Tarefa crb_papers.q054

Veredito

Pergunta feita ao analista

Across the full sample of 189 close-election municipalities, how did the change in drug-related incident rates (per 1,000 people) from before to after July 1, 2016 differ between municipalities governed by outsider mayors and those governed by establishment-aligned mayors?

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

   Across the full sample of 189 close-election municipalities, how did the change in drug-related incident rates (per 1,000 people) from before to after July 1, 2016 differ between municipalities governed by outsider mayors and those governed by establishment-aligned mayors?

   ds_0028 (agent)

   | Variable Name | Description | | --- | --- | | municipality | Text identifier for a Philippine local government unit (city/municipality), concatenated as region_province_municipality. Names align with official administrative geography used by the Philippine Statistics Authority and the Commission on Elections. | | postduterte | Period flag for the administrative change on July 1, 2016: 0 indicates observations from the months before July 1, 2016; 1 indicates observations from the months on/after that date. This creates a two-period panel for each municipality. | | drug_related_pcap | Per‑capita rate of police blotter incidents related to anti‑illegal‑drugs operations in the municipality and period, scaled per 1,000 residents. Incidents are drawn from the Philippine National Police (PNP) Bantay Krimen system and follow the PNP anti‑illegal drugs operations manual (e.g., buy‑busts, search and seizure via warrant, eradication, controlled delivery, clandestine laboratory raids, in flagrante operations). Voluntary surrenders are excluded. Counts are aggregated by municipality and normalized using official population totals. | | interaction | Post‑period outsider‑executive indicator: equals 1 if a municipality’s 2016 elected mayor is classified as a non‑establishment/outsider (independent or minor‑party) and the observation falls after July 1, 2016; 0 otherwise. Constructed from 2016 mayoral election returns and party labels that distinguish establishment (Liberal Party) from independents/minor parties. By construction, this variable is 0 in all pre‑period rows. | Dataset Context: This dataset links administrative crime records to local political context around the national administration change on July 1, 2016 in the Philippines. The core measurement of policing activity comes from the Philippine National Police’s Bantay Krimen (Crime Watch) platform, which publishes geocoded police blotter entries in near–real time as reported by municipal and city police stations and organized by provincial police offices. Within these records, drug-related incidents are tagged according to the Revised PNP Manual on Anti‑Illegal Drugs Operations and Investigation. Only events requiring active police operations are included (such as buy‑busts, searches by warrant, controlled deliveries, clandestine laboratory raids, eradication, or in‑flagrante operations); voluntary surrenders are recorded by separate agencies and are not part of this series. Incidents are aggregated to the municipality/city level and scaled per capita, allowing comparisons across differently sized localities. The time structure of the file is a simple two‑period panel: one observation per municipality before July 1, 2016 and one after. This timing captures the onset of a high‑salience national campaign against illegal drugs and the subsequent operational phases that characterized policing during the early years of the new administration (including surges and pauses communicated via national directives). Because mayors play a formal role in local police supervision—such as recommending municipal police chiefs and exercising operational oversight—information on the political profile of local executives is merged using certified 2016 mayoral election returns. Party labels at that election are used to categorize mayors as establishment (Liberal Party) versus non‑establishment/outsider (independents and minor parties), reflecting short‑term access to prevailing patronage networks at the time. In sum, the dataset provides a municipality‑by‑period view of police‑recorded drug operations aligned to an administrative break in mid‑2016, with standardized geographic identifiers, a clear temporal divider, per‑capita scaling, and a constructed indicator that flags outsider‑led municipalities in the post‑change period. It is designed to facilitate analyses of how policing activity varied across places and time under the institutional and political conditions prevailing during this transition.

   file 1930004a6c443521fb50e5b4970b189df8b7b4b761e32d8610d307ad93c85cac · frame —

   ref_panel (reference)

   file 1930004a6c443521fb50e5b4970b189df8b7b4b761e32d8610d307ad93c85cac · frame —

   1. select {"columns":["municipality","postduterte","drug_related_pcap","interaction"]}
   2. dropna {"columns":["municipality","postduterte","drug_related_pcap","interaction"]}
   3. derive {"args":{"cmp":"gt","column":"postduterte","value":0},"name":"postduterte","op":"indicator"}
   4. derive {"args":{"cmp":"gt","column":"interaction","value":0},"name":"interaction","op":"indicator"}
   5. merge {"how":"inner","on":["municipality"],"with":"ref_treat"}
   6. derive {"args":{"columns":["treated","postduterte"]},"name":"treated_post","op":"interaction"}

   ref_treat (reference)

   file 1930004a6c443521fb50e5b4970b189df8b7b4b761e32d8610d307ad93c85cac · frame —

   1. select {"columns":["municipality","postduterte","drug_related_pcap","interaction"]}
   2. dropna {"columns":["municipality","postduterte","drug_related_pcap","interaction"]}
   3. derive {"args":{"cmp":"gt","column":"postduterte","value":0},"name":"postduterte","op":"indicator"}
   4. derive {"args":{"cmp":"gt","column":"interaction","value":0},"name":"interaction","op":"indicator"}
   5. collapse {"aggs":[{"column":"interaction","fn":"max","name":"treated"}],"by":["municipality"]}
2. 1Transformação
   ref_treat · select {"columns":["municipality","postduterte","drug_related_pcap","interaction"]}

   378 → 378 linhas

   Frame antes → depois: 221ad6062e12 → 221ad6062e12
3. 2Transformação
   ref_treat · dropna {"columns":["municipality","postduterte","drug_related_pcap","interaction"]}

   378 → 378 linhas

   Frame antes → depois: 221ad6062e12 → 221ad6062e12
4. 3Transformação
   ref_treat · derive {"args":{"cmp":"gt","column":"postduterte","value":0},"name":"postduterte","op":"indicator"}

