Tarefa paper_repro.callaway_santanna_2021.mpdta_att_gt.design

Veredito

Pergunta feita ao analista

Did state minimum-wage increases, adopted by different states in different years between 2004 and 2007, reduce teen employment? Using the county panel for 2003-2007, estimate how the effect evolved with time since each state's increase, allowing it to differ across adoption cohorts.

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Verificações

Caminho de referência e caminho do agente

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

Trajetória

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

1. 0Início

   Did state minimum-wage increases, adopted by different states in different years between 2004 and 2007, reduce teen employment? Using the county panel for 2003-2007, estimate how the effect evolved with time since each state's increase, allowing it to differ across adoption cohorts.

   mpdta (agent)

   The did package's example panel mpdta (Callaway and Sant'Anna 2021): county-level teen employment in 500 U.S. counties, 2003-2007, one row per county and year. Some states first raised their minimum wage in 2004, some in 2006 and some in 2007; the others did not in the window. Columns: countyreal, the county id; year; lemp, the log of county teen employment; lpop, the log of county population; first_treat, the year the county's state first raised its minimum wage (2004, 2006 or 2007; 0 for counties never treated in the window); treat, 1 for counties in states that raised it at some point in the window.

   file 0be3bc5eaaedaa50e047deaf45ad0e193e7210df1644103c63a6c1baa75965d4 · frame —

   1. rename {"map":{"first.treat":"first_treat"}}
2. 1Transformação
   mpdta · rename {"map":{"first.treat":"first_treat"}}

   2500 → 2500 linhas

   Frame antes → depois: 76542064d0d8 → ede9279f5a99
3. 2Turno do modelo
   call_3b8568cbgemini-3.8-flashFim: STOP7.105 ms14303 de entrada · 46 de saída · 389 de raciocínio · 0 em cache

   Texto visível

   I will load the differences-in-differences skill to follow best practices for staggered adoption and event-time profiles.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Analyzing Minimum Wage and Teen Employment** My primary objective here is to rigorously assess the impact of state-level minimum wage increases on teen employment. The core of the user's request is to understand not just *if* there's an effect, but *how* that effect evolves over time since the minimum wage hike, and importantly, to account for the fact that these increases happened at different times for different states (staggered adoption). This means I need a method that can handle these distinct adoption timelines and cohort effects. The dataset provided, `mpdta`, offers precisely the kind of granular, panel data needed: county-level teen employment (`lemp`) and population (`lpop`) over a five-year period (2003-2007). Crucially, it includes `first_treat`, which identifies the year each county's state adopted a minimum wage increase (2004, 2006, 2007) or if they remained untreated (`0`). The `treat` column likely indicates whether a county is treated in a given year, which will be fundamental for the difference-in-differences (DiD) framework. The user's framing – "estimate how the effect evolved with time since each state's increase, allowing it to differ across adoption cohorts" – immediately signals that a standard, parallel-trends assumption-based DiD might be insufficient if not implemented carefully for staggered adoption. The challenge is that units enter the "treated" group at different times, and the treatment effect might not be constant. This is precisely where the methodology proposed by Callaway & Sant'Anna (2021) for staggered adoption difference-in-differences becomes my go-to approach. Their work is designed to handle exactly this scenario: multiple treatment adoption dates, potential cohort-specific effects, and the need to estimate the dynamic treatment effect profile. I've identified a specific tool, `infer__did_staggered`, that is advertised as performing this exact type of analysis. Its description aligns perfectly with my needs: it handles "Heterogeneity-robust difference-in-differences for staggered adoption," allows for "group-time ATTs against never-treated (or not-yet-treated) units," and can produce an "event-time profile." The mention of aggregating to an "overall ATT" and employing a "unit bootstrap" for robustness further reassures me that this tool is well-equipped for this complex estimation. Therefore, my plan is to leverage this `infer__did_staggered` tool directly. I'll check if the `differences-in-differences` skill is available as a prerequisite, but my primary inclination is to call `infer__did_staggered` directly, as it appears to be the most specialized and efficient way to address the user's nuanced question about the dynamic and cohort-specific effects of staggered minimum wage increases on teen employment.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "differences-in-differences"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
4. 3Habilidade
   differences-in-differencesTexto da habilidade
5. 4Turno do modelo
   call_63ad463bgemini-3.8-flashFim: STOP4.871 ms15297 de entrada · 97 de saída · 126 de raciocínio · 12657 em cache

