Tarefa paper_repro.goodman_bacon_2021.fig5_dd.design
paper_reproagentT3designépoca 1, tentativa 1Execução: 20261015T183517Z-suite-paper_repro-gemini-0cda63Veredito
falhouDecidida pela verificação 2: uma entre, ferramenta errada.
tool_called(infer.ols): answer call is infer.did_staggered | tool_called(infer.twfe): answer call is infer.did_staggeredFamília do método (das chamadas de ferramentas): did (staggered adoption (Callaway-Sant'Anna))
Pergunta feita ao analista
Did the unilateral (no-fault) divorce laws that U.S. states adopted at different times reduce female suicide? Using the state panel for 1964-1996, estimate the effect of unilateral divorce on the age-adjusted female suicide rate.
Resposta
Yes, the adoption of unilateral (no-fault) divorce laws significantly reduced female suicide rates. Using a heterogeneity-robust staggered difference-in-differences estimator (Callaway–Sant'Anna) across the 1964-1996 panel of 49 U.S. states, unilateral divorce reduced the age-adjusted female suicide rate by an average of 8.13893 suicides per million women (SE: 3.00483; 95% CI: [-14.0283, -2.24958]). Relative to the sample baseline mean of 52.1664, this represents a reduction of 15.6019% (95% CI: [-26.8914, -4.31232]%), or -0.414788 standard deviations (95% CI: [-0.71493, -0.114647]). Pre-treatment placebo effects average 0.0660911 (SE: 0.503936), supporting the parallel trends assumption prior to adoption. In contrast, the classic two-way fixed effects (TWFE) specification yields an estimate of -3.07993 (SE: 2.43063; 95% CI: [-7.84386, 1.68401]), which is biased toward zero and statistically insignificant. As demonstrated by Goodman-Bacon (2021), TWFE suffers from staggered timing bias because earlier-treated cohorts act as controls for later adopters with negative weights when treatment effects change dynamically over time. Ressalvas: TWFE is biased toward zero under staggered adoption due to dynamic treatment effects and negative weighting on earlier adopters. The average treatment effect masks substantial heterogeneity across adoption cohorts. Identification relies on 13 states that did not adopt unilateral divorce within the 1964-1996 panel window as the comparison group.
Ressalvas
- TWFE is biased toward zero under staggered adoption due to dynamic treatment effects and negative weighting on earlier adopters.
- The average treatment effect masks substantial heterogeneity across adoption cohorts.
- Identification relies on 13 states that did not adopt unilateral divorce within the 1964-1996 panel window as the comparison group.
Valores esperados e de onde vêm
goodman_bacon_2021.fig5_dd.software
- Fonte
- statsmodels 0.15.0: the reference specification of goodman_bacon_2021 fig5_dd (infer.ols, figure 'coef.post') on an independent code path
- Tipo
- software
- Localização
- goodman_bacon_2021: NBER Working Paper 25018, Figure 5 (event-study and difference-in-differences estimates of the effect of no-fault divorce on female suicide), the printed 'DD Coefficient'
- Valor
- -3,07993
-3.0799257655399144(se 1,26771.267704945311894) - Conferido por
- execução de software · sm.OLS(asmrs, add_constant(['post']), fe dummies ['stfips', 'year']; 82 independent columns).fit(cov_type='HC1') on bacon_example (the file pinned at sha256 5a555448e605f75603160be20f56ca343600ec81863f3060994bac1285c29094); n_obs = 1617. The tool infer.ols (magi_infer, numpy) does not call statsmodels. Written by scripts/paper_repro_reference.py.
- Implementação
- independente da ferramenta
- Endereço
https://pypi.org/project/statsmodels/0.15.0/
- #11 figura da resposta próxima: -3,07993
-3.0799257655399144· relativa 0.000001 · absoluta 0.000001267704945311894 - #12 figura da resposta próxima: 1,2677
1.267704945311894· relativa 0.000001 · absoluta 0.000001267704945311894
goodman_bacon_2021.fig5_dd.paper
- Fonte
- Goodman-Bacon, A. (2021). Difference-in-Differences with Variation in Treatment Timing. Journal of Econometrics 225(2): 254-277. Cell read from the NBER Working Paper 25018 (revised September 2018).
- Tipo
- paper
- Localização
- NBER Working Paper 25018, Figure 5 (event-study and difference-in-differences estimates of the effect of no-fault divorce on female suicide: replication of Stevenson and Wolfers 2006), the printed DD coefficient
- Página
- 35
- Trecho
DD Coefficient = -3.08 (s.e. = 1.27)
- Valor como publicado
-3.08- Erro-padrão como publicado
1.27- Valor
- -3,08
-3.08(se 1,271.27) suicides per million women - Conferido por
- fonte primária · Read on 2026-10-15 from the NBER Working Paper 25018 PDF (48 pages, created 2018-09-06) with pdftotext -layout and from the page image rendered at 80 dpi: Figure 5 is on PDF page 37 (printed page 35). Its notes say the specification has no other controls, does not weight by population, and that standard errors are robust to heteroskedasticity. The text of section 4 (PDF page 22) states the same coefficient with 's.e. = 1.13'; the figure, whose note states the SE convention, is the cell used, and the working paper's Table 2 column (1) also prints [1.27]. The text's 1.13 matches no standard convention on this file (classical 1.11, HC2 1.27, HC3 1.30, clustered by state 2.49; B-join review). Which SE the Journal of Econometrics version prints is unverified. The published Journal of Econometrics version could not be read without a subscription. Data facts checked before any estimate: bacon_example.dta has 1,617 state-year rows (49 states, 1964-1996) with no missing outcome or reform indicator.
- sha256 da cópia lida
4f877146cba18f2d19781e2e5ea0be9275d698ef1295f166f7d1778bfc1e17fc(identifica a cópia lida, não um arquivo canônico)- Endereço
https://www.nber.org/system/files/working_papers/w25018/w25018.pdf- Licença
- NBER working paper, copyright the author; cited, not reproduced.
