Tarefa crb_papers.q014

Veredito

Pergunta feita ao analista

Did cities that adopted CompStat in 1994 experience a change in the share of total arrests that were minor (Part II) offenses from 1993 to 1995 relative to cities that had not yet adopted (1996+)?

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
   ds_0008— | Variable Name | Description | | --- | --- | | SHAREPT2 | Proportion (0–1) of a department’s total arrests in a given calendar year that fall under Uniform Crime Reporting (UCR) Part II offense categories. Part II includes drug abuse violations, simple assault, fraud/forgery, stolen property, vandalism, weapons violations, prostitution, disorderly conduct, and other non‑index offenses. Computed as Part II arrests divided by all arrests reported for that year. | | HASCOMPSTAT | Binary indicator (0/1) that the police department operated a CompStat‑style performance/metrics management system in that year. “CompStat‑style” covers systems that regularly compile incident/arrest statistics, visualize patterns, and use management meetings for accountability once implemented and in subsequent years. | | AGENCY | Text identifier for the municipal police department/jurisdiction (e.g., “PHOENIX”). Serves as the unit index across years and corresponds to the primary city police agency reporting to the UCR program. | | YEAR | Calendar year of observation, aligning with UCR annual reporting (1990–2013). | | PART2arrests | Annual count of arrests recorded for UCR Part II offenses by the department. Includes adult and juvenile arrests as submitted to the UCR program. Integer count. | | POP | Resident population of the city for that year, based on UCR population figures (census-based estimates used by UCR for rate calculations). | Dataset Context: This file is a municipal police department–by–year panel for large U.S. cities covering 1990–2013. It merges administrative statistics from the FBI’s Uniform Crime Reporting (UCR) program with hand-collected information on whether and when departments used CompStat‑style metrics management. UCR provides standardized definitions, monthly submissions from local agencies, and annual tabulations of arrests and population. Part II arrests span a wide set of non‑index offenses—such as drug violations, simple assault, property and public‑order offenses—that are often more discretionary than index (Part I) crimes. The population series comes from UCR’s census‑based annual estimates and is commonly used to construct per‑capita measures. CompStat‑style systems began spreading among U.S. police departments in the mid‑1990s alongside advances in data systems and the broader rise of performance management in public administration. These systems typically feature frequent data reviews, mapping and trend visualization, and management meetings focused on recent activity. Adoption timing in this dataset was compiled from public sources such as department websites, press releases, and news archives, and is coded as “on” starting in the first documented year of operation. Each row represents a department-year and includes: the department identifier, the year, the count of arrests for UCR Part II offenses, the share of all arrests that are Part II, whether a CompStat‑style system was in use, and the city’s population. Because arrests are administrative measures of police activity and recording practices, they are not the same as offenses known to police or victimization; nonetheless, UCR’s standardized categories enable comparisons across cities and over time. The panel captures a period of substantial organizational change in U.S. municipal policing, during which many large departments modernized data systems and management processes while continuously reporting arrest totals to the UCR program.
   ref_adopt
   ref_panel
   ref_years
   Transformação "select" aplicada à base "ref_adopt" (1085 → 1085 linhas). {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
   Transformação "dropna" aplicada à base "ref_adopt" (1085 → 1023 linhas). {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
   Transformação "filter" aplicada à base "ref_adopt" (1023 → 430 linhas). {"query":"HASCOMPSTAT == 1"}
   Transformação "collapse" aplicada à base "ref_adopt" (430 → 47 linhas). {"aggs":[{"column":"YEAR","fn":"min","name":"adopt"}],"by":["AGENCY"]}
   Transformação "select" aplicada à base "ref_years" (1085 → 1085 linhas). {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
   Transformação "dropna" aplicada à base "ref_years" (1085 → 1023 linhas). {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
   Transformação "filter" aplicada à base "ref_years" (1023 → 89 linhas). {"query":"YEAR in [1993, 1995]"}
   Transformação "collapse" aplicada à base "ref_years" (89 → 89 linhas). {"aggs":[{"column":"SHAREPT2","fn":"count","name":"n"}],"by":["AGENCY","YEAR"]}
   Transformação "collapse" aplicada à base "ref_years" (89 → 47 linhas). {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["AGENCY"]}
   Transformação "select" aplicada à base "ref_panel" (1085 → 1085 linhas). {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
   Transformação "dropna" aplicada à base "ref_panel" (1085 → 1023 linhas). {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
   Transformação "filter" aplicada à base "ref_panel" (1023 → 89 linhas). {"query":"YEAR in [1993, 1995]"}
   Transformação "merge" aplicada à base "ref_panel" (89 → 89 linhas). {"how":"inner","on":["AGENCY"],"with":"ref_adopt"}
   Transformação "merge" aplicada à base "ref_panel" (89 → 89 linhas). {"how":"left","on":["AGENCY"],"with":"ref_years"}
   Transformação "filter" aplicada à base "ref_panel" (89 → 84 linhas). {"query":"(adopt == 1994 or adopt >= 1996) and nyears == 2"}
   Transformação "derive" aplicada à base "ref_panel" (84 → 84 linhas). {"args":{"cmp":"eq","column":"YEAR","value":1995},"name":"Post","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (84 → 84 linhas). {"args":{"cmp":"eq","column":"adopt","value":1994},"name":"Treat","op":"indicator"}
   Transformação "derive" aplicada à base "ref_panel" (84 → 84 linhas). {"args":{"columns":["Treat","Post"]},"name":"Treat_Post","op":"interaction"}
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na respostacoincide com a referência

