Tarefa causcibench_real.q017

Veredito

Pergunta feita ao analista

How do castle doctrine laws affect (log) larceny rates across states while accounting for population size across the states?

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Trajetória

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1. 0Início

   How do castle doctrine laws affect (log) larceny rates across states while accounting for population size across the states?

   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_c33bae40referenceFim: 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_larceny"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_2c239daadecidido 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_larceny"
   }

   Figuras call_2c239daa

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.cdl Coefficient on cdl in the regression of l_larceny	0,00745354	0.007453541783759591	0,01084080.01084077138040986	-0,0138467 – 0,0287538

   Diagnósticos

   • n_obs = 550550
   • k = 6161
   • r2 = 0,9421690.9421694552487825
   • df_resid = 489489
   • se_type = "HC1"
   • n_clusters = —
   • fixed_effects = "sid (50 levels), year (11 levels)"

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 8.042058728384479,
         "ci_low": 7.97981391053148,
         "coef": 8.01093631945798,
         "p": 0,
         "se": 0.01583976993992868,
         "t": 505.74827474382175,
         "term": "Intercept"
       },
       {
         "ci_high": 0.028753782965250427,
         "ci_low": -0.013846699397731243,
         "coef": 0.007453541783759591,
         "p": 0.4920639509347995,
         "se": 0.01084077138040986,
         "t": 0.6875471792744137,
         "term": "cdl"
       }
     ]
   }

   Digests dos dados

   cheng_does_strengthening 1138e3007dbd4dbbf767590fa8e58b9a3e35a279edeb3fab5066342fdf58bb77

   sha256 do resultado

   d819b689d226fb193b7de64d4aa33cf977b1eb7192ac27544b87f4c5d9ffa1d6

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_7f4aa4abreferenceFim: 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 = 0,007454.",
       "caveats": [],
       "figure_refs": [
         "call_2c239daa:coef.cdl"
       ]
     }

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

   Números encontrados

   0,007454

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.cdl = 0,007454.

   Figuras citadas: call_2c239daa:coef.cdl · Parada: answered

   Figura da resposta: call_2c239daa:coef.cdl = 0,007453540.007453541783759591 (se 0,01084080.01084077138040986)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximafalhouobrigatória0.007453541784 vs 0.003

Épocas × verificações

Tentativas

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

• época 1, tentativa 1falhoupontuada(exibindo)

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