- #13 figura da resposta próxima: -3,08
-3.08· published_cell d=2 · absoluta 0.00500000308 - #14 figura da resposta próxima: 1,27
1.27· published_cell d=2 (the SE as printed) · absoluta 0.00500000127
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_dfe02275:att (infer.did_staggered) | obrigatória | passou | — | "call_dfe02275:att" | — | — | call_dfe02275 |
| 2 | uma entre tool_called(infer.ols): answer call is infer.did_staggered | tool_called(infer.twfe): answer call is infer.did_staggered | obrigatória | falhou | [{"abs_tol":null,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"tool_called","min_lre":null,"note":"","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":"infer.ols","value":null,"weight":1,"where":null},{"abs_tol":null,"bind":"answer_call","column":null,"failure_class":null,"field":"value","interval":false,"key":null,"kind":"tool_called","min_lre":null,"note":"","open_interval":false,"rel_tol":null,"required":true,"source_id":null,"table":null,"tool":"infer.twfe","value":null,"weight":1,"where":null}] | — | — | — | — |
| 3 | argumento igual not applicable: the answer call is infer.did_staggered, not infer.ols | obrigatória | passou | "asmrs" | — | — | — | call_dfe02275 |
| 4 | argumento igual not applicable: the answer call is infer.did_staggered, not infer.ols | obrigatória | passou | "post" | — | — | — | call_dfe02275 |
| 5 | argumento igual not applicable: the answer call is infer.did_staggered, not infer.twfe | obrigatória | passou | "asmrs" | — | — | — | call_dfe02275 |
| 6 | argumento igual not applicable: the answer call is infer.did_staggered, not infer.twfe | obrigatória | passou | "post" | — | — | — | call_dfe02275 |
| 7 | argumento igual not applicable: the answer call is infer.did_staggered, not infer.ols | registrada | passou | "asmrs" | — | — | — | call_dfe02275 |
| 8 | argumento igual not applicable: the answer call is infer.did_staggered, not infer.ols | registrada | passou | ["post"] | — | — | — | call_dfe02275 |
| 9 | argumento igual not applicable: the answer call is infer.did_staggered, not infer.ols | registrada | passou | — | — | — | — | call_dfe02275 |
| 10 | diagnóstico not applicable: the answer call is infer.did_staggered, not infer.ols | registrada | passou | 1.6171617 | — | default rel 1e-6 (abs floor 1e-6) | — | call_dfe02275 |
| 11 | figura da resposta próxima -8.138932096 vs -3.0799257655399144 | registrada | falhou | -3,07993-3.0799257655399144 | -8,13893-8.138932096032464 | relativa 0.000001 · absoluta 0.000001267704945311894 | goodman_bacon_2021.fig5_dd.software | call_dfe02275 |
| 12 | figura da resposta próxima 3.004825053 vs 1.267704945311894 | registrada | falhou | 1,26771.267704945311894 | 3,004833.004825053192595 | relativa 0.000001 · absoluta 0.000001267704945311894 | goodman_bacon_2021.fig5_dd.software | call_dfe02275 |
| 13 | figura da resposta próxima -8.138932096 vs -3.08 | registrada | falhou | -3,08-3.08 | -8,13893-8.138932096032464 | published_cell d=2 · absoluta 0.00500000308 | goodman_bacon_2021.fig5_dd.paper | call_dfe02275 |
| 14 | figura da resposta próxima 3.004825053 vs 1.27 | registrada | falhou | 1,271.27 | 3,004833.004825053192595 | published_cell d=2 (the SE as printed) · absoluta 0.00500000127 | goodman_bacon_2021.fig5_dd.paper | call_dfe02275 |
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.goodman_bacon_2021.fig5_dd.tool)
Referência: infer.olsChamada do agente: infer.did_staggered (call_dfe02275)diferente
| Argumento | Referência | Agente | |
|---|---|---|---|
| control_group | — | "never_treated" | não fixado pela referência |
| controls | [] | — | ausente |
| dataset | "bacon_example" | "bacon_example" | igual |
| fe | ["stfips","year"] | — | ausente |
| first_treat | — | "_nfd" | não fixado pela referência |
| level | — | 0,950.95 | não fixado pela referência |
| n_boot | — | 199199 | não fixado pela referência |
| se | "HC1" | — | ausente |
| seed | — | 00 | não fixado pela referência |
| time | — | "year" | não fixado pela referência |
| unit | — | "stfips" | não fixado pela referência |
| weights | — | — | ausente |
| x | ["post"] | — | ausente |
| y | "asmrs" | "asmrs" | igual |
Trajetória
Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.
- 0Início
Did the unilateral (no-fault) divorce laws that U.S. states adopted at different times reduce female suicide? Using the state panel for 1964-1996, estimate the effect of unilateral divorce on the age-adjusted female suicide rate.
bacon_example(agent)The state-year panel Goodman-Bacon (2021), 'Difference-in-Differences with Variation in Treatment Timing', uses to replicate Stevenson and Wolfers (2006) on unilateral (no-fault) divorce and female suicide: one row per U.S. state and year, 1964-1996 (1,617 rows: 49 states, 33 years). Columns: stfips, the state's FIPS code; year; _nfd, the year the state adopted unilateral (no-fault) divorce, for the 36 states that adopted it between 1969 and 1985 (missing for the states that adopted it before 1964 and for those that never adopted it in the window); post, the unilateral-divorce indicator: 1 in the years in which the state had unilateral divorce (from _nfd on, and in every year for the states that adopted it before 1964) and 0 otherwise; asmrs, the age-adjusted suicide mortality of women (suicides per million women); pcinc, personal income per capita (BEA); asmrh, the age-adjusted homicide mortality of women; cases, AFDC (welfare) cases; weight, a population weight; copop, the state's population.
file5a555448e605f75603160be20f56ca343600ec81863f3060994bac1285c29094· frame — - 1Turno do modelo
call_e1503d8egemini-3.8-flashFim:STOP14.831 ms14430 de entrada · 22 de saída · 387 de raciocínio · 0 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process on Analyzing Unilateral Divorce and Female Suicide Rates** Here's how I'm approaching this question about the impact of unilateral divorce laws on female suicide rates. My goal is to understand if the staggered adoption of these "no-fault" divorce laws across U.S. states between 1964 and 1996 had a measurable effect on the age-adjusted female suicide rate. First, I've identified the dataset I need to work with: `bacon_example`. I've reviewed its description carefully, and it seems perfectly suited for this analysis. It's a state-year panel covering the years 1964 to 1996, with 1,617 rows representing 49 states across 33 years. Crucially, it contains the variables I need: * `stfips`: State FIPS code for identification. * `year`: The specific year. * `_nfd`: This is the key variable indicating *when* a state adopted unilateral divorce. I note that this is only populated for states that adopted it between 1969 and 1985, and it's missing for states that adopted it before 1964 or never adopted it within the observed window. This is important for how I'll structure my comparisons. * `post`: A binary indicator that flags whether a state was under unilateral divorce at a given time point (1 if yes, 0 if no). This is derived from `_nfd` and accounts for states that adopted it earlier. * `asmrs`: This is my primary outcome variable – the age-adjusted suicide mortality of women, measured per million women. * `pcinc`, `asmrh`, `cases`, `weight`, `copop`: These are other variables in the dataset that might be useful as controls or for understanding context, such as personal income, homicide rates, welfare cases, population, and population weights. Next, I recognize that the nature of the question – examining the effect of a policy change that happened at different times in different places – strongly suggests a **difference-in-differences (DiD)** approach. This method is designed to estimate causal effects by comparing the changes in outcomes over time between a group that receives a treatment (states adopting unilateral divorce) and a group that does not, while accounting for pre-existing trends. Therefore, I've decided to load the `differences-in-differences` skill. This will provide me with the necessary tools and functions to implement a DiD analysis, allowing me to properly model the staggered adoption of unilateral divorce laws and isolate their impact on female suicide rates. My plan is to use `asmrs` as the dependent variable and the `post` indicator (derived from `_nfd`) as my treatment variable, while considering the time-varying nature of the treatment adoption. I'll also need to think about how to best handle the states that adopted the policy before the sample period or never adopted it, as well as potential confounding factors captured by the other variables in the dataset.
Chamadas pedidasload_skill(meta-ferramenta){ "name": "differences-in-differences" }
- 2Habilidade
differences-in-differences - 3Turno do modelo
call_1fefade4gemini-3.8-flashFim:STOP3.452 ms15398 de entrada · 46 de saída · 36 de raciocínio · 0 em cacheResumo do raciocínio, como declarado pelo modelo**My Initial Data Exploration Strategy** My first step in understanding this dataset is to get a broad overview of its characteristics. To do this, I'll employ the `data.describe` function. This is my go-to for a quick statistical summary, and I'm particularly interested in the distribution and basic statistics of key variables. Specifically, I want to examine `_nfd`, `stfips`, `year`, `asmrs`, and `post`. By looking at measures like count, mean, standard deviation, minimum, maximum, and quartiles for these columns, I expect to gain an immediate sense of their ranges, central tendencies, and potential outliers. This initial pass will help me identify any obvious data quality issues or interesting patterns that warrant further investigation.
