Tarefa causcibench_real.q028

Veredito

Pergunta feita ao analista

Did an increase in minimum wage decrease employment?

Como o agente resolveu e como foi validado

Resumo passo a passo do que o agente executou, por que o caminho está correto e como cada verificação conferiu o resultado.

1. 1Preparação da base de dados
   card_minimum_wages— The dataset was collected to examine the effects of a minimum wage increase on fast food employment. It includes two survey waves from 410 fast food restaurants such as Burger King, KFC, Wendy's, and Roy Rogers in New Jersey and Pennsylvania. The first wave took place just before the April 1992 wage increase, and the second occurred seven to eight months later. The survey gathered information on employment, wages, prices, store characteristics, and location. Data collection involved phone interviews. The fast food sector was selected for its high share of low-wage jobs, standardization, and compliance with wage laws. The variables are: sheet: unique store identifier chain: chain identifier (1 = Burger King, 2 = KFC, 3 = Roy Rogers, 4 = Wendy's) co_owned: 1 if the store is company-owned, 0 otherwise state: 1 if the store is in New Jersey, 0 if in Pennsylvania after: 1 if the data was collected after an increase in minimum wage, 0 otherwise fte: number of full time employees southj: 1 if the store is in southern New Jersey centralj: 1 if the store is in central New Jersey northj: 1 if the store is in northern New Jersey pa1: 1 if the store is in northeast suburbs of Philadelphia pa2: 1 if the store is in Easton or nearby areas in Pennsylvania shore: 1 if the store is on the New Jersey shore ncalls: number of callbacks before completing the first interview empft: number of full-time employees (first interview) emppt: number of part-time employees (first interview) nmgrs: number of managers or assistant managers (first interview) wage_st: starting wage in dollars per hour (first interview) inctime: months until usual first raise (first interview) firstinc: amount of usual first raise in dollars per hour (first interview) bonus: 1 if the store offers a cash bonus for new workers pctaff: percent of employees affected by the new minimum wage meals: code for meal benefits (0 = none, 1 = free, 2 = reduced price, 3 = both) open: hour the store opens (first interview) hrsopen: number of hours the store is open per day (first interview) psoda: price of a medium soda, including tax (first interview) pfry: price of small fries, including tax (first interview) pentree: price of an entree including tax (first interview) nregs: number of cash registers in the store (first interview) nregs11: number of registers open at 11:00 am (first interview) type2: mode of second interview (1 = phone, 2 = personal) status2: status of second interview (0 = refused, 1 = answered, 2-5 = closed for various reasons) date2: date of second interview (mmddyy format) ncalls2: number of callbacks before completing the second interview empft2: number of full-time employees (second interview) emppt2: number of part-time employees (second interview) nmgrs2: number of managers or assistant managers (second interview) wage_st2: starting wage in dollars per hour (second interview) inctime2: months until usual first raise (second interview) firstin2: amount of usual first raise in dollars per hour (second interview) special2: 1 if the store has a special program for new workers (second interview) meals2: code for meal benefits (second interview; same coding as meals) open2r: hour the store opens (second interview) hrsopen2: number of hours the store is open per day (second interview) psoda2: price of a medium soda, including tax (second interview) pfry2: price of small fries, including tax (second interview) pentree2: price of an entree including tax (second interview) nregs2: number of cash registers in the store (second interview) nregs112: number of registers open at 11:00 am (second interview)
2. 2Chamada que gerou a resposta finalinfer.did_2x2(Diferenças em diferenças (2 grupos × 2 períodos))citada na respostacoincide com a referência

   Ver chamada call_9fa80e13 nos detalhes

   causcibench_metadata_rule/1: did

   Por que este é o caminho de referência: causcibench_metadata_rule/1: did
   Argumentos principais

   dataset:

   "card_minimum_wages"

   level:

   0,950.95

   post:

   "post"

   treated:

   "state"

   y:

   "fte"

   Resultado calculado

   • did = 2,75361 2.7536057829805816
     Difference-in-differences effect on fte
   Diagnósticos: design = "stacked" (stacked rows: treated × post interaction) · n_obs = 794794 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1)) · parallel_trends = "untestable" (with two periods parallel trends cannot be tested; the estimate rests on assuming them)
3. 3Conferência de lastro e resposta final

   Todos os 1 números escritos na resposta foram conferidos contra as ferramentas e o enunciado (nenhum número sem lastro).

   Números encontrados: 2,754
   Figuras citadas: call_9fa80e13:did
   • ✓
     #0resposta com lastro

     every number in the answer was stated by a tool call
   • ✓
     #1figura da resposta próxima

     CauSciBench effect accuracy: |answer figure − gold| ≤ 5% of |gold|

Valores esperados e de onde vêm

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 an increase in minimum wage decrease employment?

   card_minimum_wages (agent)

