Tarefa causcibench_real.q050

Veredito

Pergunta feita ao analista

What is the effect of CompStat adoption on the number of Part 2 arrests?

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
   eckhouse_metrics_management— This dataset is an agency-year panel that links police-reported crime, clearances, and unfounded counts to population, mortality, and local political context, tracking the timing and status of metrics-focused management practices within U.S. law enforcement agencies. The unit of observation is the police agency by calendar year, identified by a standard agency code and geographic identifiers. The variables are: AGENCY: Agency name used in reporting YEAR: Calendar year of the observation for the agency ORI: Agency's Originating Agency Identifier used in federal crime reporting systems STATE: State of the agency's jurisdiction COUNTY: County of the agency's jurisdiction POP: Jurisdiction population for the agency-year (denominator for rate variables) COMPSTAT: Indicator for adoption of a metrics-focused management program by the agency COMPSTATDATE: Date (or year) of the agency's program adoption as recorded in the source HASCOMPSTAT: Indicator equal to 1 in years at or after adoption, and 0 otherwise YEAR.UNFOUNDED.MURDER: Count of homicides deemed unfounded in the agency-year YEAR.UNFOUNDED.RAPE.TOT: Count of rapes deemed unfounded in the agency-year YEAR.UNFOUND.AUTO.THFT: Count of motor vehicle thefts deemed unfounded in the agency-year YEAR.ACT.NUM.MURDER: Count of homicide offenses known to the agency in the year (UCR ""actual"" offenses) YEAR.ACT.NUM.RAPE.TOT: Count of rape offenses known to the agency in the year YEAR.ACT.NUM.ASSAULT.TOT: Count of aggravated assault offenses known to the agency in the year YEAR.ACT.VHC.THFT.TOT: Count of motor vehicle theft offenses known to the agency in the year YEAR.ACT.ALL.FIELDS: Total ""actual"" offenses across included categories in the agency-year YEAR.TOT.CLR.MURDER: Number of homicide offenses cleared (by arrest or exceptional means) in the agency-year YEAR.TOT.CLR.RAPE.TOT: Number of rape offenses cleared in the agency-year YEAR.TOT.CLR.ASSAULT.TOT: Number of aggravated assaults cleared in the agency-year; YEAR.TOT.CLR.VHC.THFT.TOT: Number of motor vehicle thefts cleared in the agency-year YEAR.TOT.CLR.ALL.FIELDS: Total clearances across included categories in the agency-year SHAREPT2: Share of Part II arrests in total arrests (proportion) clearrate: Overall clearance rate across included offense categories (clearances divided by offenses) unfoundrapepct: Percent of reported rapes deemed unfounded murderclearrate: Clearance rate for homicide rapeclearrate: Clearance rate for rape assaultclearrate: Clearance rate for aggravated assault unfoundmurderpct: Percent of reported homicides deemed unfounded unfoundassaultpct: Percent of reported aggravated assaults deemed unfounded PART1arrests: Count of arrests for UCR Part I offenses in the agency-year PART2arrests: Count of arrests for UCR Part II offenses in the agency-year popblackpct: Percent of the jurisdiction's population identified as Black/African American popwhitepct: Percent of the jurisdiction's population identified as White unfoundautopct: Percent of reported motor vehicle thefts deemed unfounded yearssincecompstat: Years elapsed since the agency's adoption year (constructed relative to COMPSTATDATE) FIPS_PLACE_ID: FIPS place identifier for the agency's primary jurisdiction cdcHomicide: Cause-specific mortality measure for homicide matched to the agency's geography (level/units follow source coding) cdcSuicide: Cause-specific mortality measure for suicide matched to the agency's geography (level/units follow source coding) cdcUndetermined: Cause-specific mortality measure for undetermined intent matched to the agency's geography (level/units follow source coding) cdcUnintentional: Cause-specific mortality measure for unintentional injury matched to the agency's geography (level/units follow source coding) haselection: Indicator equal to 1 if a relevant local election occurred for the agency's jurisdiction in that year years_since_election: Years since the most recent relevant local election for the agency's jurisdiction partisantotal: Summary measure of local political partisanship for the jurisdiction (construction follows source coding) incidentsperpop: Total incidents per population (offenses divided by POP), a per-capita incident rate autoclearrate: Clearance rate for motor vehicle theft YEAR.ACT.NUM.KILLINGS: Count of killings as coded in the source (maintained separately from homicide for internal consistency and checks)
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_3d1b4154 nos detalhes

   causcibench_metadata_rule/1: did

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

   controls:

