Tarefa crb_papers.q015

Veredito

Pergunta feita ao analista

Did cities that adopted CompStat in 1996 experience a change in the share of total arrests that were minor (Part II) offenses from 1995 to 1997 relative to cities that had not yet adopted (1998+)?

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 1996 experience a change in the share of total arrests that were minor (Part II) offenses from 1995 to 1997 relative to cities that had not yet adopted (1998+)?

   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 [1995, 1997]"}
   4. merge {"how":"inner","on":["AGENCY"],"with":"ref_adopt"}
   5. merge {"how":"left","on":["AGENCY"],"with":"ref_years"}
   6. filter {"query":"(adopt == 1996 or adopt >= 1998) and nyears == 2"}
   7. derive {"args":{"cmp":"eq","column":"YEAR","value":1997},"name":"Post","op":"indicator"}
   8. derive {"args":{"cmp":"eq","column":"adopt","value":1996},"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 [1995, 1997]"}
   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 [1995, 1997]"}

   1023 → 87 linhas

   Frame antes → depois: 408ba99bfd12 → 351771473b03
9. 8Transformação
   ref_years · collapse {"aggs":[{"column":"SHAREPT2","fn":"count","name":"n"}],"by":["AGENCY","YEAR"]}

   87 → 87 linhas

   Frame antes → depois: 351771473b03 → fb516d61644e
10. 9Transformação
    ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["AGENCY"]}

    87 → 46 linhas

    Frame antes → depois: fb516d61644e → 03f7f018769b
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 [1995, 1997]"}

    1023 → 87 linhas

    Frame antes → depois: 408ba99bfd12 → 351771473b03
14. 13Transformação
    ref_panel · merge {"how":"inner","on":["AGENCY"],"with":"ref_adopt"}

    87 → 87 linhas

    Frame antes → depois: 351771473b03 → 0125673fe4af
15. 14Transformação
    ref_panel · merge {"how":"left","on":["AGENCY"],"with":"ref_years"}

    87 → 87 linhas

    Frame antes → depois: 0125673fe4af → e4777fc8b34e
16. 15Transformação
    ref_panel · filter {"query":"(adopt == 1996 or adopt >= 1998) and nyears == 2"}

    87 → 74 linhas

    Frame antes → depois: e4777fc8b34e → e249f3de0f24
17. 16Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"YEAR","value":1997},"name":"Post","op":"indicator"}

    74 → 74 linhas

    Frame antes → depois: e249f3de0f24 → 0716c33b50a8
18. 17Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"adopt","value":1996},"name":"Treat","op":"indicator"}

    74 → 74 linhas

    Frame antes → depois: 0716c33b50a8 → af6c2c73fd3b
19. 18Transformação
    ref_panel · derive {"args":{"columns":["Treat","Post"]},"name":"Treat_Post","op":"interaction"}

    74 → 74 linhas

    Frame antes → depois: af6c2c73fd3b → 4ffb482f82d8
20. 19Turno do modelo
    call_b0c4bf91referenceFim: 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.olsv0.1.0call_867f7727decidido 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_867f7727

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.Treat_Post Coefficient on Treat_Post in the regression of SHAREPT2	-0,0229723	-0.022972284086693825	0,004762860.004762857794788828	-0,0326318 – -0,0133128

    Diagnósticos

    • n_obs = 7474
    • k = 44
    • r2 = 0,09657310.09657305294043383
    • df_resid = 7070
    • se_type = "cluster"
    • n_clusters = 3737

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.7399692064726602,
          "ci_low": 0.7014132749602164,
          "coef": 0.7206912407164383,
          "p": 2.5987147228852327e-41,
          "se": 0.009505459681307801,
          "t": 75.8186626296102,
          "term": "Intercept"
        },
        {
          "ci_high": -0.013312760765559626,
          "ci_low": -0.03263180740782802,
          "coef": -0.022972284086693825,
          "p": 0.000025755976135405874,
          "se": 0.004762857794788828,
          "t": -4.8232143550093864,
          "term": "Treat_Post"
        },
        {
          "ci_high": 0.11832729792272856,
          "ci_low": 0.07977136641028476,
          "coef": 0.09904933216650666,
          "p": 2.0447462462141838e-12,
          "se": 0.009505459681307803,
          "t": 10.420256935209999,
          "term": "Treat"
        },
        {
          "ci_high": 0.03208757100741101,
          "ci_low": 0.012768524365142617,
          "coef": 0.022428047686276816,
          "p": 0.000036436832901095995,
          "se": 0.004762857794788828,
          "t": 4.7089475799206,
          "term": "Post"
        }
      ]
    }

    Digests dos dados

    ref_panel 4ffb482f82d8b34dbfe786d137b97029bcaa75453286a398947e18f39cf9c7ee

    sha256 do resultado

    eb9ee90bb0f579123c43ee8edf1b11bb6ba9d3e1a3d074dd0b71166495047f4f

    Resultado mostrado ao modelo
22. 21Turno do modelo
    call_c663fc61referenceFim: 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 = -0,02297. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_867f7727:coef.Treat_Post"
        ]
      }

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

    Números encontrados

    -0,02297

    Sem lastro

    —

    Não citados

    —

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

    Estimativa (infer.ols): coef.Treat_Post = -0,02297. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_867f7727:coef.Treat_Post · Parada: answered

    Figura da resposta: call_867f7727:coef.Treat_Post = -0,0229723-0.022972284086693825 (se 0,004762860.004762857794788828)
25. 24Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
26. 25Pontuação
    #1figura da resposta próximapassouobrigatória-0.02297228409 in [-0.03230748536, -0.01363708281]?
27. 26Pontuação
    #2figura da resposta próximapassouregistrada-0.02297228409 vs -0.02297228408669323
28. 27Pontuação
    #3figura da resposta próximapassouregistrada0.004762857795 vs 0.004762857794788859

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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