   The dataset was collected to examine the effects of a minimum wage increase on fast food employment. It includes two survey waves from 410 fast food restaurants such as Burger King, KFC, Wendy's, and Roy Rogers in New Jersey and Pennsylvania. The first wave took place just before the April 1992 wage increase, and the second occurred seven to eight months later. The survey gathered information on employment, wages, prices, store characteristics, and location. Data collection involved phone interviews. The fast food sector was selected for its high share of low-wage jobs, standardization, and compliance with wage laws. The variables are: sheet: unique store identifier chain: chain identifier (1 = Burger King, 2 = KFC, 3 = Roy Rogers, 4 = Wendy's) co_owned: 1 if the store is company-owned, 0 otherwise state: 1 if the store is in New Jersey, 0 if in Pennsylvania after: 1 if the data was collected after an increase in minimum wage, 0 otherwise fte: number of full time employees southj: 1 if the store is in southern New Jersey centralj: 1 if the store is in central New Jersey northj: 1 if the store is in northern New Jersey pa1: 1 if the store is in northeast suburbs of Philadelphia pa2: 1 if the store is in Easton or nearby areas in Pennsylvania shore: 1 if the store is on the New Jersey shore ncalls: number of callbacks before completing the first interview empft: number of full-time employees (first interview) emppt: number of part-time employees (first interview) nmgrs: number of managers or assistant managers (first interview) wage_st: starting wage in dollars per hour (first interview) inctime: months until usual first raise (first interview) firstinc: amount of usual first raise in dollars per hour (first interview) bonus: 1 if the store offers a cash bonus for new workers pctaff: percent of employees affected by the new minimum wage meals: code for meal benefits (0 = none, 1 = free, 2 = reduced price, 3 = both) open: hour the store opens (first interview) hrsopen: number of hours the store is open per day (first interview) psoda: price of a medium soda, including tax (first interview) pfry: price of small fries, including tax (first interview) pentree: price of an entree including tax (first interview) nregs: number of cash registers in the store (first interview) nregs11: number of registers open at 11:00 am (first interview) type2: mode of second interview (1 = phone, 2 = personal) status2: status of second interview (0 = refused, 1 = answered, 2-5 = closed for various reasons) date2: date of second interview (mmddyy format) ncalls2: number of callbacks before completing the second interview empft2: number of full-time employees (second interview) emppt2: number of part-time employees (second interview) nmgrs2: number of managers or assistant managers (second interview) wage_st2: starting wage in dollars per hour (second interview) inctime2: months until usual first raise (second interview) firstin2: amount of usual first raise in dollars per hour (second interview) special2: 1 if the store has a special program for new workers (second interview) meals2: code for meal benefits (second interview; same coding as meals) open2r: hour the store opens (second interview) hrsopen2: number of hours the store is open per day (second interview) psoda2: price of a medium soda, including tax (second interview) pfry2: price of small fries, including tax (second interview) pentree2: price of an entree including tax (second interview) nregs2: number of cash registers in the store (second interview) nregs112: number of registers open at 11:00 am (second interview)

   file d60fdf3cbe6dc79b86bff0c6499806848793e9b6a642643cd9d5fbb390a80732 · frame —
2. 1Turno do modelo
   call_e766cfd9referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: did

   Chamadas pedidas

   • infer.did_2x2

     {
       "controls": [],
       "dataset": "card_minimum_wages",
       "post": "post",
       "treated": "state",
       "y": "fte"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.did_2x2— Diferenças em diferenças (2 grupos × 2 períodos)v0.1.0call_9fa80e13decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: did

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "card_minimum_wages",
     "level": 0.95,
     "post": "post",
     "treated": "state",
     "y": "fte"
   }

   Figuras call_9fa80e13

   Chave	Valor	Valor bruto	Erro-padrão	IC
   did Difference-in-differences effect on fte	2,75361	2.7536057829805816	1,795451.7954508096057529	-0,770813 – 6,27802

   Diagnósticos

   • design = "stacked" (stacked rows: treated × post interaction)
   • n_obs = 794794
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • parallel_trends = "untestable" (with two periods parallel trends cannot be tested; the estimate rests on assuming them)
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   did

   1. Variação da média do grupo tratado menos a variação da média do grupo de comparação.DiD = (ȳ_T1 − ȳ_T0) − (ȳ_C1 − ȳ_C0)→ 2,75361 (2.7536057829805816)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 1,79545 (1.7954508096057529)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 790 graus de liberdade vezes o erro-padrão.IC = DiD ± c · ep

   Pressupostos do estimador

   • without the policy the treated group's outcome would have changed as much as the comparison group's (parallel trends)
   • no other change hit one group and not the other between the two periods

   Tabelas (1)

   {
     "means": [
       {
         "group": "treated",
         "mean": 20.439408099688475,
         "n": 321,
         "period": "before"
       },
       {
         "group": "treated",
         "mean": 21.02742946708464,
         "n": 319,
         "period": "after"
       },
       {
         "group": "comparison",
         "mean": 23.33116883116883,
         "n": 77,
         "period": "before"
       },
       {
         "group": "comparison",
         "mean": 21.165584415584416,
         "n": 77,
         "period": "after"
       }
     ]
   }

   • 26 rows were dropped because one of 'fte', 'state', 'post' was missing
   • with two periods parallel trends cannot be tested

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   card_minimum_wages 9ecd0139eedcfa298eb4f431b84bcea62de46362ee01b1f2e959eaddda54d225

   sha256 do resultado

   65d4160e7bd20426a45f52b9c189b04843d5e74b21dcb24b4ea84386a60401a8

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_f8033038referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.did_2x2): did = 2,754.",
       "caveats": [],
       "figure_refs": [
         "call_9fa80e13:did"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Guarda
   resposta aceita

   Números encontrados

   2,754

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
6. 5Resposta

   Estimativa (infer.did_2x2): did = 2,754.

   Figuras citadas: call_9fa80e13:did · Parada: answered

   Figura da resposta: call_9fa80e13:did = 2,753612.7536057829805816 (se 1,795451.7954508096057529)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória2.753605783 vs 2.754

Épocas × verificações

Tentativas

Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

• época 1, tentativa 1passoupontuada(exibindo)

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