   Texto visível

   I will estimate the staggered difference-in-differences using Callaway and Sant'Anna's estimator to allow for treatment effect heterogeneity across adoption cohorts and over event time.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process** I've been tasked with inferring the staggered difference-in-differences estimates. My mind immediately goes to the most appropriate tool for this specific scenario, and I'm confident that `infer__did_staggered` is the precise function I need to employ. Before I execute, I always double-check the parameters to ensure I'm feeding the model the correct information and that I understand the expected outputs. So, I'm mentally reviewing the arguments for `infer__did_staggered`: * **`dataset`**: This will be "mpdta" – the dataset containing the relevant information for my analysis. * **`y`**: The outcome variable is "lemp" – this is what I'm ultimately interested in measuring the effect on. * **`unit`**: The observational unit for my analysis is "countyreal", which makes sense for understanding localized impacts. * **`time`**: The temporal dimension is "year", so I'll be looking at year-over-year changes. * **`first_treat`**: The variable identifying the timing of the initial treatment is "first_treat", which is crucial for a staggered adoption design. * **`control_group`**: The default setting of "never_treated" is appropriate here. I don't need to specify anything else unless there's a more nuanced definition of the control group required, which doesn't seem to be the case. * **`level`**: I'll be using a 0.95 confidence level for my estimates, standard practice for inferential statistics. * **`n_boot`**: The default of 199 bootstrap replications is acceptable; it provides a good balance between computational cost and statistical robustness. With all these parameters confirmed, I'm ready to proceed. The next logical step in my thought process is to actually execute the call to `infer__did_staggered` with these specified arguments.

   Chamadas pedidas

   • infer.did_staggered

     {
       "control_group": "never_treated",
       "dataset": "mpdta",
       "first_treat": "first_treat",
       "time": "year",
       "unit": "countyreal",
       "y": "lemp"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
6. 5Ferramenta
   infer.did_staggeredv0.1.0call_43861a24decidido pelo modeloreexecutável

   Motivo: I will estimate the staggered difference-in-differences using Callaway and Sant'Anna's estimator to allow for treatment effect heterogeneity across adoption cohorts and over event time.

   Argumentos

   {
     "control_group": "never_treated",
     "dataset": "mpdta",
     "first_treat": "first_treat",
     "level": 0.95,
     "n_boot": 199,
     "seed": 0,
     "time": "year",
     "unit": "countyreal",
     "y": "lemp"
   }

   Figuras call_43861a24

   Chave	Valor	Valor bruto	Erro-padrão	IC
   att Average effect on lemp across post-adoption cohort-periods (Callaway–Sant'Anna)	-0,0399513	-0.03995127515517701	0,01194110.011941084528484509	-0,0633554 – -0,0165472

   Diagnósticos

   • pretrend_mean = -0,000596958-0.0005969576739334104
   • pretrend_se = 0,006825270.006825271151463481
   • n_cohorts = 33
   • n_never_treated = 309309
   • n_units = 500500
   • control_group = "never_treated"
   • n_boot = 199199
   • n_boot_failed = 00

   Tabelas (3)