Chamadas pedidasdata.describe{ "columns": [ "stfips", "year", "_nfd", "post", "asmrs" ], "dataset": "bacon_example" }
- 4Ferramenta
data.describev0.1.0call_7c9b996edecidido pelo modeloreexecutávelArgumentos
{ "columns": [ "stfips", "year", "_nfd", "post", "asmrs" ], "dataset": "bacon_example" }Figuras call_7c9b996e Chave Valor Valor bruto Erro-padrão IC n_rows Rows in the dataset1.617 rows 1617— — Tabelas (1)
{ "columns": [ { "column": "stfips", "dtype": "int8", "max": 56, "mean": 29.79591836734694, "min": 1, "n_missing": 0, "n_unique": 49, "sd": 15.3763161124943 }, { "column": "year", "dtype": "float32", "max": 1996, "mean": 1980, "min": 1964, "n_missing": 0, "n_unique": 33, "sd": 9.524850249595934 }, { "column": "_nfd", "dtype": "float64", "max": 1985, "mean": 1973.5833333333333, "min": 1969, "n_missing": 429, "n_unique": 12, "sd": 3.539969157262867 }, { "column": "post", "dtype": "float32", "max": 1, "mean": 0.6846011131725418, "min": 0, "n_missing": 0, "n_unique": 2, "sd": 0.46481829178717593 }, { "column": "asmrs", "dtype": "float32", "max": 185.97088623046875, "mean": 52.16641296010439, "min": 7.477235317230225, "n_missing": 0, "n_unique": 1616, "sd": 19.621875950113182 } ] }- Digests dos dados
bacon_example2e60642b02d0d3cb7932bb330f7cd97434f0eb86b21224a60ef6f440e4bf2374- sha256 do resultado
fac0b67ad1b972beb5484ed79933bf20a6c35acbbf3d7a03950310b43a4daef4
- 5Turno do modelo
call_666fb848gemini-3.8-flashFim:STOP8.076 ms15709 de entrada · 64 de saída · 676 de raciocínio · 12629 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process Regarding the Goodman-Bacon Dataset and DiD Estimators** My initial focus lands on the `_nfd` variable within this dataset, noticing its range (1969-1985) and a significant number of missing values (429). I then delve into the dataset's description to understand its context. It's a state-year panel designed to replicate Stevenson and Wolfers (2006) on unilateral divorce and female suicide, spanning 1964-1996. The `_nfd` column specifically records the year a state adopted unilateral divorce, but importantly, it's missing for states that adopted *before* 1964 and for those that *never* adopted it within the observed window. This immediately flags a potential issue for my analysis. My mind immediately jumps to the paper this dataset is associated with: Goodman-Bacon (2021). I recall or infer that Goodman-Bacon's work is about decomposing the TWFE estimator, particularly in the context of staggered adoption or staggered DiD. This is a critical insight because it suggests that simply applying a standard TWFE estimator might be problematic given the varying treatment timings. Therefore, my first inclination is to utilize `infer.did_staggered`, as it's designed for such staggered adoption scenarios. However, I also consider running `infer.twfe` as a point of comparison, or to understand how `infer.did_staggered` might handle missing `_nfd` values within the `twfe` framework if I were to use it. A key point of confusion arises when I re-read the documentation for `infer.did_staggered`. It specifies `first_treat: Column with the first treated period of each unit; 0 or missing for never-treated units`. My concern is about how this function will interpret the missing `_nfd` values. Specifically, the description for `_nfd` notes it's missing for states that adopted *before* 1964. These states are effectively "always treated" within the dataset's timeframe. How will `infer.did_staggered` distinguish between "always treated" states with missing `_nfd` and "never treated" states with missing `_nfd`? My worry is that if `_nfd` is missing for an always-treated unit (adopted pre-1964), `infer.did_staggered` might incorrectly categorize it as a "never-treated" unit if it relies solely on the absence of a `_nfd` value. The documentation states "0 or missing for never-treated units," which could lead to misclassification if not handled carefully. This leads me to a decisive step: I need to empirically test this. I plan to run both `infer.did_staggered` and `infer.twfe` to observe the resulting estimates and understand how each estimator treats these missing `_nfd` values, particularly for the "always treated" states. This empirical investigation will be crucial for validating my understanding of the data and the chosen inference methods, and to ensure I'm accurately reflecting the findings of Goodman-Bacon (2021).
Chamadas pedidasinfer.did_staggered{ "control_group": "never_treated", "dataset": "bacon_example", "first_treat": "_nfd", "time": "year", "unit": "stfips", "y": "asmrs" }
- 6Ferramenta
infer.did_staggeredv0.1.0call_dfe02275decidido pelo modeloreexecutávelArgumentos
{ "control_group": "never_treated", "dataset": "bacon_example", "first_treat": "_nfd", "level": 0.95, "n_boot": 199, "seed": 0, "time": "year", "unit": "stfips", "y": "asmrs" }Figuras call_dfe02275 Chave Valor Valor bruto Erro-padrão IC att Average effect on asmrs across post-adoption cohort-periods (Callaway–Sant'Anna)-8,13893 -8.1389320960324643,00483 3.004825053192595-14,0283 – -2,24958 Diagnósticospretrend_mean= 0,06609110.06609111227501326pretrend_se= 0,5039360.5039355644515057n_cohorts= 1212n_never_treated= 1313n_units= 4949control_group="never_treated"n_boot= 199199n_boot_failed= 00
Tabelas (3)
{ "att_gt": [ { "att": -6.939305085402268, "e": -4, "g": 1969, "n_control": 13, "n_g": 2, "post": false, "se": 7.372513833598786, "t": 1965 }, { "att": 5.0532756218543415, "e": -3, "g": 1969, "n_control": 13, "n_g": 2, "post": false, "se": 10.843792021328385, "t": 1966 }, { "att": 7.444206824669472, "e": -2, "g": 1969, "n_control": 13, "n_g": 2, "post": false, "se": 6.401564480943541, "t": 1967 }, { "att": 1.053803957425631, "e": -1, "g": 1969, "n_control": 13, "n_g": 2, "post": false, "se": 3.463266820157204, "t": 1968 }, { "att": 2.171876173753005, "e": 0, "g": 1969, "n_control": 13, "n_g": 2, "post": true, "se": 6.0616999185466955, "t": 1969 }, { "att": -5.887880912193885, "e": 1, "g": 1969, "n_control": 13, "n_g": 2, "post": true, "se": 8.796925873583062, "t": 1970 }, { "att": -10.781351236196665, "e": 2, "g": 1969, "n_control": 13, "n_g": 2, "post": true, "se": 7.529350756785859, "t": 1971 }, { "att": 1.1032959864689755, "e": 3, "g": 1969, "n_control": 13, "n_g": 2, "post": true, 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"g": 1985, "n_control": 13, "n_g": 1, "post": false, "se": 3.6035723562695052, "t": 1984 }, { "att": 3.187276106614333, "e": 0, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 3.7293249679696587, "t": 1985 }, { "att": 7.273217347952036, "e": 1, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 2.3147822036148673, "t": 1986 }, { "att": 17.964036134573128, "e": 2, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 1.951942717115881, "t": 1987 }, { "att": -11.250388072087215, "e": 3, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 2.003481310659703, "t": 1988 }, { "att": 12.199047308701736, "e": 4, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 3.5284975142351533, "t": 1989 }, { "att": 20.60645110790546, "e": 5, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 3.216778977108292, "t": 1990 }, { "att": 5.511717649606558, "e": 6, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 3.3154156361197233, "t": 1991 }, { "att": 18.857855796813965, "e": 7, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 4.20362724725264, "t": 1992 }, { "att": 15.1064421580388, "e": 8, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 2.951844620448628, "t": 1993 }, { "att": 11.600502527677095, "e": 9, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 3.0279221625586077, "t": 1994 }, { "att": 8.138764968285194, "e": 10, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 2.9709239495105244, "t": 1995 }, { "att": 20.07148104447585, "e": 11, "g": 1985, "n_control": 13, "n_g": 1, "post": true, "se": 4.733552521345442, "t": 1996 } ], "by_cohort": [ { "att": -3.877193385428125, "g": 1969, "n_g": 2, "post_periods": 28 }, { "att": -30.10731165633242, "g": 1970, "n_g": 2, "post_periods": 27 }, { "att": -11.682110319274617, "g": 1971, "n_g": 7, "post_periods": 26 }, { "att": -6.261505026205993, "g": 1972, "n_g": 3, "post_periods": 25 }, { "att": -7.169700600550726, "g": 1973, "n_g": 10, "post_periods": 24 }, { "att": -0.8874323907637415, "g": 1974, "n_g": 3, "post_periods": 23 }, { "att": 0.11329186379492699, "g": 1975, "n_g": 2, "post_periods": 22 }, { "att": -2.8061823408245603, "g": 1976, "n_g": 1, "post_periods": 21 }, { "att": -10.520133096132522, "g": 1977, "n_g": 3, "post_periods": 20 }, { "att": -0.47697715845582694, "g": 1980, "n_g": 1, "post_periods": 17 }, { "att": -5.883767940588958, "g": 1984, "n_g": 1, "post_periods": 13 }, { "att": 10.772200339879745, "g": 1985, "n_g": 1, "post_periods": 12 } ], "event_time": [ { "att": -7.298947114210862, "e": -20, "n_cohorts": 1, "se": 4.4429225560809655 }, { "att": 19.625283608069786, "e": -19, "n_cohorts": 2, "se": 16.62574421401062 }, { "att": -7.324435160710261, "e": -18, "n_cohorts": 2, "se": 6.9926210516938525 }, { "att": -2.3164815169114332, "e": -17, "n_cohorts": 2, "se": 4.816352598209057 }, { "att": 1.8078130575326772, "e": -16, "n_cohorts": 2, "se": 4.973664534648236 }, { "att": 1.8282459943722458, "e": -15, "n_cohorts": 3, "se": 5.984695838936107 }, { "att": 2.092591016720503, "e": -14, "n_cohorts": 3, "se": 2.982328936169987 }, { "att": -0.9573475764347954, "e": -13, "n_cohorts": 3, "se": 6.239157690175702 }, { "att": -3.3016897348257213, "e": -12, "n_cohorts": 4, "se": 8.866220717564186 }, { "att": -8.19362049312382, "e": -11, "n_cohorts": 5, "se": 5.3742074526093 }, { "att": 8.929786388690657, "e": -10, "n_cohorts": 6, "se": 5.349263665194888 }, { "att": -7.458616794683994, "e": -9, "n_cohorts": 7, "se": 4.180714428238863 }, { "att": 2.0400724010867672, "e": -8, "n_cohorts": 8, "se": 2.7711093104190754 }, { "att": 0.4789205521803636, "e": -7, "n_cohorts": 9, "se": 2.3795149665260906 }, { "att": -0.33074381259771457, "e": -6, "n_cohorts": 10, "se": 3.0896812863731875 }, { "att": -1.7048819011153138, "e": -5, "n_cohorts": 11, "se": 2.295593761889488 }, { "att": 2.8929911637917547, "e": -4, "n_cohorts": 12, "se": 2.272389846536111 }, { "att": -1.6605979683052776, "e": -3, "n_cohorts": 12, "se": 2.2003857670279685 }, { "att": 0.7556916342841257, "e": -2, "n_cohorts": 12, "se": 2.6325779668681193 }, { "att": 0.6019779046376545, "e": -1, "n_cohorts": 12, "se": 2.749393813097068 }, { "att": -1.8974160842406442, "e": 0, "n_cohorts": 12, "se": 2.8107853502457547 }, { "att": -2.6697742185022078, "e": 1, "n_cohorts": 12, "se": 2.856708206799876 }, { "att": -4.354261506317008, "e": 2, "n_cohorts": 12, "se": 3.8951129371554805 }, { "att": -2.8600383701487484, "e": 3, "n_cohorts": 12, "se": 2.619918310867885 }, { "att": -5.7140999920347815, "e": 4, "n_cohorts": 12, "se": 2.7514744430619476 }, { "att": -4.929904505737827, "e": 5, "n_cohorts": 12, "se": 2.6515777757070547 }, { "att": -6.276016742755206, "e": 6, "n_cohorts": 12, "se": 3.4153095018065294 }, { "att": -8.78488251286694, "e": 7, "n_cohorts": 12, "se": 3.50396617434475 }, { "att": -9.947348297151743, "e": 8, "n_cohorts": 12, "se": 3.3575171005765 }, { "att": -6.235176277975751, "e": 9, "n_cohorts": 12, "se": 3.603417783701141 }, { "att": -9.747392389509413, "e": 10, "n_cohorts": 12, "se": 3.973682111929847 }, { "att": -7.67110639759618, "e": 11, "n_cohorts": 12, "se": 3.9846391963153 }, { "att": -6.550974328177317, "e": 12, "n_cohorts": 11, "se": 4.528934254203562 }, { "att": -8.235903670884905, "e": 13, "n_cohorts": 10, "se": 4.412823089507213 }, { "att": -7.449182411124803, "e": 14, "n_cohorts": 10, "se": 4.560430540994105 }, { "att": -9.274923851047708, "e": 15, "n_cohorts": 10, "se": 4.44871861053166 }, { "att": -8.279150995194103, "e": 16, "n_cohorts": 10, "se": 4.4201513311136855 }, { "att": -9.613733912959244, "e": 17, "n_cohorts": 9, "se": 4.547542922521425 }, { "att": -11.866857064075958, "e": 18, "n_cohorts": 9, "se": 3.8948055895682336 }, { "att": -12.52466401409158, "e": 19, "n_cohorts": 9, "se": 3.5433759546398202 }, { "att": -10.027287478324695, "e": 20, "n_cohorts": 8, "se": 3.944965625607022 }, { "att": -11.57334828566493, "e": 21, "n_cohorts": 7, "se": 3.743126657491615 }, { "att": -12.715595788765498, "e": 22, "n_cohorts": 6, "se": 3.7481868589580833 }, { "att": -14.416585845825002, "e": 23, "n_cohorts": 5, "se": 4.447922360588129 }, { "att": -15.5885900245918, "e": 24, "n_cohorts": 4, "se": 5.447446603133908 }, { "att": -16.12262788519159, "e": 25, "n_cohorts": 3, "se": 7.749429106585113 }, { "att": -25.875268349280724, "e": 26, "n_cohorts": 2, "se": 17.015058115680798 }, { "att": 2.235682560847355, "e": 27, "n_cohorts": 1, "se": 11.336147182912633 } ] }- Digests dos dados
bacon_example2e60642b02d0d3cb7932bb330f7cd97434f0eb86b21224a60ef6f440e4bf2374- sha256 do resultado
7eb1a05b584d4e0b958876641deb261a32061432c5657391f66c69626cd4ff55
- 7Turno do modelo
call_1d36c85dgemini-3.8-flashFim:STOP4.210 ms17706 de entrada · 52 de saída · 55 de raciocínio · 12662 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process for Evaluating `infer.twfe`** My primary objective here is to conduct a comparative analysis of the `infer.twfe` function against a well-established benchmark in the field of difference-in-differences (DiD) estimation. Specifically, I want to assess how `infer.twfe` performs relative to the approach detailed by Stevenson and Wolfers in their 2006 work, and more recently discussed by Goodman-Bacon in their 2021 paper. This benchmark represents a classic, widely recognized method for Two-Way Fixed Effects (TWFE) DiD, and understanding how a new implementation aligns or deviates from it is crucial for validating its utility and reliability. Therefore, my immediate next step is to execute `infer.twfe` and examine its output.
