Tarefa crb_papers.q002

Veredito

Pergunta feita ao analista

For countries that newly experienced at least one fatal armed conflict in 2003-2005, how did the log of annual non-fuel mineral exploration investment change relative to countries that remained conflict-free in both 2000-2002 and 2003-2005?

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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

   For countries that newly experienced at least one fatal armed conflict in 2003-2005, how did the log of annual non-fuel mineral exploration investment change relative to countries that remained conflict-free in both 2000-2002 and 2003-2005?

   ds_0001 (agent)

   | Variable Name | Description | | --- | --- | | dv | Natural log of total annual exploration spending on non‑fuel minerals (USD, constant 1997) plus one, aggregated to the country–year. Figures come from SNL Metals & Mining’s compilation of company surveys and reports and cover activities such as drilling, geophysics, sampling, assaying, and feasibility studies for metals and minerals (e.g., copper, gold, diamonds, iron ore, silver, uranium). Values are deflated to 1997 dollars and summed across firms within each country and year; the log(+1) form preserves observations with zero reported spending. | | ucdp_lead | Indicator equal to 1 if the UCDP Georeferenced Event Dataset records at least one fatal armed conflict event within the country in the current calendar year or the previous year; 0 otherwise. UCDP defines events as organized use of armed force resulting in at least one direct death and classifies state‑based, one‑sided, and non‑state violence. | | country | Country or territory corresponding to the observation. The panel spans a broad global sample (about 177 countries). | | year | Calendar year of the observation (1997–2014). Stored numerically; represents the Gregorian year. | Dataset Context: This global panel links country–year exploration budgets in the mining sector with standardized information on the presence of armed conflict. Exploration spending is compiled by SNL Metals & Mining from company surveys and public financial reports, then aggregated from firm-level entries to the country–year level. The series covers commercially oriented exploration for non‑fuel minerals—such as base and precious metals, diamonds, iron ore, rare earths, and uranium—and excludes hydrocarbons. Expenditures typically encompass early‑stage and appraisal activities (e.g., drilling campaigns, geophysical surveys, sampling and assaying, scoping and feasibility studies). SNL’s process captures the vast majority of global nonferrous exploration outlays and excludes very small projects below common reporting thresholds; amounts are converted to constant 1997 USD to remove the effects of inflation. Because many country–years legitimately report zero exploration, the spending measure is stored as ln(expenditure + 1). Conflict information is sourced from the Uppsala Conflict Data Program’s georeferenced event data, which catalogs individual incidents where organized actors use armed force resulting in at least one direct fatality. Events include state‑based battles, one‑sided violence against civilians, and non‑state clashes. For each country–year, the dataset flags whether any fatal incident occurred in that year or the year immediately prior, capturing near‑term conflict conditions that overlap with annual financial reporting cycles. The 1997–2014 window spans substantial swings in global metals prices and investment appetites, including the mid‑2000s commodity upcycle and subsequent downturn, and includes both “junior” and “major” mining companies whose exploration budgets respond to financing conditions and market sentiment. The resulting dataset provides harmonized, inflation‑adjusted coverage of exploration activity across roughly 177 countries aligned with a consistent, event‑based record of armed conflict incidence.

   file 887d9adebe0378bfe7ea873256a67275c50fc96aab151431c32de798d1756d41 · frame —

   ref_panel (reference)

   file 887d9adebe0378bfe7ea873256a67275c50fc96aab151431c32de798d1756d41 · frame —

   1. select {"columns":["country","year","dv","ucdp_lead"]}
   2. dropna {"columns":["country","year","dv","ucdp_lead"]}
   3. derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   4. filter {"query":"year in [2000, 2001, 2002, 2003, 2004, 2005]"}
   5. collapse {"aggs":[{"column":"dv","fn":"count","name":"n"}],"by":["country"]}
   6. select {"columns":["country"]}
   7. merge {"how":"inner","on":["country"],"with":"ref_pre"}
   8. merge {"how":"inner","on":["country"],"with":"ref_post"}
   9. filter {"query":"d_pre == 0 and (d_post > 0 or d_post == 0)"}
   10. derive {"args":{"cmp":"gt","column":"d_post","value":0},"name":"treated","op":"indicator"}
   11. reshape_long {"id":["country"],"j":"post","stubs":["y"]}
   12. derive {"args":{"columns":["treated","post"]},"name":"interaction","op":"interaction"}

   ref_post (reference)

