativa 0.000001 · absoluta 6.015900729523346e-9 | kessler_roth_2014.t18_2_twfe.software | call_9a04d5da |
| 8 | figura da resposta próxima -0.02245897436 vs -0.022 | registrada | passou | -0,022-0.022 | -0,022459-0.022458974358974378 | published_cell d=3 · absoluta 0.000500001 | kessler_roth_2014.t18_2_twfe.paper | call_9a04d5da |
| 9 | figura da resposta próxima 0.00601590073 vs 0.006 | registrada | passou | 0,0060.006 | 0,00601590.006015900729523345 | published_cell d=3 (the SE as printed) · absoluta 0.000500001 | kessler_roth_2014.t18_2_twfe.paper | call_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)
| Argumento | Referência | Agente | |
|---|---|---|---|
| covariates | — | [] | não fixado pela referência |
| dataset | "organ_donations" | "organ_donations" | igual |
| level | — | 0,950.95 | não 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.
- 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.
fileb758f11dd4f32c404877bc0b79a09b09d6e074318d2f91030060c9adb26fe56a· frame —derive{"args":{"cmp":"eq","column":"State","value":"California"},"name":"California","op":"indicator"}derive{"args":{"cmp":"gt","column":"Quarter_Num","value":3},"name":"After","op":"indicator"}derive{"args":{"columns":["California","After"]},"name":"Treated","op":"product"}
- 1Transformação
organ_donations·derive{"args":{"cmp":"eq","column":"State","value":"California"},"name":"California","op":"indicator"}162 → 162 linhasFrame antes → depois:66a839324c71→20bef437811a - 2Transformação
organ_donations·derive{"args":{"cmp":"gt","column":"Quarter_Num","value":3},"name":"After","op":"indicator"}162 → 162 linhasFrame antes → depois:20bef437811a→fa0dbe02ae97 - 3Transformação
organ_donations·derive{"args":{"columns":["California","After"]},"name":"Treated","op":"product"}162 → 162 linhasFrame antes → depois:fa0dbe02ae97→2c3e5c0fb179 - 4Turno do modelo
call_e3e35afareferenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelThe 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 pedidasinfer.twfe{ "dataset": "organ_donations", "time": "Quarter_Num", "treat": "Treated", "unit": "State", "y": "Rate" }
- 5Ferramenta
infer.twfev0.1.0call_9a04d5dadecidido pelo modeloreexecutávelMotivo: 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 Chave Valor Valor bruto Erro-padrão IC coef.Treated TWFE coefficient on Treated for Rate-0,022459 -0.0224589743589743780,0060159 0.006015900729523345-0,0342499 – -0,010668 Diagnósticosstaggered=falsen_adoption_dates= 11treatment_reversals=falsen_units= 2727n_periods= 66n_obs= 162162se_type="cluster"n_clusters= 2727
- Digests dos dados
organ_donations2c3e5c0fb1799ed60b282e41e0f6e4dda1b1a1f9704c871775e013e3a18c8c59- sha256 do resultado
b937ff2cbe16b5ff92fbdace7d7d05372556ebddbe2dbf2fe87acd87b2942dbd
- 6Turno do modelo
call_80409208referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.twfe): coef.Treated = -0,02246.", "caveats": [], "figure_refs": [ "call_9a04d5da:coef.Treated" ] }
- 7Guardaresposta aceita
- Números encontrados
- -0,02246
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 8Resposta
Estimativa (infer.twfe): coef.Treated = -0,02246.