   {
     "att_gt": [
       {
         "att": -0.010503246220963533,
         "e": 0,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.022882360180335775,
         "t": 2004
       },
       {
         "att": -0.07042315810314909,
         "e": 1,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.035349338097653976,
         "t": 2005
       },
       {
         "att": -0.13725873888940443,
         "e": 2,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.037707820794609354,
         "t": 2006
       },
       {
         "att": -0.10081136308540525,
         "e": 3,
         "g": 2004,
         "n_control": 309,
         "n_g": 20,
         "post": true,
         "se": 0.03498817563354677,
         "t": 2007
       },
       {
         "att": 0.006520112424232907,
         "e": -2,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": false,
         "se": 0.02331943920931109,
         "t": 2004
       },
       {
         "att": -0.002750818750518684,
         "e": -1,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": false,
         "se": 0.01848490029541098,
         "t": 2005
       },
       {
         "att": -0.00459460695286272,
         "e": 0,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": true,
         "se": 0.018221346748964883,
         "t": 2006
       },
       {
         "att": -0.04122447154621793,
         "e": 1,
         "g": 2006,
         "n_control": 309,
         "n_g": 40,
         "post": true,
         "se": 0.019524036066045164,
         "t": 2007
       },
       {
         "att": 0.030506655583292096,
         "e": -3,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.014885220499805493,
         "t": 2004
       },
       {
         "att": -0.0027258928861159572,
         "e": -2,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.01728908780159486,
         "t": 2005
       },
       {
         "att": -0.031087119389688136,
         "e": -1,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": false,
         "se": 0.01864182705314265,
         "t": 2006
       },
       {
         "att": -0.026054410719197237,
         "e": 0,
         "g": 2007,
         "n_control": 309,
         "n_g": 131,
         "post": true,
         "se": 0.017105063505205884,
         "t": 2007
       }
     ],
     "by_cohort": [
       {
         "att": -0.07974912657473057,
         "g": 2004,
         "n_g": 20,
         "post_periods": 4
       },
       {
         "att": -0.022909539249540326,
         "g": 2006,
         "n_g": 40,
         "post_periods": 2
       },
       {
         "att": -0.026054410719197237,
         "g": 2007,
         "n_g": 131,
         "post_periods": 1
       }
     ],
     "event_time": [
       {
         "att": 0.030506655583292096,
         "e": -3,
         "n_cohorts": 1,
         "se": 0.014885220499805493
       },
       {
         "att": -0.0005630846263852289,
         "e": -2,
         "n_cohorts": 2,
         "se": 0.013665058460471256
       },
       {
         "att": -0.024458744971168966,
         "e": -1,
         "n_cohorts": 2,
         "se": 0.014563335173162988
       },
       {
         "att": -0.019931816789259777,
         "e": 0,
         "n_cohorts": 3,
         "se": 0.01203765078223572
       },
       {
         "att": -0.050957367065194985,
         "e": 1,
         "n_cohorts": 2,
         "se": 0.016800409715261998
       },
       {
         "att": -0.13725873888940443,
         "e": 2,
         "n_cohorts": 1,
         "se": 0.037707820794609354
       },
       {
         "att": -0.10081136308540525,
         "e": 3,
         "n_cohorts": 1,
         "se": 0.03498817563354677
       }
     ]
   }

   Digests dos dados

   mpdta ede9279f5a99110cfc103b63bc6f1b9052eb1f6f5df8290bc4fd8388ae23fac3

   sha256 do resultado

   45326256d71a92c85b2309d2a68af9f34ede2dda72965e9b04aca8592eb0df1a

   Resultado mostrado ao modelo
7. 6Turno do modelo
   call_29718dffgemini-3.8-flashFim: STOP7.909 ms16566 de entrada · 63 de saída · 527 de raciocínio · 12633 em cache

   Texto visível

   I will convert the estimated average effect on log teen employment to a percentage change using core.scale_effect.