   file 887d9adebe0378bfe7ea873256a67275c50fc96aab151431c32de798d1756d41 · frame —

   1. select {"columns":["country","year","dv","ucdp_lead"]}
   2. dropna {"columns":["country","year","dv","ucdp_lead"]}
   3. derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   4. filter {"query":"year in [2003, 2004, 2005]"}
   5. collapse {"aggs":[{"column":"dv","fn":"mean","name":"y1"},{"column":"ucdp_lead","fn":"mean","name":"d_post"}],"by":["country"]}

   ref_pre (reference)

   file 887d9adebe0378bfe7ea873256a67275c50fc96aab151431c32de798d1756d41 · frame —

   1. select {"columns":["country","year","dv","ucdp_lead"]}
   2. dropna {"columns":["country","year","dv","ucdp_lead"]}
   3. derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}
   4. filter {"query":"year in [2000, 2001, 2002]"}
   5. collapse {"aggs":[{"column":"dv","fn":"mean","name":"y0"},{"column":"ucdp_lead","fn":"mean","name":"d_pre"}],"by":["country"]}
2. 1Transformação
   ref_pre · select {"columns":["country","year","dv","ucdp_lead"]}

   3186 → 3186 linhas

   Frame antes → depois: 6fc175d00f59 → a23840f8ea16
3. 2Transformação
   ref_pre · dropna {"columns":["country","year","dv","ucdp_lead"]}

   3186 → 3186 linhas

   Frame antes → depois: a23840f8ea16 → a23840f8ea16
4. 3Transformação
   ref_pre · derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}

   3186 → 3186 linhas

   Frame antes → depois: a23840f8ea16 → a23840f8ea16
5. 4Transformação
   ref_pre · filter {"query":"year in [2000, 2001, 2002]"}

   3186 → 531 linhas

   Frame antes → depois: a23840f8ea16 → c3ee4ac54e46
6. 5Transformação
   ref_pre · collapse {"aggs":[{"column":"dv","fn":"mean","name":"y0"},{"column":"ucdp_lead","fn":"mean","name":"d_pre"}],"by":["country"]}

   531 → 177 linhas

   Frame antes → depois: c3ee4ac54e46 → 82c6cd8f83e9
7. 6Transformação
   ref_post · select {"columns":["country","year","dv","ucdp_lead"]}

   3186 → 3186 linhas

   Frame antes → depois: 6fc175d00f59 → a23840f8ea16
8. 7Transformação
   ref_post · dropna {"columns":["country","year","dv","ucdp_lead"]}

   3186 → 3186 linhas

   Frame antes → depois: a23840f8ea16 → a23840f8ea16
9. 8Transformação
   ref_post · derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}

   3186 → 3186 linhas

   Frame antes → depois: a23840f8ea16 → a23840f8ea16
10. 9Transformação
    ref_post · filter {"query":"year in [2003, 2004, 2005]"}

    3186 → 531 linhas

    Frame antes → depois: a23840f8ea16 → 28bb52b939a4
11. 10Transformação
    ref_post · collapse {"aggs":[{"column":"dv","fn":"mean","name":"y1"},{"column":"ucdp_lead","fn":"mean","name":"d_post"}],"by":["country"]}

    531 → 177 linhas

    Frame antes → depois: 28bb52b939a4 → 18c22d1d2649
12. 11Transformação
    ref_panel · select {"columns":["country","year","dv","ucdp_lead"]}

    3186 → 3186 linhas

    Frame antes → depois: 6fc175d00f59 → a23840f8ea16
13. 12Transformação
    ref_panel · dropna {"columns":["country","year","dv","ucdp_lead"]}

    3186 → 3186 linhas

    Frame antes → depois: a23840f8ea16 → a23840f8ea16
14. 13Transformação
    ref_panel · derive {"args":{"cmp":"gt","column":"ucdp_lead","value":0},"name":"ucdp_lead","op":"indicator"}

    3186 → 3186 linhas

    Frame antes → depois: a23840f8ea16 → a23840f8ea16
15. 14Transformação
    ref_panel · filter {"query":"year in [2000, 2001, 2002, 2003, 2004, 2005]"}

    3186 → 1062 linhas

    Frame antes → depois: a23840f8ea16 → 46773d193e95
16. 15Transformação
    ref_panel · collapse {"aggs":[{"column":"dv","fn":"count","name":"n"}],"by":["country"]}

    1062 → 177 linhas

    Frame antes → depois: 46773d193e95 → b336986f44ea
17. 16Transformação
    ref_panel · select {"columns":["country"]}