Chamadas pedidasinfer.twfe{ "covariates": [], "dataset": "bacon_example", "time": "year", "treat": "post", "unit": "stfips", "y": "asmrs" }
- 8Ferramenta
infer.twfev0.1.0call_d526a7c2decidido pelo modeloreexecutávelArgumentos
{ "covariates": [], "dataset": "bacon_example", "level": 0.95, "time": "year", "treat": "post", "unit": "stfips", "y": "asmrs" }Figuras call_d526a7c2 Chave Valor Valor bruto Erro-padrão IC coef.post TWFE coefficient on post for asmrs-3,07993 -3.0799257655399022,43063 2.4306251935482086-7,84386 – 1,68401 Diagnósticosstaggered=truen_adoption_dates= 1313treatment_reversals=falsen_units= 4949n_periods= 3333n_obs= 1.6171617se_type="cluster"n_clusters= 4949
- adoption is staggered: this TWFE coefficient can be far from any average effect; use infer.did_staggered
- Digests dos dados
bacon_example2e60642b02d0d3cb7932bb330f7cd97434f0eb86b21224a60ef6f440e4bf2374- sha256 do resultado
0253dae9035cce9008e04ac66ac9244c22f61aa259540da4893c181315bd7bd4
- 9Turno do modelo
call_0fdd3e04gemini-3.8-flashFim:STOP5.133 ms18015 de entrada · 66 de saída · 225 de raciocínio · 15794 em cacheResumo do raciocínio, como declarado pelo modelo**A Deep Dive into Our Difference-in-Differences Estimates** My mind immediately went to that familiar table from Goodman-Bacon (2021) upon seeing the TWFE coefficient. There it is, a coefficient of -3.07993 with a standard error of 2.43063, yielding a 95% confidence interval from -7.84386 to 1.68401. It's precisely the value presented for $\beta^{DD}$ in their Table 1. This correspondence is, of course, expected if the TWFE approach is applied to the same data and specification. However, the real intrigue comes when I look at the Callaway-Sant'Anna staggered DiD estimate generated by `infer.did_staggered`. This provides an Average Treatment Effect on the Treated (ATT) of -8.13893, with a standard error of 3.00483 and a 95% confidence interval spanning from -14.0283 to -2.24958. The stark difference between this ATT and the TWFE estimate is precisely what Goodman-Bacon's work highlights – the potential for TWFE to produce biased estimates in the presence of staggered adoption and differential treatment effects over time. My next step, naturally, is to probe the `infer.did_staggered` function further. I need to confirm the default behavior regarding the control group. Specifically, I'm curious about how `control_group="not_yet_treated"` compares to what is considered the standard, likely the `never_treated` group. This is crucial for understanding the robustness of my findings and ensuring I'm using the most appropriate comparison group for my analysis.
Chamadas pedidasinfer.did_staggered{ "control_group": "not_yet_treated", "dataset": "bacon_example", "first_treat": "_nfd", "time": "year", "unit": "stfips", "y": "asmrs" }
- 10Ferramenta
infer.did_staggeredv0.1.0call_9a63b4d0decidido pelo modeloreexecutávelArgumentos
{ "control_group": "not_yet_treated", "dataset": "bacon_example", "first_treat": "_nfd", "level": 0.95, "n_boot": 199, "seed": 0, "time": "year", "unit": "stfips", "y": "asmrs" }Figuras call_9a63b4d0 Chave Valor Valor bruto Erro-padrão IC att Average effect on asmrs across post-adoption cohort-periods (Callaway–Sant'Anna)-7,26004 -7.2600380495334123,02178 3.0217769967898627-13,1826 – -1,33746 Diagnósticospretrend_mean= -0,0678103-0.06781031825741392pretrend_se= 0,2270340.22703425069361471n_cohorts= 1212n_never_treated= 1313n_units= 4949control_group="not_yet_treated"n_boot= 199199n_boot_failed= 00
Tabelas (3)
{ "att_gt": [ { "att": -4.727176098113365, "e": -4, "g": 1969, "n_control": 47, "n_g": 2, "post": false, "se": 5.577104602726828, "t": 1965 }, { "att": 5.418962762710896, "e": -3, "g": 1969, "n_control": 47, "n_g": 2, "post": false, "se": 9.335585048529783, "t": 1966 }, { "att": 3.5383088943806102, "e": -2, "g": 1969, "n_control": 47, "n_g": 2, "post": false, "se": 4.998086317820897, "t": 1967 }, { "att": 2.5197929219996675, "e": -1, "g": 1969, "n_control": 47, "n_g": 2, "post": false, "se": 2.184845884905818, "t": 1968 }, { "att": 1.230716867649809, "e": 0, "g": 1969, "n_control": 47, "n_g": 2, "post": true, "se": 5.6429879338555775, "t": 1969 }, { "att": -8.509539159138997, "e": 1, "g": 1969, "n_control": 45, "n_g": 2, "post": true, "se": 8.1344622174323, "t": 1970 }, { "att": -11.834927307932, "e": 2, "g": 1969, "n_control": 38, "n_g": 2, "post": true, "se": 6.772598967764304, "t": 1971 }, { "att": -2.250278309413365, "e": 3, "g": 1969, "n_control": 35, "n_g": 2, "post": true, "se": 3.0349344268516267, "t": 1972 }, { "att": 1.665885391235352, "e": 4, "g": 1969, "n_control": 25, "n_g": 2, "post": true, "se": 7.287738966978911, "t": 1973 }, { "att": -0.07192646373401956, "e": 5, "g": 1969, "n_control": 22, "n_g": 2, "post": true, "se": 4.589822781781702, "t": 1974 }, { "att": -1.9575609207153324, "e": 6, "g": 1969, "n_control": 20, "n_g": 2, "post": true, "se": 4.417878490121371, "t": 1975 }, { "att": -10.992921728836862, "e": 7, "g": 1969, "n_control": 19, "n_g": 2, "post": true, "se": 4.231719864910602, "t": 1976 }, { "att": -5.730818390846252, "e": 8, "g": 1969, "n_control": 16, "n_g": 2, "post": true, "se": 7.420349183751771, "t": 1977 }, { "att": -8.493724584579468, "e": 9, "g": 1969, "n_control": 16, "n_g": 2, "post": true, "se": 9.287318389219747, "t": 1978 }, { "att": -9.7069491147995, "e": 10, "g": 1969, "n_control": 16, "n_g": 2, "post": true, "se": 4.681468339404791, "t": 1979 }, { "att": -12.316284688313802, "e": 11, "g": 1969, "n_control": 15, "n_g": 2, "post": true, "se": 5.005203254978176, "t": 1980 }, { "att": -7.2058973948160805, "e": 12, "g": 1969, "n_control": 15, "n_g": 2, "post": true, "se": 6.268899038883529, "t": 1981 }, { "att": 1.4908982594807947, "e": 13, "g": 1969, "n_control": 15, "n_g": 2, "post": true, "se": 8.876371994489359, "t": 1982 }, { "att": 6.879344050089518, "e": 14, "g": 1969, "n_control": 15, "n_g": 2, "post": true, "se": 8.727213500381033, "t": 1983 }, { "att": -4.135175364358084, "e": 15, "g": 1969, "n_control": 14, "n_g": 2, "post": true, "se": 10.714694101396987, "t": 1984 }, { "att": 2.086787223815918, "e": 16, "g": 1969, "n_control": 13, "n_g": 2, "post": true, "se": 10.573911184692243, "t": 1985 }, { "att": -4.177348210261418, "e": 17, "g": 1969, "n_control": 13, "n_g": 2, "post": true, "se": 9.254064573937331, "t": 1986 }, { "att": -4.934819294856145, "e": 18, "g": 1969, "n_control": 13, "n_g": 2, "post": true, "se": 8.291223019054991, "t": 1987 }, { "att": -7.225140718313364, "e": 19, "g": 1969, 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-1.4396100343198683, "g": 1974, "n_g": 3, "post_periods": 23 }, { "att": 0.1197979030083967, "g": 1975, "n_g": 2, "post_periods": 22 }, { "att": -3.404626499840351, "g": 1976, "n_g": 1, "post_periods": 21 }, { "att": -10.442004128445635, "g": 1977, "n_g": 3, "post_periods": 20 }, { "att": -0.7888202128649733, "g": 1980, "n_g": 1, "post_periods": 17 }, { "att": -5.868491673812158, "g": 1984, "n_g": 1, "post_periods": 13 }, { "att": 10.772200339879745, "g": 1985, "n_g": 1, "post_periods": 12 } ], "event_time": [ { "att": -4.995827833811442, "e": -20, "n_cohorts": 1, "se": 1.7881484479887777 }, { "att": 21.33549177646637, "e": -19, "n_cohorts": 2, "se": 15.28270141786798 }, { "att": -9.470619976520538, "e": -18, "n_cohorts": 2, "se": 7.695736376139825 }, { "att": -3.736322204271952, "e": -17, "n_cohorts": 2, "se": 2.454988934609794 }, { "att": 2.063954458720442, "e": -16, "n_cohorts": 2, "se": 3.987091543412005 }, { "att": 1.7099062891241017, "e": -15, "n_cohorts": 3, "se": 4.6134717917357 }, { "att": 2.2067624659867615, "e": -14, "n_cohorts": 3, "se": 1.4656389321605547 }, { "att": -2.2489123157847586, "e": -13, "n_cohorts": 3, "se": 4.900071028330432 }, { "att": -1.0390601316003778, "e": -12, "n_cohorts": 4, "se": 7.982010978741711 }, { "att": -7.72608244292167, "e": -11, "n_cohorts": 5, "se": 4.760648785160678 }, { "att": 8.31595822505019, "e": -10, "n_cohorts": 6, "se": 5.138609622389859 }, { "att": -7.030658683732905, "e": -9, "n_cohorts": 7, "se": 3.7856832497226027 }, { "att": 2.6339565188363694, "e": -8, "n_cohorts": 8, "se": 2.3557288993739487 }, { "att": 0.4466399389465067, "e": -7, "n_cohorts": 9, "se": 2.0782556995928365 }, { "att": -0.9944509544338767, "e": -6, "n_cohorts": 10, "se": 3.0033024234159047 }, { "att": -1.9957046860005372, "e": -5, "n_cohorts": 11, "se": 2.247693458710787 }, { "att": 2.8355209603048035, "e": -4, "n_cohorts": 12, "se": 2.030261608239588 }, { "att": -2.47371755741508, "e": -3, "n_cohorts": 12, "se": 2.2984001745254816 }, { "att": 1.1103399478222644, "e": -2, "n_cohorts": 12, "se": 2.8130580531598004 }, { "att": 0.16837687464190576, "e": -1, "n_cohorts": 12, "se": 2.6162883140406823 }, { "att": -0.05847080191358462, "e": 0, "n_cohorts": 12, "se": 2.824955105065783 }, { "att": -1.5025960897011759, "e": 1, "n_cohorts": 12, "se": 2.8714937044388638 }, { "att": -1.1632378606158509, "e": 2, "n_cohorts": 12, "se": 3.9270522786676194 }, { "att": -0.7775638626392392, "e": 3, "n_cohorts": 12, "se": 2.845834856109963 }, { "att": -3.295604638749087, "e": 4, "n_cohorts": 12, "se": 3.0508821299329303 }, { "att": -3.323540289217504, "e": 5, "n_cohorts": 12, "se": 3.1030406433113034 }, { "att": -4.514863418891952, "e": 6, "n_cohorts": 12, "se": 3.607008793886574 }, { "att": -7.419722656564332, "e": 7, "n_cohorts": 12, "se": 3.653660170230541 }, { "att": -8.307924298637031, "e": 8, "n_cohorts": 12, "se": 3.5091653315050992 }, { "att": -5.275603665832975, "e": 9, "n_cohorts": 12, "se": 3.473418581853035 }, { "att": -8.63866488180075, "e": 10, "n_cohorts": 12, "se": 3.9868809631262234 }, { "att": -6.834600657508487, "e": 11, "n_cohorts": 12, "se": 3.9923403722141426 }, { "att": -6.112127491887611, "e": 12, "n_cohorts": 11, "se": 4.411649412235541 }, { "att": -8.026767079610812, "e": 13, "n_cohorts": 10, "se": 4.3113187472092696 }, { "att": -7.457230480044495, "e": 14, "n_cohorts": 10, "se": 4.512499691887277 }, { "att": -9.286389676077413, "e": 15, "n_cohorts": 10, "se": 4.431733265976338 }, { "att": -8.279150995194103, "e": 16, "n_cohorts": 10, "se": 4.4201513311136855 }, { "att": -9.613733912959244, "e": 17, "n_cohorts": 9, "se": 4.547542922521425 }, { "att": -11.866857064075958, "e": 18, "n_cohorts": 9, "se": 3.8948055895682336 }, { "att": -12.52466401409158, "e": 19, "n_cohorts": 9, "se": 3.5433759546398202 }, { "att": -10.027287478324695, "e": 20, "n_cohorts": 8, "se": 3.944965625607022 }, { "att": -11.57334828566493, "e": 21, "n_cohorts": 7, "se": 3.743126657491615 }, { "att": -12.715595788765498, "e": 22, "n_cohorts": 6, "se": 3.7481868589580833 }, { "att": -14.416585845825002, "e": 23, "n_cohorts": 5, "se": 4.447922360588129 }, { "att": -15.5885900245918, "e": 24, "n_cohorts": 4, "se": 5.447446603133908 }, { "att": -16.12262788519159, "e": 25, "n_cohorts": 3, "se": 7.749429106585113 }, { "att": -25.875268349280724, "e": 26, "n_cohorts": 2, "se": 17.015058115680798 }, { "att": 2.235682560847355, "e": 27, "n_cohorts": 1, "se": 11.336147182912633 } ] }- Digests dos dados
bacon_example2e60642b02d0d3cb7932bb330f7cd97434f0eb86b21224a60ef6f440e4bf2374- sha256 do resultado
5993ccd46d5802866b752ed7db944ea1e51b7f6b0e87a1ada587d4239b80bc2c
- 11Turno do modelo
call_2740772fgemini-3.8-flashFim:STOP8.802 ms19566 de entrada · 47 de saída · 598 de raciocínio · 15748 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process on Analyzing the DiD Results** Okay, so I've been digging into these difference-in-differences (DiD) estimates, specifically looking at the impact of a treatment with staggered adoption. My initial focus was on `infer.did_staggered` using the `never_treated` group. Here's what `infer.did_staggered` told me: * The Average Treatment Effect on the Treated (ATT) is -8.13893. * The standard error (se) is 3.00483. * The 95% confidence interval (CI) is quite telling: [-14.0283, -2.24958]. This suggests a statistically significant negative effect. Crucially, I also looked at the `pretrend_mean`. It's 0.0660911 with a standard error of 0.503936. This value is extremely close to zero and not statistically significant, which is exactly what I want to see – it supports the assumption of parallel trends before the treatment. This is a good sign. I also noted the sample structure: 12 cohorts, 13 never-treated units, and a total of 49 units. Then, I contrasted this with the standard `infer.twfe` (Two-Way Fixed Effects) estimation. This gave me a `coef.post` of -3.07993 with a standard error of 2.43063 and a 95% CI of [-7.84386, 1.68401]. This is where I paused and thought, "Why is the TWFE estimate (-3.08) so different from the Callaway-Sant'Anna (which is what `infer.did_staggered` effectively uses for ATT) estimate (-8.14)?" The key reason, as I understand it, is the issue of staggered adoption and the associated problems with TWFE. As highlighted by Goodman-Bacon (2021), TWFE can be problematic because it effectively uses units that have already been treated as controls for units that are treated later. When treatment effects vary over time or across different cohorts (which is common with staggered adoption), TWFE can end up assigning negative weights to some of the 2x2 DiD comparisons. This can bias the overall TWFE estimate, often pulling it towards zero. In contrast, `infer.did_staggered` is designed to be robust to this heterogeneity in treatment effects and avoids using already-treated units as controls. That's why its estimate of the ATT, -8.13893 suicides per million women, is more reliable and, in this case, statistically significant. Now, I'm thinking about how to best contextualize this effect. Is it helpful to look at the baseline mean of the outcome variable, `asmrs`? The `data.describe` shows the mean of `asmrs` is 52.1664 with a standard deviation of 19.6219. This brings up the question of reporting the effect as a percentage change. Can I compute this using `calc.combine`? Or more directly, I should consider `core.scale_effect`. The documentation for `core.scale_effect` is quite clear: it has a `transform="percent_of_baseline"` option, which calculates 100 * coefficient / baseline. The baseline is defined as the baseline mean of the outcome. So, the next step is to use `core.scale_effect` with the ATT estimate (which I've identified as `call_dfe02275:att` based on the call ID and key) and the baseline mean of `asmrs` (52.1664) to report the effect as a percentage. I need to decide if this percentage change is a more informative way to present my findings to an expert audience.
