Tarefa paper_repro.cheng_hoekstra_2013.tab5_pa_c1.design

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Pergunta feita ao analista

Did the castle doctrine (stand-your-ground) laws that U.S. states adopted between 2000 and 2010, which widened the right to use lethal force in self-defence, deter crime or escalate violence? Using the state panel for 2000-2010, estimate the effect of these laws on the log homicide rate faced by the average U.S. resident.

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Verificações

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Trajetória

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1. 0Início

   Did the castle doctrine (stand-your-ground) laws that U.S. states adopted between 2000 and 2010, which widened the right to use lethal force in self-defence, deter crime or escalate violence? Using the state panel for 2000-2010, estimate the effect of these laws on the log homicide rate faced by the average U.S. resident.

   castle (agent)

   The state-year panel of Cheng and Hoekstra (2013), 'Does Strengthening Self-Defense Law Deter Crime or Escalate Violence? Evidence from Expansions to Castle Doctrine', as distributed with Cunningham's Causal Inference: The Mixtape: one row per U.S. state and year, 2000-2010 (550 rows: 50 states, 11 years). Columns used most: state, the state's name; sid, a numeric state id; year; cdl, the castle doctrine law variable as the paper codes it: the fraction of the year in which the state's castle doctrine (stand-your-ground) law was in effect, 0 before adoption and 1 in full years after it; post, 1 in every year from the adoption year on (any part of the year); effyear and treatment_date, the year the law took effect (missing for states that never adopted one); homicide, the homicide rate per 100,000 residents (FBI Uniform Crime Reports), and l_homicide its natural log; murder and l_murder, the murder rate and its log; population; popwt, the state's average population over 2000-2010 (constant within a state), the weight the paper's weighted regressions use; northeast, midwest, south, west, census region indicators; r20001 to r20104, region-by-year indicators named r20<two-digit year><region 1-4> (for example r20063 is region 3 in 2006); trend_1 to trend_51, state-specific linear trends; lead1 to lead9 and lag0 to lag5, event-time indicators relative to adoption; other crime rates (robbery, assault, burglary, larceny, motor) and their logs (l_robbery and so on); and the time-varying controls of the paper (police, unemployrt, income, poverty, prisoner, lagprisoner, exp_subsidy, exp_pubwelfare, and the shares of black and white men aged 15-24 and 25-44).

   file 804633c161827b6c0824f86f239046386d1a8266a866f83bf5ddb2aa762a5f29 · frame —
2. 1Turno do modelo
   call_249c4560referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Table 5, Panel A, column (1): log homicide rate on the castle doctrine law variable (the fraction of the year in effect) with state and year fixed effects, weighted by population, errors clustered by state.

   Chamadas pedidas

   • infer.ols

     {
       "cluster": "sid",
       "controls": [],
       "dataset": "castle",
       "fe": [
         "sid",
         "year"
       ],
       "weights": "popwt",
       "x": [
         "cdl"
       ],
       "y": "l_homicide"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.olsv0.1.0call_1eee45ebdecidido pelo modeloreexecutável

   Motivo: Table 5, Panel A, column (1): log homicide rate on the castle doctrine law variable (the fraction of the year in effect) with state and year fixed effects, weighted by population, errors clustered by state.

   Argumentos

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

   Figuras call_1eee45eb

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.cdl Coefficient on cdl in the regression of l_homicide	0,0800635	0.08006351033824458	0,03589850.0358984941171576	0,00792279 – 0,152204

   Diagnósticos

   • n_obs = 550550
   • k = 6161
   • r2 = 0,9341670.9341667643844439
   • df_resid = 489489
   • se_type = "cluster"
   • n_clusters = 5050
   • fixed_effects = "sid (50 levels), year (11 levels)"

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 2.0246397884543303,
         "ci_low": 1.9368211431033613,
         "coef": 1.9807304657788458,
         "p": 3.0984228479904365e-56,
         "se": 0.021850051624944748,
         "t": 90.65106571728086,
         "term": "Intercept"
       },
       {
         "ci_high": 0.15220423516631415,
         "ci_low": 0.007922785510174987,
         "coef": 0.08006351033824458,
         "p": 0.030341540853612167,
         "se": 0.0358984941171576,
         "t": 2.2302748989122194,
         "term": "cdl"
       }
     ]
   }

   Digests dos dados

   castle 6a9af2b4a14b985d82e0f3aea93c535c24cfc2497c4d9f07242bac5f84ae11c1

   sha256 do resultado

   3540fa80248a046b519fbf92841d79616e4d2e6b0d314c558871db2e2229d37a

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_265e7bfcreferenceFim: 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 = 0,08006.",
       "caveats": [],
       "figure_refs": [
         "call_1eee45eb:coef.cdl"
       ]
     }

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

   Números encontrados

   0,08006

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.cdl = 0,08006.

   Figuras citadas: call_1eee45eb:coef.cdl · Parada: answered

   Figura da resposta: call_1eee45eb:coef.cdl = 0,08006350.08006351033824458 (se 0,03589850.0358984941171576)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta escolhidapassouobrigatóriaanswer figure call_1eee45eb:coef.cdl (infer.ols)
9. 8Pontuação
   #2uma entrepassouobrigatóriatool_called(infer.ols): ok | tool_called(infer.twfe): answer call is infer.ols
10. 9Pontuação
    #3argumento igualpassouobrigatóriainfer.ols.y = 'l_homicide'
11. 10Pontuação
    #4argumento igualpassouobrigatóriainfer.ols.x[0] = 'cdl'
12. 11Pontuação
    #5argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.twfe
13. 12Pontuação
    #6argumento igualpassouobrigatórianot applicable: the answer call is infer.ols, not infer.twfe
14. 13Pontuação
    #7argumento igualpassouregistradainfer.ols.y = 'l_homicide'
15. 14Pontuação
    #8argumento igualpassouregistradainfer.ols.x = ['cdl']
16. 15Pontuação
    #9argumento igualpassouregistradainfer.ols.weights = 'popwt'
17. 16Pontuação
    #10diagnósticopassouregistrada550 vs 550
18. 17Pontuação
    #11figura da resposta próximapassouregistrada0.08006351034 vs 0.08006351033824417
19. 18Pontuação
    #12figura da resposta próximapassouregistrada0.03589849412 vs 0.03589849411715782
20. 19Pontuação
    #13figura da resposta próximapassouregistrada0.08006351034 vs 0.0801
21. 20Pontuação
    #14figura da resposta próximafalhouregistrada0.03589849412 vs 0.0342

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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