Tarefa causcibench_real.q016

Veredito

Pergunta feita ao analista

What is the effect of castle doctrine laws on (log) motor vehicle theft rates?

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Verificações

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

Trajetória

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1. 0Início

   What is the effect of castle doctrine laws on (log) motor vehicle theft rates?

   cheng_does_strengthening (agent)

   This dataset examines the impact of castle-doctrine statutes on violent crime rates in the United States. It combines state-level crime data from the FBI Uniform Crime Reports (UCR) Summary Files with information on the timing of castle-doctrine law implementation across states. The laws were adopted at different times, with 21 states enacting them between 2000 and 2010. Dataset Variables: year: Calendar year post: Indicator for post-treatment period sid: State ID robbery_gun_r: Region-quarter fixed effect (robbery with gun) jhcitizen_c: Justifiable homicides by private citizens jhpolice_c: Justifiable homicides by police officers homicide: Homicide rate per 100,000 state population robbery: Robbery rate per 100,000 state population assault: Aggravated assault rate per 100,000 state population burglary: Burglary rate per 100,000 state population larceny: Larceny rate per 100,000 state population motor: Motor vehicle theft rate per 100,000 state population murder: Murder rate per 100,000 state population l_homicide: Log of homicide rate l_robbery: Log of robbery rate l_assault: Log of aggravated assault rate l_burglary: Log of burglary rate l_larceny: Log of larceny rate l_motor: Log of motor vehicle theft rate l_murder: Log of murder rate unemployrt: State unemployment rate blackm_15_24: Percent of Black males aged 15 to 24 whitem_15_24: Percent of White males aged 15 to 24 blackm_25_44: Percent of Black males aged 25 to 44 whitem_25_44: Percent of White males aged 25 to 44 poverty: State poverty rate l_police: Logged police presence l_income: Logged income l_prisoner: Logged number of prisoners l_lagprisoner: Lagged log prisoners l_exp_subsidy: Logged subsidy spending l_exp_pubwelfare: Logged public welfare spending lead1, lead2, lead3, lead4, lead5, lead6, lead7, lead8, lead9, lag0, lag1, lag2, lag3, lag4, lag5: Indicators of how many time periods until/since treatment popwt: Population weight r20001, r20002, r20003, r20004, r20011, r20012, r20013, r20014, r20021, r20022, r20023, r20024, r20031, r20032, r20033: Region-quarter fixed effects trend_1, trend_10, trend_11, trend_12, trend_13, trend_14, trend_15, trend_16, trend_17, trend_18, trend_19: linear time trends

   file b84b3549a35f5322b7d6adaa8030c435fdcd26bbb63eea02e24883c1dc6f7fed · frame —
2. 1Turno do modelo
   call_c0a33066referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   causcibench_metadata_rule/1: did

   Chamadas pedidas

   • infer.ols

     {
       "controls": [],
       "dataset": "cheng_does_strengthening",
       "fe": [
         "sid",
         "year"
       ],
       "x": [
         "cdl"
       ],
       "y": "l_motor"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_334902aadecidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: did

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "cheng_does_strengthening",
     "fe": [
       "sid",
       "year"
     ],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "cdl"
     ],
     "y": "l_motor"
   }

   Figuras call_334902aa

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.cdl Coefficient on cdl in the regression of l_motor	0,0766538	0.07665384953479437	0,02104660.021046610079155148	0,0353009 – 0,118007

   Diagnósticos

   • n_obs = 550550
   • k = 6161
   • r2 = 0,9581550.9581547077665671
   • df_resid = 489489
   • se_type = "HC1"
   • n_clusters = —
   • fixed_effects = "sid (50 levels), year (11 levels)"

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 5.8545237660388745,
         "ci_low": 5.715802260791087,
         "coef": 5.785163013414981,
         "p": 0,
         "se": 0.035301199435346514,
         "t": 163.88006940133573,
         "term": "Intercept"
       },
       {
         "ci_high": 0.11800679897056152,
         "ci_low": 0.03530090009902723,
         "coef": 0.07665384953479437,
         "p": 0.0002992924836259781,
         "se": 0.021046610079155148,
         "t": 3.642099570738634,
         "term": "cdl"
       }
     ]
   }

   Digests dos dados

   cheng_does_strengthening 1138e3007dbd4dbbf767590fa8e58b9a3e35a279edeb3fab5066342fdf58bb77

   sha256 do resultado

   bec724f0f2522711a2877b26609c76a98b950b15f9b40d6a1ee5e1accfa263c1

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_b782855areferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.ols): coef.cdl = 0,07665.",
       "caveats": [],
       "figure_refs": [
         "call_334902aa:coef.cdl"
       ]
     }

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

   Números encontrados

   0,07665

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.cdl = 0,07665.

   Figuras citadas: call_334902aa:coef.cdl · Parada: answered

   Figura da resposta: call_334902aa:coef.cdl = 0,07665380.07665384953479437 (se 0,02104660.021046610079155148)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória0.07665384953 vs 0.077

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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