Chamadas pedidascore.scale_effect{ "baseline": 52.1664, "coefficient": "call_dfe02275:att", "transform": "percent_of_baseline" }
- 12Ferramenta
core.scale_effectv0.1.0call_31574844decidido pelo modeloreexecutávelArgumentos
{ "baseline": 52.1664, "coefficient": "call_dfe02275:att", "sd": null, "transform": "percent_of_baseline" }Figuras call_31574844 Chave Valor Valor bruto Erro-padrão IC effect Effect as a percent of the baseline mean: Average effect on asmrs across post-adoption cohort-periods (Callaway–Sant'Anna)-15,6019 % of baseline -15.6018665195076985,76008 5.760077469774788-26,8914 – -4,31232 Diagnósticostransform="percent_of_baseline"baseline= 52,166452.1664
- Digests dos dados
- sha256 do resultado
99f6fee96b27b3557be3970036d0dbcd4ad6126ef4b8b5e9da1aca020a73b682
- 13Turno do modelo
call_05ebcf88gemini-3.8-flashFim:STOP3.178 ms20328 de entrada · 45 de saída · 48 de raciocínio · 15837 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process** Right, so the immediate next step in my thinking is to explore an additional avenue related to unit scaling. I'm specifically wondering if the `sd_units` functionality is applicable here. To that end, I've noted the standard deviation of `asmrs` stands at 19.6219. My intention is to then proceed with executing `core.scale_effect`, but crucially, *with* `sd_units` engaged. This is to see how that particular scaling parameter influences the outcome, given the observed variability in `asmrs`.
Chamadas pedidascore.scale_effect{ "coefficient": "call_dfe02275:att", "sd": 19.6219, "transform": "sd_units" }
- 14Ferramenta
core.scale_effectv0.1.0call_03dea4bfdecidido pelo modeloreexecutávelArgumentos
{ "baseline": null, "coefficient": "call_dfe02275:att", "sd": 19.6219, "transform": "sd_units" }Figuras call_03dea4bf Chave Valor Valor bruto Erro-padrão IC effect Effect in standard deviations of the outcome: Average effect on asmrs across post-adoption cohort-periods (Callaway–Sant'Anna)-0,414788 SD -0.41478817525481550,153136 0.15313629430343623-0,71493 – -0,114647 Diagnósticostransform="sd_units"sd= 19,621919.6219
- Digests dos dados
- sha256 do resultado
4ff4e9196c4a3d1c99b4134d3e7f6e1f94bb6e02b306faba89ef8f423e288494
- 15Turno do modelo
call_db0bc3a5gemini-3.8-flashFim:STOP33.123 ms20534 de entrada · 524 de saída · 3886 de raciocínio · 15825 em cacheResumo do raciocínio, como declarado pelo modelo**My Thought Process on Analyzing the Impact of Unilateral Divorce Laws on Female Suicide Rates** I need to analyze the provided text to understand the user's request and formulate a response as if I were a MAGI economist. My primary goal is to determine the effect of unilateral (no-fault) divorce laws on age-adjusted female suicide rates using state-level panel data from 1964-1996. The key constraints are: I must only use numbers generated by tool calls or present in the question, use specific tool calls with clear rationales, and present the final answer concisely in the language of the question, including the estimate, its uncertainty, the research design, and potential limitations. First, I've reviewed the core rules. Rule 1 is critical: "You never compute a number. Every number in your answer must have been stated by a tool call in this run (a figure, its standard error or interval, a diagnostic, a table cell) or written in the question. No mental arithmetic..." This means I need to be extremely careful to only reference figures directly produced by the tools or given in the prompt. For any calculations (like ratios or differences between figures), I'd have to use `calc.combine`. Rule 2 states: "Before each tool call, write one sentence on why this tool and these arguments fit the design. It is recorded as your rationale." This means every time I decide to use a tool, I need to explicitly justify my choice. Rule 7 outlines the output format: "Finish with submit_answer. Write the answer in the language of the question (Portuguese: decimal comma), short and direct: the estimate with its uncertainty, the design that identifies it, and what could undermine it. List in figure_refs every figure you quote, as '<call_id>:<key>' (each tool result carries its call_id), and put diagnostics that limit the conclusion in caveats. Write numbers as the tools stated them; do not quote rule-of-thumb thresholds as numbers." Now, I've noticed a potential ambiguity regarding Rule 7: "Write the answer in the language of the question (Portuguese: decimal comma)." The question itself is in English. My interpretation is that "the language of the question" is English. The parenthetical "(Portuguese: decimal comma)" likely serves as a reminder for cases where the question *is* in Portuguese, or as a general clarification about decimal notation common in Brazil (where the analyst is hypothetically located). Given that the tools output numbers with decimal points (e.g., -8.13893), and standard English uses decimal points, writing in English with numbers as stated by the tools seems to be the most direct and correct interpretation. I will write my answer in English. Next, I need to select the appropriate tools and their arguments to address the research question. The question asks to "estimate the effect of unilateral divorce on the age-adjusted female suicide rate" using a "state panel for 1964-1996." This immediately signals a difference-in-differences (DiD) setting, particularly one with staggered adoption, as states adopted these laws at "different times." I've reviewed the available tool outputs. There are two primary ways the effect of unilateral divorce on age-adjusted female suicide rates (asmrs) is presented: 1. **Callaway–Sant'Anna estimator**: `call_dfe02275` provides the "Average effect on asmrs across post-adoption cohort-periods (Callaway–Sant'Anna)". This estimator is known for its robustness to dynamic treatment effects and staggered adoption, using a clean control group (never treated units). * The estimated effect (ATT) is -8.13893 (SE: 3.00483, 95% CI: [-14.0283, -2.24958]). * This estimator is crucial because, as identified by Goodman-Bacon (2021) and referenced in my understanding of this data, standard two-way fixed effects (TWFE) can be biased in staggered DiD settings. 2. **Two-way Fixed Effects (TWFE) estimator**: `call_d526a7c2` provides the "TWFE coefficient on post for asmrs". * The estimated effect is -3.07993 (SE: 2.43063, 95% CI: [-7.84386, 1.68401]). * This is important for comparison and to highlight the potential bias due to staggered adoption, where earlier treated units can act as controls for later treated units with negative weights, biasing the TWFE estimate toward zero. Given the question's focus on the effect and the potential for bias in standard TWFE with staggered adoption, the Callaway–Sant'Anna estimator is the more robust and appropriate primary estimate. The pre-trend diagnostic associated with `call_dfe02275` (`pretrend_mean: 0.0660911122750133` with SE `0.503935564451506`) is also vital for assessing the parallel trends assumption. I also see results for the effect expressed as a percentage of the baseline mean (`call_31574844`) and in standard deviations (`call_03dea4bf`). These provide useful context and can be included in the summary. The baseline mean of the outcome is `52.1664` from `call_7c9b996e` which is used to calculate the percentage change. So, my plan is: 1. State the Callaway–Sant'Anna estimate as the primary finding. 2. Provide its uncertainty (standard error and confidence interval). 3. Explain the research design, referencing the Callaway–Sant'Anna methodology and the comparison to the biased TWFE. 4. Discuss potential limitations or caveats, such as the bias in TWFE and the evidence from pre-trends. 5. Ensure all numbers are directly from tool outputs or the question. 6. List all quoted figures in `figure_refs`. 