   Ver chamada call_1e788882 nos detalhes

   smf.ols('SHAREPT2 ~ Treat*Post'), cluster by AGENCY

   Por que este é o caminho de referência: smf.ols('SHAREPT2 ~ Treat*Post'), cluster by AGENCY
   Argumentos principais

   cluster:

   "AGENCY"

   controls:

   ["Treat","Post"]

   dataset:

   "ref_panel"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["Treat_Post"]

   y:

   "SHAREPT2"

   Resultado calculado

   • coef.Treat_Post = 0,0135258 0.013525759691176349
     Coefficient on Treat_Post in the regression of SHAREPT2
   Diagnósticos: n_obs = 8484 (observations used) · k = 44 (parameters estimated, fixed-effect dummies included) · r2 = 0,07624180.07624177865716641 · df_resid = 8080 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'AGENCY') · n_clusters = 4242 (42 clusters of 'AGENCY')
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: 0,01353
   Figuras citadas: call_1e788882:coef.Treat_Post
   • ✓
     #0resposta com lastro

     every number traced to a tool call or the question
   • ✓
     #1figura da resposta próxima

     CRB estimate_within_solution_ci: the answer figure inside the gold 95% interval effect ± 1.96·|SE| of source crb_papers.q014.gold (closed, evaluate_result.py; no --auto-rescale)
   • ✓
     #2figura da resposta próxima

     replication cell: the gold effect to rel 1e-6 (abs floor 1e-6·SE); never required
   • ✓
     #3figura da resposta próxima

     replication cell: the gold SE to rel 1e-6; never required

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 cities that adopted CompStat in 1994 experience a change in the share of total arrests that were minor (Part II) offenses from 1993 to 1995 relative to cities that had not yet adopted (1996+)?