    177 → 177 linhas

    Frame antes → depois: b336986f44ea → 08a3639bb0e5
18. 17Transformação
    ref_panel · merge {"how":"inner","on":["country"],"with":"ref_pre"}

    177 → 177 linhas

    Frame antes → depois: 08a3639bb0e5 → 82c6cd8f83e9
19. 18Transformação
    ref_panel · merge {"how":"inner","on":["country"],"with":"ref_post"}

    177 → 177 linhas

    Frame antes → depois: 82c6cd8f83e9 → 608f541e4c09
20. 19Transformação
    ref_panel · filter {"query":"d_pre == 0 and (d_post > 0 or d_post == 0)"}

    177 → 106 linhas

    Frame antes → depois: 608f541e4c09 → d274308cef3f
21. 20Transformação
    ref_panel · derive {"args":{"cmp":"gt","column":"d_post","value":0},"name":"treated","op":"indicator"}

    106 → 106 linhas

    Frame antes → depois: d274308cef3f → e83c626f6e92
22. 21Transformação
    ref_panel · reshape_long {"id":["country"],"j":"post","stubs":["y"]}

    106 → 212 linhas

    Frame antes → depois: e83c626f6e92 → 95e3be3d4654
23. 22Transformação
    ref_panel · derive {"args":{"columns":["treated","post"]},"name":"interaction","op":"interaction"}

    212 → 212 linhas

    Frame antes → depois: 95e3be3d4654 → e0b3f0f87ca1
24. 23Turno do modelo
    call_fdb96cd5referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "country",
        "controls": [
          "treated",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "interaction"
        ],
        "y": "y"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
25. 24Ferramenta
    infer.olsv0.1.0call_cc73ccc7decidido pelo modeloreexecutável

    Motivo: sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country

    Argumentos

    {
      "cluster": "country",
      "controls": [
        "treated",
        "post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "interaction"
      ],
      "y": "y"
    }

    Figuras call_cc73ccc7

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.interaction Coefficient on interaction in the regression of y	-0,594928	-0.5949280867361766	2,276832.2768332933651694	-5,10947 – 3,91961

    Diagnósticos

    • n_obs = 212212
    • k = 44
    • r2 = 0,02011060.020110646661667064
    • df_resid = 208208
    • se_type = "cluster"
    • n_clusters = 106106

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 8.231009680191107,
          "ci_low": 5.312180505709703,
          "coef": 6.771595092950405,
          "p": 3.814046522815149e-15,
          "se": 0.7360315439004198,
          "t": 9.200142506211062,
          "term": "Intercept"
        },
        {
          "ci_high": 3.9196117432333617,
          "ci_low": -5.109467916705715,
          "coef": -0.5949280867361766,
          "p": 0.7943758657305547,
          "se": 2.2768332933651694,
          "t": -0.2612962874663829,
          "term": "interaction"
        },
        {
          "ci_high": 8.043883430636296,
          "ci_low": -0.16074601246842102,
          "coef": 3.941568709083937,
          "p": 0.05950024155062042,
          "se": 2.0689343963447757,
          "t": 1.9051201990974576,
          "term": "treated"
        },
        {
          "ci_high": 0.4852214100972564,
          "ci_low": -0.7379243034675643,
          "coef": -0.12635144668515394,
          "p": 0.6828963287946432,
          "se": 0.3084366278921579,
          "t": -0.40965123872814346,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel e0b3f0f87ca1af4d9d6b1c6c002c9e43bbe00e22024a09235045bfd9485c24f6

    sha256 do resultado

    853f1991cdcc62f1d1dbb08f2f4fa15abc6dabe1e6ddd9d4f8e666642c5186a0

    Resultado mostrado ao modelo
26. 25Turno do modelo
    call_c22ba83dreferenceFim: 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.interaction = -0,5949. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_cc73ccc7:coef.interaction"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
27. 26Guarda
    resposta aceita

    Números encontrados

    -0,5949

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
28. 27Resposta

    Estimativa (infer.ols): coef.interaction = -0,5949. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_cc73ccc7:coef.interaction · Parada: answered

    Figura da resposta: call_cc73ccc7:coef.interaction = -0,594928-0.5949280867361766 (se 2,276832.2768332933651694)
29. 28Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
30. 29Pontuação
    #1figura da resposta próximapassouobrigatória-0.5949280867 in [-5.057521342, 3.867665168]?
31. 30Pontuação
    #2figura da resposta próximapassouregistrada-0.5949280867 vs -0.5949280867362181
32. 31Pontuação
    #3figura da resposta próximapassouregistrada2.276833293 vs 2.2768332933651543

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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