7. Use the correct language (English) and number formatting (decimal points as stated by tools). Let's start by formulating the tool calls and their rationales. **Rationale for `call_dfe02275`:** This tool call estimates the average treatment effect on the treated (ATT) using the Callaway–Sant'Anna estimator, which is appropriate for panel data with staggered treatment adoption and potential time-varying treatment effects, as it provides a robust estimate by using a clean control group of never-treated units. I will use `call_dfe02275`. **Rationale for `call_d526a7c2`:** This tool call estimates the effect using a standard two-way fixed effects (TWFE) model, which is included for comparison and to illustrate the potential bias introduced by staggered adoption, as earlier treated states can inadvertently act as controls for later treated states. I will use `call_d526a7c2`. **Rationale for `call_31574844`:** This tool call calculates the estimated effect as a percentage of the baseline mean outcome. This provides a standardized interpretation of the magnitude of the effect relative to the average suicide rate before the law changes. I will use `call_31574844`. **Rationale for `call_03dea4bf`:** This tool call expresses the estimated effect in terms of standard deviations of the outcome. This offers another standardized measure of the effect's magnitude, useful for comparing its impact across different studies or contexts. I will use `call_03dea4bf`. Now, I can construct the final answer. ```json [ {"tool_code": "call_dfe02275", "tool_name": "estimate_difference_in_differences", "parameters": {"estimation_method": "Callaway–Sant'Anna", "outcome": "asmrs", "treatment": "unilateral_divorce", "time_period": "1964-1996", "state_panel": true}}, {"tool_code": "call_d526a7c2", "tool_name": "estimate_difference_in_differences", "parameters": {"estimation_method": "TWFE", "outcome": "asmrs", "treatment": "unilateral_divorce", "time_period": "1964-1996", "state_panel": true}}, {"tool_code": "call_31574844", "tool_name": "scale_effect", "parameters": {"effect_call_id": "call_dfe02275", "scale_to": "% of baseline"}}, {"tool_code": "call_03dea4bf", "tool_name": "scale_effect", "parameters": {"effect_call_id": "call_dfe02275", "scale_to": "SD"}} ] ```
Chamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Yes, the adoption of unilateral (no-fault) divorce laws significantly reduced female suicide rates.\n\nUsing a heterogeneity-robust staggered difference-in-differences estimator (Callaway–Sant'Anna) across the 1964-1996 panel of 49 U.S. states, unilateral divorce reduced the age-adjusted female suicide rate by an average of 8.13893 suicides per million women (SE: 3.00483; 95% CI: [-14.0283, -2.24958]). Relative to the sample baseline mean of 52.1664, this represents a reduction of 15.6019% (95% CI: [-26.8914, -4.31232]%), or -0.414788 standard deviations (95% CI: [-0.71493, -0.114647]).\n\nPre-treatment placebo effects average 0.0660911 (SE: 0.503936), supporting the parallel trends assumption prior to adoption.\n\nIn contrast, the classic two-way fixed effects (TWFE) specification yields an estimate of -3.07993 (SE: 2.43063; 95% CI: [-7.84386, 1.68401]), which is biased toward zero and statistically insignificant. As demonstrated by Goodman-Bacon (2021), TWFE suffers from staggered timing bias because earlier-treated cohorts act as controls for later adopters with negative weights when treatment effects change dynamically over time.", "caveats": [ "TWFE is biased toward zero under staggered adoption due to dynamic treatment effects and negative weighting on earlier adopters.", "The average treatment effect masks substantial heterogeneity across adoption cohorts.", "Identification relies on 13 states that did not adopt unilateral divorce within the 1964-1996 panel window as the comparison group." ], "figure_refs": [ "call_dfe02275:att", "call_d526a7c2:coef.post", "call_31574844:effect", "call_03dea4bf:effect" ] }
- 16Guardaresposta aceita
- Números encontrados
- 1964 · 1996 · 49 · 8.13893 · 3.00483 · 95% · -14.0283 · -2.24958 · 52.1664 · 15.6019% · 95% · -26.8914 · -4.31232 · -0.414788 · 95% · -0.71493 · -0.114647 · 0.0660911 · 0.503936 · -3.07993 · 2.43063 · 95% · -7.84386 · 1.68401 · 2021 · 13 · 1964 · 1996
- Sem lastro
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- Não citados
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1 nova(s) tentativa(s) restante(s) - 17Resposta
Yes, the adoption of unilateral (no-fault) divorce laws significantly reduced female suicide rates. Using a heterogeneity-robust staggered difference-in-differences estimator (Callaway–Sant'Anna) across the 1964-1996 panel of 49 U.S. states, unilateral divorce reduced the age-adjusted female suicide rate by an average of 8.13893 suicides per million women (SE: 3.00483; 95% CI: [-14.0283, -2.24958]). Relative to the sample baseline mean of 52.1664, this represents a reduction of 15.6019% (95% CI: [-26.8914, -4.31232]%), or -0.414788 standard deviations (95% CI: [-0.71493, -0.114647]). Pre-treatment placebo effects average 0.0660911 (SE: 0.503936), supporting the parallel trends assumption prior to adoption. In contrast, the classic two-way fixed effects (TWFE) specification yields an estimate of -3.07993 (SE: 2.43063; 95% CI: [-7.84386, 1.68401]), which is biased toward zero and statistically insignificant. As demonstrated by Goodman-Bacon (2021), TWFE suffers from staggered timing bias because earlier-treated cohorts act as controls for later adopters with negative weights when treatment effects change dynamically over time. Ressalvas: TWFE is biased toward zero under staggered adoption due to dynamic treatment effects and negative weighting on earlier adopters. The average treatment effect masks substantial heterogeneity across adoption cohorts. Identification relies on 13 states that did not adopt unilateral divorce within the 1964-1996 panel window as the comparison group.
- TWFE is biased toward zero under staggered adoption due to dynamic treatment effects and negative weighting on earlier adopters.
- The average treatment effect masks substantial heterogeneity across adoption cohorts.
- Identification relies on 13 states that did not adopt unilateral divorce within the 1964-1996 panel window as the comparison group.
Figuras citadas:call_dfe02275:att, call_d526a7c2:coef.post, call_31574844:effect, call_03dea4bf:effect· Parada:answeredFigura da resposta:call_dfe02275:att= -8,13893-8.138932096032464(se 3,004833.004825053192595) - 18Pontuação#0resposta com lastropassouobrigatória
every number backed - 19Pontuação#1figura da resposta escolhidapassouobrigatória
answer figure call_dfe02275:att (infer.did_staggered) - 20Pontuação#2uma entrefalhouobrigatória
tool_called(infer.ols): answer call is infer.did_staggered | tool_called(infer.twfe): answer call is infer.did_staggered - 21Pontuação#3argumento igualpassouobrigatória
not applicable: the answer call is infer.did_staggered, not infer.ols - 22Pontuação#4argumento igualpassouobrigatória
not applicable: the answer call is infer.did_staggered, not infer.ols - 23Pontuação#5argumento igualpassouobrigatória
not applicable: the answer call is infer.did_staggered, not infer.twfe - 24Pontuação#6argumento igualpassouobrigatória
not applicable: the answer call is infer.did_staggered, not infer.twfe - 25Pontuação#7argumento igualpassouregistrada
not applicable: the answer call is infer.did_staggered, not infer.ols - 26Pontuação#8argumento igualpassouregistrada
not applicable: the answer call is infer.did_staggered, not infer.ols - 27Pontuação#9argumento igualpassouregistrada
not applicable: the answer call is infer.did_staggered, not infer.ols - 28Pontuação#10diagnósticopassouregistrada
not applicable: the answer call is infer.did_staggered, not infer.ols - 29Pontuação#11figura da resposta próximafalhouregistrada
-8.138932096 vs -3.0799257655399144 - 30Pontuação#12figura da resposta próximafalhouregistrada
3.004825053 vs 1.267704945311894 - 31Pontuação#13figura da resposta próximafalhouregistrada
-8.138932096 vs -3.08 - 32Pontuação#14figura da resposta próximafalhouregistrada
3.004825053 vs 1.27
Épocas × verificações
| # | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Época 1 | passou | passou | falhou | passou | passou | passou | passou | passou | passou | passou | passou | falhou | falhou | falhou | falhou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1falhoupontuada(exibindo)
Downloads desta tarefa
Pacote da tarefa (.zip)
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b136e7dad38d5a9733653cb405a5fddbc212b4ed6365ccaf6a02463875c5a428O 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.