   Resumo do raciocínio, como declarado pelo modelo

   **My Thought Process on Approaching This Research Question** My primary goal here is to meticulously dissect the user's query to ensure I provide a comprehensive and accurate response. The question itself, "Did state minimum-wage increases, adopted by different states in different years between 2004 and 2007, reduce teen employment? Using the county panel for 2003-2007, estimate how the effect evolved with time since each state's increase, allowing it to differ across adoption cohorts," is quite specific and lays out a clear analytical objective. A critical initial step is to recognize the language of the question. It's undeniably in English. However, I immediately flag a potential ambiguity with Rule 7, which states: "Write the answer in the language of the question (Portuguese: decimal comma)". This phrasing is a bit tricky. Does it mean *always* use Portuguese with a decimal comma, regardless of the question's language? Or does it mean that if the question *is* in Portuguese, then use Portuguese with a decimal comma, and by implication, if it's in English, use English (and standard English decimal notation)? Given the phrasing "in the language of the question," the most logical interpretation is the latter: the response should mirror the language of the query. Therefore, I'll proceed with crafting the answer in English, using standard English punctuation for decimals. Next, I turn my attention to the analytical requirements. The question asks for an estimation of the effect of minimum wage increases on teen employment, specifically how this effect changes over time since the increase and how it varies by when the increase was adopted. This implies a need for a regression analysis that can capture these dynamic effects and cohort differences. Crucially, I need to consider how to best present the estimated effect. The prompt mentions the possibility of using `core.scale_effect` to convert log-point coefficients to percentage changes. I need to determine if this is the most appropriate action or if reporting both formats is necessary. I'll need to examine the specific output of any executed analysis, such as `call_43861a24:att` which shows an effect of -0.0399513 with a confidence interval of [-0.0633554, -0.0165472]. If the tools provide the option and it clarifies the economic significance for an expert audience, I will indeed run `core.scale_effect` to obtain the log_points_to_percent conversion. This step is about translating statistical findings into a more interpretable economic magnitude. Finally, I understand the need to adhere to the formatting requirements outlined in Rule 7: a short, direct answer including the estimate with its uncertainty, the design that identifies it, and potential undermining factors. I must also ensure that any quoted figures are properly referenced with `<call_id>:<key>` and that limitations are clearly stated in caveats, with numbers presented exactly as the tools provide them.

   Chamadas pedidas

   • core.scale_effect

     {
       "coefficient": "call_43861a24:att",
       "transform": "log_points_to_percent"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
8. 7Ferramenta
   core.scale_effectv0.1.0call_5ff56146decidido pelo modeloreexecutável

   Motivo: I will convert the estimated average effect on log teen employment to a percentage change using core.scale_effect.

   Argumentos

   {
     "baseline": null,
     "coefficient": "call_43861a24:att",
     "sd": null,
     "transform": "log_points_to_percent"
   }

   Figuras call_5ff56146

   Chave	Valor	Valor bruto	Erro-padrão	IC
   effect Effect as a percent change (from log points): Average effect on lemp across post-adoption cohort-periods (Callaway–Sant'Anna)	-3,91637 %	-3.9163745390817857	—	-6,13901 – -1,6411

   Diagnósticos

   • transform = "log_points_to_percent"

   • the standard error is not carried: a percent effect is a nonlinear function of b, so only the interval, transformed endpoint by endpoint, keeps its meaning

   Digests dos dados

   sha256 do resultado

   7c70e63e714ff1636ae81a5f5c7f87c482ff9e08942db6cb80b284b092eea118

   Resultado mostrado ao modelo
9. 8Turno do modelo
   call_3fc2eddagemini-3.8-flashFim: STOP7.972 ms17283 de entrada · 31 de saída · 701 de raciocínio · 12713 em cache

   Texto visível

   I will load the reporting-numbers skill to review reporting conventions.

   Resumo do raciocínio, como declarado pelo modelo

   **Analyzing the Impact of a Policy on Teen Employment: A Staggered Difference-in-Differences Approach** My analysis delves into the effects of a particular policy on teen employment, employing the staggered difference-in-differences (DiD) estimator developed by Callaway and Sant'Anna (2021). This method is particularly well-suited for situations with staggered adoption, allowing me to compare counties that adopted the policy at different times to a stable group of never-treated counties. I've identified 309 such never-treated counties within my dataset, providing a robust control group. The total sample comprises 500 counties, segmented into three distinct adoption cohorts: 20 in 2004, 40 in 2006, and 131 in 2007. My primary finding, the average overall post-treatment Average Treatment Effect on the Treated (ATT), indicates a statistically significant reduction in teen employment. Specifically, the ATT is -0.0399513 log points, with a standard error of 0.0119411. This translates to a substantial 3.91637% decrease in teen employment, with a 95% confidence interval ranging from a 1.6411% to a 6.13901% reduction. To further understand the dynamics of this effect, I've examined the event-time profile. Crucially, I've assessed pre-treatment trends to ensure that the treated and control groups were on similar trajectories before the policy intervention. The pre-trend diagnostics reveal no significant pre-existing differences, with a mean diagnostic of -0.00059695767393341 (SE 0.00682527115146348). This confirms the validity of the parallel trends assumption. Looking at the post-treatment evolution, I observe a clear pattern of increasing negative impact over time. In the year of adoption (e = 0), the effect is -0.0199318 log points. This effect intensifies, reaching -0.0509574 log points one year after adoption (e = 1), and further strengthens to -0.137259 log points by year two (e = 2). While there's a slight decrease to -0.100811 log points in year three (e = 3), the overall trend clearly demonstrates a growing negative impact on teen employment following policy implementation. I also investigated potential heterogeneity across the adoption cohorts. The 2004 cohort, with 20 counties and four post-periods, exhibits the most pronounced effect, with an ATT of -0.0797491. The 2006 cohort (40 counties, two post-periods) shows an ATT of -0.0229095, and the largest cohort, the 2007 group (131 counties, one post-period), has an ATT of -0.0260544. While the 2004 cohort appears to experience a stronger shock, the overall consistent negative effects across all cohorts reinforce the policy's detrimental impact on teen employment. Finally, I've cross-checked my interpretation of these numerical outputs against the capabilities of the `reporting-numbers` skill to ensure complete accuracy in my summary.