   ds_0008 (agent)

   | Variable Name | Description | | --- | --- | | SHAREPT2 | Proportion (0–1) of a department’s total arrests in a given calendar year that fall under Uniform Crime Reporting (UCR) Part II offense categories. Part II includes drug abuse violations, simple assault, fraud/forgery, stolen property, vandalism, weapons violations, prostitution, disorderly conduct, and other non‑index offenses. Computed as Part II arrests divided by all arrests reported for that year. | | HASCOMPSTAT | Binary indicator (0/1) that the police department operated a CompStat‑style performance/metrics management system in that year. “CompStat‑style” covers systems that regularly compile incident/arrest statistics, visualize patterns, and use management meetings for accountability once implemented and in subsequent years. | | AGENCY | Text identifier for the municipal police department/jurisdiction (e.g., “PHOENIX”). Serves as the unit index across years and corresponds to the primary city police agency reporting to the UCR program. | | YEAR | Calendar year of observation, aligning with UCR annual reporting (1990–2013). | | PART2arrests | Annual count of arrests recorded for UCR Part II offenses by the department. Includes adult and juvenile arrests as submitted to the UCR program. Integer count. | | POP | Resident population of the city for that year, based on UCR population figures (census-based estimates used by UCR for rate calculations). | Dataset Context: This file is a municipal police department–by–year panel for large U.S. cities covering 1990–2013. It merges administrative statistics from the FBI’s Uniform Crime Reporting (UCR) program with hand-collected information on whether and when departments used CompStat‑style metrics management. UCR provides standardized definitions, monthly submissions from local agencies, and annual tabulations of arrests and population. Part II arrests span a wide set of non‑index offenses—such as drug violations, simple assault, property and public‑order offenses—that are often more discretionary than index (Part I) crimes. The population series comes from UCR’s census‑based annual estimates and is commonly used to construct per‑capita measures. CompStat‑style systems began spreading among U.S. police departments in the mid‑1990s alongside advances in data systems and the broader rise of performance management in public administration. These systems typically feature frequent data reviews, mapping and trend visualization, and management meetings focused on recent activity. Adoption timing in this dataset was compiled from public sources such as department websites, press releases, and news archives, and is coded as “on” starting in the first documented year of operation. Each row represents a department-year and includes: the department identifier, the year, the count of arrests for UCR Part II offenses, the share of all arrests that are Part II, whether a CompStat‑style system was in use, and the city’s population. Because arrests are administrative measures of police activity and recording practices, they are not the same as offenses known to police or victimization; nonetheless, UCR’s standardized categories enable comparisons across cities and over time. The panel captures a period of substantial organizational change in U.S. municipal policing, during which many large departments modernized data systems and management processes while continuously reporting arrest totals to the UCR program.

   file 44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212 · frame —

   ref_adopt (reference)

   file 44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212 · frame —

   1. select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
   2. dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
   3. filter {"query":"HASCOMPSTAT == 1"}
   4. collapse {"aggs":[{"column":"YEAR","fn":"min","name":"adopt"}],"by":["AGENCY"]}

   ref_panel (reference)

   file 44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212 · frame —

   1. select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
   2. dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
   3. filter {"query":"YEAR in [1993, 1995]"}
   4. merge {"how":"inner","on":["AGENCY"],"with":"ref_adopt"}
   5. merge {"how":"left","on":["AGENCY"],"with":"ref_years"}
   6. filter {"query":"(adopt == 1994 or adopt >= 1996) and nyears == 2"}
   7. derive {"args":{"cmp":"eq","column":"YEAR","value":1995},"name":"Post","op":"indicator"}
   8. derive {"args":{"cmp":"eq","column":"adopt","value":1994},"name":"Treat","op":"indicator"}
   9. derive {"args":{"columns":["Treat","Post"]},"name":"Treat_Post","op":"interaction"}

   ref_years (reference)

   file 44d92e04bfa13eb2ab33b50c943cce8cda89582240c42d68917d74656dd90212 · frame —

   1. select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}
   2. dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}
   3. filter {"query":"YEAR in [1993, 1995]"}
   4. collapse {"aggs":[{"column":"SHAREPT2","fn":"count","name":"n"}],"by":["AGENCY","YEAR"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["AGENCY"]}
2. 1Transformação
   ref_adopt · select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}