Figuras citadas:call_9a04d5da:coef.Treated· Parada:answeredFigura da resposta:call_9a04d5da:coef.Treated= -0,022459-0.022458974358974378(se 0,00601590.006015900729523345) - 9Pontuação#0resposta com lastropassouobrigatória
every number backed - 10Pontuação#1figura da resposta escolhidapassouobrigatória
answer figure call_9a04d5da:coef.Treated (infer.twfe) - 11Pontuação#2ferramenta chamadapassouobrigatória
answer call is infer.twfe - 12Pontuação#3argumento igualpassouobrigatória
infer.twfe.y = 'Rate' - 13Pontuação#4argumento igualpassouobrigatória
infer.twfe.treat = 'Treated' - 14Pontuação#5diagnósticopassouobrigatória
162 vs 162 - 15Pontuação#6figura da resposta próximapassouobrigatória
-0.02245897436 vs -0.022458974358974367 - 16Pontuação#7figura da resposta próximapassouobrigatória
0.00601590073 vs 0.0060159007295233465 - 17Pontuação#8figura da resposta próximapassouregistrada
-0.02245897436 vs -0.022 - 18Pontuação#9figura da resposta próximapassouregistrada
0.00601590073 vs 0.006
Épocas × verificações
| # | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|
| Época 1 | passou | passou | passou | passou | passou | passou | passou | passou | passou | passou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1passoupontuada(exibindo)
Downloads desta tarefa
bbb2accbbfcf117d597d8d6e989fb4bd95b8b2fd6a6a41636ecb3e349d4c12a8O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.
| Arquivo | Tamanho | sha256 |
|---|---|---|
| attachments/0a2776e2c53fc8fc1eef8419b5c346bee5a649dc407d4950f5bd7dcc82bd13ad.json | 907 B | 0a2776e2c53fc8fc1eef8419b5c346bee5a649dc407d4950f5bd7dcc82bd13ad |
| attachments/2c78d5565a2bbf3cf08339380f4d6c73015eced0076322fca0f10eacc13abcdc.json | 800 B | 2c78d5565a2bbf3cf08339380f4d6c73015eced0076322fca0f10eacc13abcdc |
| attachments/2ede085bf5963a76f4c893ddfbd779b7be0d414bc036f94f373e51a3c7f9e87e.json | 1,3 KB | 2ede085bf5963a76f4c893ddfbd779b7be0d414bc036f94f373e51a3c7f9e87e |
| attachments/349a4e0e735a8a96914000e119fb94dc929af4151cc8bc53044bfe0a3844a2bb.json | 406 B | 349a4e0e735a8a96914000e119fb94dc929af4151cc8bc53044bfe0a3844a2bb |
| attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json | 2,4 KB | 433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420 |
| attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json | 64,0 KB | 52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b |
| attachments/5b5465e9dbf32f30acdb23819a4800155373c1543c3e73cfac0dda231b13ea69.json | 739 B | 5b5465e9dbf32f30acdb23819a4800155373c1543c3e73cfac0dda231b13ea69 |
| native/paper_repro/paper_repro.kessler_roth_2014.t18_2_twfe.agent.e1.a1.json | 36 B | 39a1ffcef98d10bf196fee2aa93ee0343ff9c7c2b32629831f61ccb9c4e8fdd7 |
| sources/kessler_roth_2014.t18_2_twfe.paper.json | 2,1 KB | d7e17fb7708cadc968b842cdad45931454948f7cd5122e191962e4cd6fbff6c9 |
| sources/kessler_roth_2014.t18_2_twfe.software.json | 1,6 KB | 73659e03f0630d7e605d8a178a8ff221360525ce0fb0a49c6e6cf8aaaadea317 |
| specs/paper_repro.kessler_roth_2014.t18_2_twfe.agent.json | 9,1 KB | 09e6163714cba3695b6f4a1db8edac05075f4b9452f9b94defd9c0bcf4503244 |
| traces/paper_repro.kessler_roth_2014.t18_2_twfe.agent.e1.a1.jsonl | 16,0 KB | 13382a9adc36d798d79fa42cf38fb52d882cd913343d0a5025b412ffa3766532 |
| trajectories/paper_repro.kessler_roth_2014.t18_2_twfe.agent.e1.a1.jsonl | 16,9 KB | fffd27084388474da20097866742df8f047fc3e6e8c12dd2dd86f7bb7c2d3ef8 |