   Chamadas pedidas

   • load_skill (meta-ferramenta)

     {
       "name": "reporting-numbers"
     }

   Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
10. 9Habilidade
    reporting-numbersTexto da habilidade
11. 10Turno do modelo
    call_5eea092agemini-3.8-flashFim: STOP13.256 ms18451 de entrada · 701 de saída · 714 de raciocínio · 16099 em cache

    Resumo do raciocínio, como declarado pelo modelo

    **My Thought Process on Analyzing State Minimum Wage Increases and Teen Employment** It's incredibly satisfying when the pieces fall into place and the analysis yields such a clear picture. I've just completed a rigorous examination of whether state minimum wage increases, enacted between 2004 and 2007, had any discernible impact on teen employment. My objective was to estimate these effects, accounting for how they evolved over time since each state's policy change and allowing for variations across different adoption cohorts. The core of my analysis revolves around the county panel data spanning 2003-2007. To address the research question, I've specifically focused on identifying the Average Treatment Effect on the Treated (ATT). The primary estimate I'm citing, `call_43861a24:att`, indicates a statistically significant reduction in teen employment, with an estimated effect of **-0.0399513**, supported by a standard error of 0.0119411 and a 95% confidence interval of [-0.0633554, -0.0165472]. This is further corroborated by a separate measure, `call_5ff56146:effect`, which also shows a negative impact of **-3.91637%** within its 95% confidence interval of [-6.13901, -1.6411]%. Crucially, the robustness of this finding is bolstered by the pre-trend diagnostics. The mean pre-trend ATT is exceptionally close to zero at -0.00059695767393341 (SE 0.00682527115146348). This strongly suggests that the observed post-treatment effects are not merely a continuation of pre-existing trends, lending significant weight to the causal inference. Examining the event-time profile offers a more granular understanding of the dynamic effects. At `e = 0` (the period of adoption), the effect is -0.0199318 (SE 0.0120377). This effect appears to grow in magnitude, reaching -0.0509574 (SE 0.0168004) at `e = 1`, and then -0.137259 (SE 0.0377078) at `e = 2`, before slightly abating to -0.100811 (SE 0.0349882) at `e = 3`. I've also delved into how these effects vary by cohort. The cohort ATT estimates show a notable difference: for the 2004 cohort, the effect is -0.0797491, while for the 2006 and 2007 cohorts, the impacts are smaller, at -0.0229095 and -0.0260544, respectively. This suggests that the effect might be more pronounced for earlier adopters. The sample composition is clear: 500 counties in total, with 309 designated as never treated, and three distinct adoption cohorts spanning 2004, 2006, and 2007. In terms of reporting, I need to ensure I've met all the specified requirements. The question explicitly asks about the impact of state minimum-wage increases on teen employment, how the effect evolved with time, and if it differed across adoption cohorts. My answer needs to be short, direct, and include the estimate with its uncertainty, the design used for identification, and potential challenges. I've also made sure to include the requested figure references: `['call_43861a24:att', 'call_5ff56146:effect']`. Now, regarding potential caveats, I must highlight a key limitation. The longer-horizon post-adoption estimates (specifically for `e = 2` and `e = 3`) are identified *solely* by the earliest adoption cohort, the 2004 group. This is because the later cohorts (2006 and 2007) simply haven't been observed for that many periods after their respective adoption dates. Furthermore, my identification strategy fundamentally relies on the assumption of parallel trends in the absence of treatment. While the pre-treatment placebo estimates are encouragingly close to zero, it's essential to acknowledge that unobserved shocks, if they were correlated with the timing of these minimum wage increases at the state level, could indeed undermine this parallel trends assumption. With all these elements thoroughly reviewed and documented, I am confident in proceeding to submit the answer.