   ["POP"]

   dataset:

   "eckhouse_metrics_management"

   fe:

   ["AGENCY","YEAR"]

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["HASCOMPSTAT"]

   y:

   "PART2arrests"

   Resultado calculado

   • coef.HASCOMPSTAT = 20.471,1 20471.05741625657
     Coefficient on HASCOMPSTAT in the regression of PART2arrests
   Diagnósticos: n_obs = 1.0811081 (observations used) · k = 7272 (parameters estimated, fixed-effect dummies included) · r2 = 0,6300990.6300993423273054 · df_resid = 1.0091009 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1)) · fixed_effects = "AGENCY (47 levels), YEAR (24 levels)" (absorbed as dummies; their coefficients are not tabulated)
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: 20471
   Figuras citadas: call_3d1b4154:coef.HASCOMPSTAT
   • ✓
     #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

   What is the effect of CompStat adoption on the number of Part 2 arrests?

   eckhouse_metrics_management (agent)

   This dataset is an agency-year panel that links police-reported crime, clearances, and unfounded counts to population, mortality, and local political context, tracking the timing and status of metrics-focused management practices within U.S. law enforcement agencies. The unit of observation is the police agency by calendar year, identified by a standard agency code and geographic identifiers. The variables are: AGENCY: Agency name used in reporting YEAR: Calendar year of the observation for the agency ORI: Agency's Originating Agency Identifier used in federal crime reporting systems STATE: State of the agency's jurisdiction COUNTY: County of the agency's jurisdiction POP: Jurisdiction population for the agency-year (denominator for rate variables) COMPSTAT: Indicator for adoption of a metrics-focused management program by the agency COMPSTATDATE: Date (or year) of the agency's program adoption as recorded in the source HASCOMPSTAT: Indicator equal to 1 in years at or after adoption, and 0 otherwise YEAR.UNFOUNDED.MURDER: Count of homicides deemed unfounded in the agency-year YEAR.UNFOUNDED.RAPE.TOT: Count of rapes deemed unfounded in the agency-year YEAR.UNFOUND.AUTO.THFT: Count of motor vehicle thefts deemed unfounded in the agency-year YEAR.ACT.NUM.MURDER: Count of homicide offenses known to the agency in the year (UCR ""actual"" offenses) YEAR.ACT.NUM.RAPE.TOT: Count of rape offenses known to the agency in the year YEAR.ACT.NUM.ASSAULT.TOT: Count of aggravated assault offenses known to the agency in the year YEAR.ACT.VHC.THFT.TOT: Count of motor vehicle theft offenses known to the agency in the year YEAR.ACT.ALL.FIELDS: Total ""actual"" offenses across included categories in the agency-year YEAR.TOT.CLR.MURDER: Number of homicide offenses cleared (by arrest or exceptional means) in the agency-year YEAR.TOT.CLR.RAPE.TOT: Number of rape offenses cleared in the agency-year YEAR.TOT.CLR.ASSAULT.TOT: Number of aggravated assaults cleared in the agency-year; YEAR.TOT.CLR.VHC.THFT.TOT: Number of motor vehicle thefts cleared in the agency-year YEAR.TOT.CLR.ALL.FIELDS: Total clearances across included categories in the agency-year SHAREPT2: Share of Part II arrests in total arrests (proportion) clearrate: Overall clearance rate across included offense categories (clearances divided by offenses) unfoundrapepct: Percent of reported rapes deemed unfounded murderclearrate: Clearance rate for homicide rapeclearrate: Clearance rate for rape assaultclearrate: Clearance rate for aggravated assault unfoundmurderpct: Percent of reported homicides deemed unfounded unfoundassaultpct: Percent of reported aggravated assaults deemed unfounded PART1arrests: Count of arrests for UCR Part I offenses in the agency-year PART2arrests: Count of arrests for UCR Part II offenses in the agency-year popblackpct: Percent of the jurisdiction's population identified as Black/African American popwhitepct: Percent of the jurisdiction's population identified as White unfoundautopct: Percent of reported motor vehicle thefts deemed unfounded yearssincecompstat: Years elapsed since the agency's adoption year (constructed relative to COMPSTATDATE) FIPS_PLACE_ID: FIPS place identifier for the agency's primary jurisdiction cdcHomicide: Cause-specific mortality measure for homicide matched to the agency's geography (level/units follow source coding) cdcSuicide: Cause-specific mortality measure for suicide matched to the agency's geography (level/units follow source coding) cdcUndetermined: Cause-specific mortality measure for undetermined intent matched to the agency's geography (level/units follow source coding) cdcUnintentional: Cause-specific mortality measure for unintentional injury matched to the agency's geography (level/units follow source coding) haselection: Indicator equal to 1 if a relevant local election occurred for the agency's jurisdiction in that year years_since_election: Years since the most recent relevant local election for the agency's jurisdiction partisantotal: Summary measure of local political partisanship for the jurisdiction (construction follows source coding) incidentsperpop: Total incidents per population (offenses divided by POP), a per-capita incident rate autoclearrate: Clearance rate for motor vehicle theft YEAR.ACT.NUM.KILLINGS: Count of killings as coded in the source (maintained separately from homicide for internal consistency and checks)