   378 → 378 linhas

   Frame antes → depois: 221ad6062e12 → 8e6cc15cddb9
5. 4Transformação
   ref_treat · derive {"args":{"cmp":"gt","column":"interaction","value":0},"name":"interaction","op":"indicator"}

   378 → 378 linhas

   Frame antes → depois: 8e6cc15cddb9 → 83b68fcb0b92
6. 5Transformação
   ref_treat · collapse {"aggs":[{"column":"interaction","fn":"max","name":"treated"}],"by":["municipality"]}

   378 → 189 linhas

   Frame antes → depois: 83b68fcb0b92 → 69228dd5879f
7. 6Transformação
   ref_panel · select {"columns":["municipality","postduterte","drug_related_pcap","interaction"]}

   378 → 378 linhas

   Frame antes → depois: 221ad6062e12 → 221ad6062e12
8. 7Transformação
   ref_panel · dropna {"columns":["municipality","postduterte","drug_related_pcap","interaction"]}

   378 → 378 linhas

   Frame antes → depois: 221ad6062e12 → 221ad6062e12
9. 8Transformação
   ref_panel · derive {"args":{"cmp":"gt","column":"postduterte","value":0},"name":"postduterte","op":"indicator"}

   378 → 378 linhas

   Frame antes → depois: 221ad6062e12 → 8e6cc15cddb9
10. 9Transformação
    ref_panel · derive {"args":{"cmp":"gt","column":"interaction","value":0},"name":"interaction","op":"indicator"}

    378 → 378 linhas

    Frame antes → depois: 8e6cc15cddb9 → 83b68fcb0b92
11. 10Transformação
    ref_panel · merge {"how":"inner","on":["municipality"],"with":"ref_treat"}

    378 → 378 linhas

    Frame antes → depois: 83b68fcb0b92 → 84301f4ce536
12. 11Transformação
    ref_panel · derive {"args":{"columns":["treated","postduterte"]},"name":"treated_post","op":"interaction"}

    378 → 378 linhas

    Frame antes → depois: 84301f4ce536 → 0e072f58f7a1
13. 12Turno do modelo
    call_659f7bc2referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('drug_related_pcap ~ treated + postduterte + treated:postduterte'), cluster by municipality

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "municipality",
        "controls": [
          "treated",
          "postduterte"
        ],
        "dataset": "ref_panel",
        "x": [
          "treated_post"
        ],
        "y": "drug_related_pcap"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
14. 13Ferramenta
    infer.olsv0.1.0call_a4cc2013decidido pelo modeloreexecutável

    Motivo: smf.ols('drug_related_pcap ~ treated + postduterte + treated:postduterte'), cluster by municipality

    Argumentos

    {
      "cluster": "municipality",
      "controls": [
        "treated",
        "postduterte"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treated_post"
      ],
      "y": "drug_related_pcap"
    }

    Figuras call_a4cc2013

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treated_post Coefficient on treated_post in the regression of drug_related_pcap	0,361468	0.36146776953030796	0,1780850.17808457478058767	0,010167 – 0,712769

    Diagnósticos

    • n_obs = 378378
    • k = 44
    • r2 = 0,2074470.20744738507869787
    • df_resid = 374374
    • se_type = "cluster"
    • n_clusters = 189189

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.2405153444899109,
          "ci_low": 0.1407543976936284,
          "coef": 0.19063487109176966,
          "p": 1.9504761374991924e-12,
          "se": 0.02528586037074996,
          "t": 7.539188633355392,
          "term": "Intercept"
        },
        {
          "ci_high": 0.712768566288561,
          "ci_low": 0.01016697277205486,
          "coef": 0.36146776953030796,
          "p": 0.04379104463360018,
          "se": 0.17808457478058767,
          "t": 2.02975339091362,
          "term": "treated_post"
        },
        {
          "ci_high": 0.07438691249005357,
          "ci_low": -0.06690383329100745,
          "coef": 0.003741539599523063,
          "p": 0.9169024325319457,
          "se": 0.0358121908846215,
          "t": 0.10447670212574897,
          "term": "treated"
        },
        {
          "ci_high": 0.9290898338930391,
          "ci_low": 0.5883105781859839,
          "coef": 0.7587002060395115,
          "p": 9.748155254396144e-16,
          "se": 0.08637545005113964,
          "t": 8.783748224643851,
          "term": "postduterte"
        }
      ]
    }

    Digests dos dados

    ref_panel 0e072f58f7a129a0e659095422d2bfbdc7c47b5fdb0142e8cc35fa8fa8bfad0b

    sha256 do resultado

    afa7a8795d7d1c9417dfdc31cbfbc2a71380e8092d6dca8f46734045581b314e

    Resultado mostrado ao modelo
15. 14Turno do modelo
    call_8662b172referenceFim: 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.treated_post = 0,3615. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_a4cc2013:coef.treated_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
16. 15Guarda
    resposta aceita

    Números encontrados

    0,3615

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
17. 16Resposta

    Estimativa (infer.ols): coef.treated_post = 0,3615. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_a4cc2013:coef.treated_post · Parada: answered

    Figura da resposta: call_a4cc2013:coef.treated_post = 0,3614680.36146776953030796 (se 0,1780850.17808457478058767)
18. 17Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
19. 18Pontuação
    #1figura da resposta próximapassouobrigatória0.3614677695 in [0.01242200296, 0.7105135361]?
20. 19Pontuação
    #2figura da resposta próximapassouregistrada0.3614677695 vs 0.36146776953030685
21. 20Pontuação
    #3figura da resposta próximapassouregistrada0.1780845748 vs 0.17808457478058692

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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