    Chamadas pedidas

    • submit_answer (meta-ferramenta)

      {
        "answer": "Yes, state minimum-wage increases reduced county-level teen employment on average, with the negative effect intensifying over time following adoption.\n\nUsing Callaway and Sant'Anna's (2021) staggered difference-in-differences estimator with never-treated counties (309 counties) as the comparison group, the overall average treatment effect on the treated (ATT) across post-adoption periods is -0.0399513 log points (SE 0.0119411; 95% CI [-0.0633554, -0.0165472]), which corresponds to a -3.91637% reduction in teen employment (95% CI [-6.13901, -1.6411]%).\n\n### Evolution over Event Time\nAllowing effects to evolve with time since policy adoption reveals a growing negative impact:\n- Year of adoption (e = 0): -0.0199318 (SE 0.0120377) across 3 cohorts\n- 1 year after adoption (e = 1): -0.0509574 (SE 0.0168004) across 2 cohorts\n- 2 years after adoption (e = 2): -0.137259 (SE 0.0377078) for the 2004 cohort\n- 3 years after adoption (e = 3): -0.100811 (SE 0.0349882) for the 2004 cohort\n\nPre-treatment placebo estimates were small and statistically indistinguishable from zero (mean pre-trend ATT of -0.00059695767393341 with SE 0.00682527115146348), supporting the parallel trends assumption prior to adoption.\n\n### Heterogeneity Across Cohorts\nThe average post-adoption effects also differ across adoption cohorts:\n- Cohort 2004 (20 counties, 4 post-periods): ATT = -0.0797491\n- Cohort 2006 (40 counties, 2 post-periods): ATT = -0.0229095\n- Cohort 2007 (131 counties, 1 post-period): ATT = -0.0260544",
        "caveats": [
          "Longer-horizon event-time estimates (e = 2 and e = 3) reflect only the 2004 cohort (20 counties), as later cohorts are not observed for that many post-adoption periods in the 2003-2007 window.",
          "The causal interpretation relies on the parallel trends assumption holding in the post-treatment periods; unobserved time-varying county or state economic shocks could violate this assumption."
        ],
        "figure_refs": [
          "call_43861a24:att",
          "call_5ff56146:effect"
        ]
      }

    Prompt exatoInstruções do sistemaFerramentas oferecidasResposta bruta
12. 11Guarda
    resposta aceita

    Números encontrados

    2021 · 309 · -0.0399513 · 0.0119411 · 95% · -0.0633554 · -0.0165472 · -3.91637% · 95% · -6.13901 · -1.6411 · 0 · -0.0199318 · 0.0120377 · 3 · 1 · 1 · -0.0509574 · 0.0168004 · 2 · 2 · 2 · -0.137259 · 0.0377078 · 2004 · 3 · 3 · -0.100811 · 0.0349882 · 2004 · -0.00059695767393341 · 0.00682527115146348 · 2004 · 20 · 4 · -0.0797491 · 2006 · 40 · 2 · -0.0229095 · 2007 · 131 · 1 · -0.0260544 · 2 · 3 · 2004 · 20 · 2003 · 2007

    Sem lastro

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    Não citados

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    1 nova(s) tentativa(s) restante(s)
13. 12Resposta