   file b303608e3fc216acfb6520b968a7a6de354eb1248fdd53ac70671f7eb04f1298 · frame —
2. 1Turno do modelo
   call_ae2a7f77referenceFim: 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": [
         "POP"
       ],
       "dataset": "eckhouse_metrics_management",
       "fe": [
         "AGENCY",
         "YEAR"
       ],
       "x": [
         "HASCOMPSTAT"
       ],
       "y": "PART2arrests"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_3d1b4154decidido pelo modeloreexecutável

   Motivo: causcibench_metadata_rule/1: did

   Argumentos

   {
     "cluster": null,
     "controls": [
       "POP"
     ],
     "dataset": "eckhouse_metrics_management",
     "fe": [
       "AGENCY",
       "YEAR"
     ],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "HASCOMPSTAT"
     ],
     "y": "PART2arrests"
   }

   Figuras call_3d1b4154

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.HASCOMPSTAT Coefficient on HASCOMPSTAT in the regression of PART2arrests	20.471,1	20471.05741625657	6.658,556658.553954809597	7.404,86 – 33.537,3

   Diagnósticos

   • n_obs = 1.0811081 (observations used)
   • k = 7272 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,6300990.6300993423273054
   • df_resid = 1.0091009
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)
   • fixed_effects = "AGENCY (47 levels), YEAR (24 levels)" (absorbed as dummies; their coefficients are not tabulated)

   Derivação matemática e pressupostos

   coef.HASCOMPSTAT

   1. Coeficiente de HASCOMPSTAT na regressão por mínimos quadrados ordinários de PART2arrests, efeitos fixos de AGENCY, YEAR.→ 20.471,1 (20471.05741625657)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 6.658,55 (6658.553954809597)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 1009 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the conditional mean of the outcome is linear in the regressors
   • errors are independent across observations; their variance may differ

   Tabelas (1)

   {
     "coefficients": [
       {
         "ci_high": 184969.32723625033,
         "ci_low": 80960.74323936789,
         "coef": 132965.03523780912,
         "p": 6.191006261001499e-7,
         "se": 26501.4617543785,
         "t": 5.017271744108266,
         "term": "Intercept"
       },
       {
         "ci_high": 33537.25683433522,
         "ci_low": 7404.857998177922,
         "coef": 20471.05741625657,
         "p": 0.0021659751936975303,
         "se": 6658.553954809597,
         "t": 3.0743998704808795,
         "term": "HASCOMPSTAT"
       },
       {
         "ci_high": -0.17759882445915823,
         "ci_low": -0.43003684328917813,
         "coef": -0.30381783387416816,
         "p": 0.0000026469661928991627,
         "se": 0.06432138814211119,
         "t": -4.72343403414919,
         "term": "POP"
       }
     ]
   }

   • 4 rows were dropped because one of 'PART2arrests', 'HASCOMPSTAT', 'POP', 'AGENCY', 'YEAR' was missing

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   eckhouse_metrics_management edcc0e3e260047813f052e191a75de4b0bf34c47415a9adb49cf78c6244589b6

   sha256 do resultado

   9bbf2335ce0b5f05192c2703fd1e007a84cfe56abd346f3525b8c68016dcd315

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_186d7e4freferenceFim: 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.HASCOMPSTAT = 20471.",
       "caveats": [],
       "figure_refs": [
         "call_3d1b4154:coef.HASCOMPSTAT"
       ]
     }

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

   Números encontrados

   20471

   Sem lastro

   —

   Não citados

   —

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

   Estimativa (infer.ols): coef.HASCOMPSTAT = 20471.

   Figuras citadas: call_3d1b4154:coef.HASCOMPSTAT · Parada: answered

   Figura da resposta: call_3d1b4154:coef.HASCOMPSTAT = 20.471,120471.05741625657 (se 6.658,556658.553954809597)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória20471.05742 vs 20471.057

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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