   1085 → 1085 linhas

   Frame antes → depois: 773f00f7c7cd → 82552e3d50ad
3. 2Transformação
   ref_adopt · dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}

   1085 → 1023 linhas

   Frame antes → depois: 82552e3d50ad → 408ba99bfd12
4. 3Transformação
   ref_adopt · filter {"query":"HASCOMPSTAT == 1"}

   1023 → 430 linhas

   Frame antes → depois: 408ba99bfd12 → 54273eff1d96
5. 4Transformação
   ref_adopt · collapse {"aggs":[{"column":"YEAR","fn":"min","name":"adopt"}],"by":["AGENCY"]}

   430 → 47 linhas

   Frame antes → depois: 54273eff1d96 → a1354fc38920
6. 5Transformação
   ref_years · select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}

   1085 → 1085 linhas

   Frame antes → depois: 773f00f7c7cd → 82552e3d50ad
7. 6Transformação
   ref_years · dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}

   1085 → 1023 linhas

   Frame antes → depois: 82552e3d50ad → 408ba99bfd12
8. 7Transformação
   ref_years · filter {"query":"YEAR in [1993, 1995]"}

   1023 → 89 linhas

   Frame antes → depois: 408ba99bfd12 → 0f9656a26df3
9. 8Transformação
   ref_years · collapse {"aggs":[{"column":"SHAREPT2","fn":"count","name":"n"}],"by":["AGENCY","YEAR"]}

   89 → 89 linhas

   Frame antes → depois: 0f9656a26df3 → 0254beb7ebeb
10. 9Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["AGENCY"]}

    89 → 47 linhas

    Frame antes → depois: 0254beb7ebeb → da165966722e
11. 10Transformação
    ref_panel · select {"columns":["SHAREPT2","HASCOMPSTAT","AGENCY","YEAR"]}

    1085 → 1085 linhas

    Frame antes → depois: 773f00f7c7cd → 82552e3d50ad
12. 11Transformação
    ref_panel · dropna {"columns":["YEAR","HASCOMPSTAT","SHAREPT2","AGENCY"]}

    1085 → 1023 linhas

    Frame antes → depois: 82552e3d50ad → 408ba99bfd12
13. 12Transformação
    ref_panel · filter {"query":"YEAR in [1993, 1995]"}

    1023 → 89 linhas

    Frame antes → depois: 408ba99bfd12 → 0f9656a26df3
14. 13Transformação
    ref_panel · merge {"how":"inner","on":["AGENCY"],"with":"ref_adopt"}

    89 → 89 linhas

    Frame antes → depois: 0f9656a26df3 → 192af9a15790
15. 14Transformação
    ref_panel · merge {"how":"left","on":["AGENCY"],"with":"ref_years"}

    89 → 89 linhas

    Frame antes → depois: 192af9a15790 → 71f2f4fba812
16. 15Transformação
    ref_panel · filter {"query":"(adopt == 1994 or adopt >= 1996) and nyears == 2"}

    89 → 84 linhas

    Frame antes → depois: 71f2f4fba812 → aefc01906807
17. 16Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"YEAR","value":1995},"name":"Post","op":"indicator"}

    84 → 84 linhas

    Frame antes → depois: aefc01906807 → 7cbd5af8d391
18. 17Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"adopt","value":1994},"name":"Treat","op":"indicator"}

    84 → 84 linhas

    Frame antes → depois: 7cbd5af8d391 → 26a3fb5b9e96
19. 18Transformação
    ref_panel · derive {"args":{"columns":["Treat","Post"]},"name":"Treat_Post","op":"interaction"}

    84 → 84 linhas

    Frame antes → depois: 26a3fb5b9e96 → 228961c4f80d
20. 19Turno do modelo
    call_2acf2575referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('SHAREPT2 ~ Treat*Post'), cluster by AGENCY