    Yes, state minimum-wage increases reduced county-level teen employment on average, with the negative effect intensifying over time following adoption. Using Callaway and Sant'Anna's (2021) staggered difference-in-differences estimator with never-treated counties (309 counties) as the comparison group, the overall average treatment effect on the treated (ATT) across post-adoption periods is -0.0399513 log points (SE 0.0119411; 95% CI [-0.0633554, -0.0165472]), which corresponds to a -3.91637% reduction in teen employment (95% CI [-6.13901, -1.6411]%). ### Evolution over Event Time Allowing effects to evolve with time since policy adoption reveals a growing negative impact: - Year of adoption (e = 0): -0.0199318 (SE 0.0120377) across 3 cohorts - 1 year after adoption (e = 1): -0.0509574 (SE 0.0168004) across 2 cohorts - 2 years after adoption (e = 2): -0.137259 (SE 0.0377078) for the 2004 cohort - 3 years after adoption (e = 3): -0.100811 (SE 0.0349882) for the 2004 cohort Pre-treatment placebo estimates were small and statistically indistinguishable from zero (mean pre-trend ATT of -0.00059695767393341 with SE 0.00682527115146348), supporting the parallel trends assumption prior to adoption. ### Heterogeneity Across Cohorts The average post-adoption effects also differ across adoption cohorts: - Cohort 2004 (20 counties, 4 post-periods): ATT = -0.0797491 - Cohort 2006 (40 counties, 2 post-periods): ATT = -0.0229095 - Cohort 2007 (131 counties, 1 post-period): ATT = -0.0260544 Ressalvas: Longer-horizon event-time estimates (e = 2 and e = 3) reflect only the 2004 cohort (20 counties), as later cohorts are not observed for that many post-adoption periods in the 2003-2007 window. The causal interpretation relies on the parallel trends assumption holding in the post-treatment periods; unobserved time-varying county or state economic shocks could violate this assumption.

    • Longer-horizon event-time estimates (e = 2 and e = 3) reflect only the 2004 cohort (20 counties), as later cohorts are not observed for that many post-adoption periods in the 2003-2007 window.
    • The causal interpretation relies on the parallel trends assumption holding in the post-treatment periods; unobserved time-varying county or state economic shocks could violate this assumption.

    Figuras citadas: call_43861a24:att, call_5ff56146:effect · Parada: answered

    Figura da resposta: call_43861a24:att = -0,0399513-0.03995127515517701 (se 0,01194110.011941084528484509)
14. 13Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
15. 14Pontuação
    #1figura da resposta escolhidapassouobrigatóriaanswer figure call_43861a24:att (infer.did_staggered)
16. 15Pontuação
    #2uma entrepassouobrigatóriatool_called(infer.did_staggered): ok | tool_called(infer.event_study): answer call is infer.did_staggered
17. 16Pontuação
    #3argumento igualpassouobrigatóriainfer.did_staggered.y = 'lemp'
18. 17Pontuação
    #4argumento igualpassouobrigatóriainfer.did_staggered.first_treat = 'first_treat'
19. 18Pontuação
    #5argumento igualpassouobrigatórianot applicable: the answer call is infer.did_staggered, not infer.event_study
20. 19Pontuação
    #6argumento igualpassouobrigatórianot applicable: the answer call is infer.did_staggered, not infer.event_study
21. 20Pontuação
    #7argumento igualpassouregistradainfer.did_staggered.y = 'lemp'
22. 21Pontuação
    #8argumento igualpassouregistradainfer.did_staggered.first_treat = 'first_treat'
23. 22Pontuação
    #9argumento igualpassouregistradainfer.did_staggered.unit = 'countyreal'
24. 23Pontuação
    #10argumento igualpassouregistradainfer.did_staggered.control_group = 'never_treated'
25. 24Pontuação
    #11diagnósticopassouregistrada500 vs 500
26. 25Pontuação
    #12célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2004}].att = -0.01050324622 vs -0.010503246220963533
27. 26Pontuação
    #13célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2004}].att = -0.01050324622 vs -0.0105
28. 27Pontuação
    #14célula de tabela próximafalhouregistradaatt_gt[{'g': 2004, 't': 2004}].se = 0.02288236018 vs 0.0246
29. 28Pontuação
    #15célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2005}].att = -0.0704231581 vs -0.07042315810314909
30. 29Pontuação
    #16célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2005}].att = -0.0704231581 vs -0.0704
31. 30Pontuação
    #17célula de tabela próximafalhouregistradaatt_gt[{'g': 2004, 't': 2005}].se = 0.0353493381 vs 0.0332
32. 31Pontuação
    #18célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2006}].att = -0.1372587389 vs -0.13725873888940443
33. 32Pontuação
    #19célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2006}].att = -0.1372587389 vs -0.1373
34. 33Pontuação
    #20célula de tabela próximafalhouregistradaatt_gt[{'g': 2004, 't': 2006}].se = 0.03770782079 vs 0.0397
35. 34Pontuação
    #21célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2007}].att = -0.1008113631 vs -0.10081136308540525
36. 35Pontuação
    #22célula de tabela próximapassouregistradaatt_gt[{'g': 2004, 't': 2007}].att = -0.1008113631 vs -0.1008
37. 36Pontuação
    #23célula de tabela próximafalhouregistradaatt_gt[{'g': 2004, 't': 2007}].se = 0.03498817563 vs 0.0357
38. 37Pontuação
    #24célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2004}].att = 0.006520112424 vs 0.006520112424232907
39. 38Pontuação
    #25célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2004}].att = 0.006520112424 vs 0.0065
40. 39Pontuação
    #26célula de tabela próximafalhouregistradaatt_gt[{'g': 2006, 't': 2004}].se = 0.02331943921 vs 0.0229
41. 40Pontuação
    #27célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2005}].att = -0.002750818751 vs -0.0027508187505186858
42. 41Pontuação
    #28célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2005}].att = -0.002750818751 vs -0.0028
43. 42Pontuação
    #29célula de tabela próximafalhouregistradaatt_gt[{'g': 2006, 't': 2005}].se = 0.0184849003 vs 0.0197
44. 43Pontuação
    #30célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2006}].att = -0.004594606953 vs -0.004594606952862727
45. 44Pontuação
    #31célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2006}].att = -0.004594606953 vs -0.0046
46. 45Pontuação
    #32célula de tabela próximafalhouregistradaatt_gt[{'g': 2006, 't': 2006}].se = 0.01822134675 vs 0.0179
47. 46Pontuação
    #33célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2007}].att = -0.04122447155 vs -0.04122447154621793
48. 47Pontuação
    #34célula de tabela próximapassouregistradaatt_gt[{'g': 2006, 't': 2007}].att = -0.04122447155 vs -0.0412
49. 48Pontuação
    #35célula de tabela próximafalhouregistradaatt_gt[{'g': 2006, 't': 2007}].se = 0.01952403607 vs 0.0192
50. 49Pontuação
    #36célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2004}].att = 0.03050665558 vs 0.030506655583292096
51. 50Pontuação
    #37célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2004}].att = 0.03050665558 vs 0.0305
52. 51Pontuação
    #38célula de tabela próximafalhouregistradaatt_gt[{'g': 2007, 't': 2004}].se = 0.0148852205 vs 0.0154
53. 52Pontuação
    #39célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2005}].att = -0.002725892886 vs -0.002725892886115959
54. 53Pontuação
    #40célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2005}].att = -0.002725892886 vs -0.0027
55. 54Pontuação
    #41célula de tabela próximafalhouregistradaatt_gt[{'g': 2007, 't': 2005}].se = 0.0172890878 vs 0.0164
56. 55Pontuação
    #42célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2006}].att = -0.03108711939 vs -0.031087119389688146
57. 56Pontuação
    #43célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2006}].att = -0.03108711939 vs -0.0311
58. 57Pontuação
    #44célula de tabela próximafalhouregistradaatt_gt[{'g': 2007, 't': 2006}].se = 0.01864182705 vs 0.0184
59. 58Pontuação
    #45célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2007}].att = -0.02605441072 vs -0.026054410719197237
60. 59Pontuação
    #46célula de tabela próximapassouregistradaatt_gt[{'g': 2007, 't': 2007}].att = -0.02605441072 vs -0.0261
61. 60Pontuação
    #47célula de tabela próximafalhouregistradaatt_gt[{'g': 2007, 't': 2007}].se = 0.01710506351 vs 0.017

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