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "AGENCY",
        "controls": [
          "Treat",
          "Post"
        ],
        "dataset": "ref_panel",
        "x": [
          "Treat_Post"
        ],
        "y": "SHAREPT2"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
21. 20Ferramenta
    infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_1e788882decidido pelo modeloreexecutável

    Motivo: smf.ols('SHAREPT2 ~ Treat*Post'), cluster by AGENCY

    Argumentos

    {
      "cluster": "AGENCY",
      "controls": [
        "Treat",
        "Post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "Treat_Post"
      ],
      "y": "SHAREPT2"
    }

    Figuras call_1e788882

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.Treat_Post Coefficient on Treat_Post in the regression of SHAREPT2	0,0135258	0.013525759691176349	0,005525090.00552509183598485	0,00236761 – 0,0246839

    Diagnósticos

    • n_obs = 8484 (observations used)
    • k = 44 (parameters estimated, fixed-effect dummies included)
    • r2 = 0,07624180.07624177865716641
    • df_resid = 8080
    • se_type = "cluster" (cluster-robust standard errors (CR1) by 'AGENCY')
    • n_clusters = 4242 (42 clusters of 'AGENCY')

    Derivação matemática e pressupostos

    coef.Treat_Post

    1. Coeficiente de Treat_Post na regressão por mínimos quadrados ordinários de SHAREPT2.→ 0,0135258 (0.013525759691176349)
    2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,00552509 (0.00552509183598485)
    3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 41 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 clusters of 'AGENCY' and may be correlated within them

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.7286486710918715,
          "ci_low": 0.685745128850959,
          "coef": 0.7071968999714152,
          "p": 2.0815156482734154e-43,
          "se": 0.010622102465080442,
          "t": 66.57786462673324,
          "term": "Intercept"
        },
        {
          "ci_high": 0.024683909019398917,
          "ci_low": 0.0023676103629537825,
          "coef": 0.013525759691176349,
          "p": 0.018730821800230446,
          "se": 0.00552509183598485,
          "t": 2.448060610157329,
          "term": "Treat_Post"
        },
        {
          "ci_high": 0.11540987278080006,
          "ci_low": 0.07250633053988748,
          "coef": 0.09395810166034377,
          "p": 4.7251320400484836e-11,
          "se": 0.010622102465080442,
          "t": 8.845527706894721,
          "term": "Treat"
        },
        {
          "ci_high": 0.02739165453552124,
          "ci_low": 0.005075355879076107,
          "coef": 0.016233505207298674,
          "p": 0.005398853714139169,
          "se": 0.00552509183598485,
          "t": 2.938142150248086,
          "term": "Post"
        }
      ]
    }

    Hashes SHA-256 e metadados de reprodução

    Digests dos dados

    ref_panel 228961c4f80d4c1c301429fd7800c108f3586b9061ce52e23554f2e2c1b65c9b

    sha256 do resultado

    cd03da5b0715bcc81d2f1e1c1a59a4a4a357b7a4d2ebec364bd87d7ffa1c6c30

    Resultado mostrado ao modelo
22. 21Turno do modelo
    call_c4ea1d22referenceFim: 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.Treat_Post = 0,01353. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_1e788882:coef.Treat_Post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
23. 22Guarda
    resposta aceita

    Números encontrados

    0,01353

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
24. 23Resposta

    Estimativa (infer.ols): coef.Treat_Post = 0,01353. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_1e788882:coef.Treat_Post · Parada: answered

    Figura da resposta: call_1e788882:coef.Treat_Post = 0,01352580.013525759691176349 (se 0,005525090.00552509183598485)
25. 24Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
26. 25Pontuação
    #1figura da resposta próximapassouobrigatória0.01352575969 in [0.002696579693, 0.02435493969]?
27. 26Pontuação
    #2figura da resposta próximapassouregistrada0.01352575969 vs 0.013525759691176156
28. 27Pontuação
    #3figura da resposta próximapassouregistrada0.005525091836 vs 0.005525091835984817

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)

Downloads desta tarefa