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?

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_0001— | 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.
   ref_panel
   ref_post
   ref_pre
2. 2Chamada que gerou a resposta finalinfer.ols(Regressão linear por mínimos quadrados ordinários (MQO))citada na resposta

   Ver chamada call_8c09aeb1 nos detalhes

   Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Por que este é o caminho de referência: sm.OLS(y ~ const + treated + post + interaction), cov_type='cluster' by country
   Argumentos principais

   dataset:

   "ds_0001"

   intercept:

   true

   level:

   0,950.95

   se:

   "HC1"

   x:

   ["ucdp_lead"]

   y:

   "dv"

   Resultado calculado

   • coef.ucdp_lead = 2,66131 2.661306316484741
     Coefficient on ucdp_lead in the regression of dv
   Diagnósticos: n_obs = 3.1863186 (observations used) · k = 22 (parameters estimated, fixed-effect dummies included) · r2 = 0,02534050.02534047668387529 · df_resid = 3.1843184 · se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
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: 2,661
   Figuras citadas: call_8c09aeb1:coef.ucdp_lead
   • ✓
     #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.q002.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

   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. 1Turno do modelo
   call_9fa62553naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Chamadas pedidas

   • infer.ols

     {
       "dataset": "ds_0001",
       "x": [
         "ucdp_lead"
       ],
       "y": "dv"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
3. 2Ferramenta
   infer.ols— Regressão linear por mínimos quadrados ordinários (MQO)v0.1.0call_8c09aeb1decidido pelo modeloreexecutável

   Motivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.

   Argumentos

   {
     "cluster": null,
     "controls": [],
     "dataset": "ds_0001",
     "fe": [],
     "intercept": true,
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "weights": null,
     "x": [
       "ucdp_lead"
     ],
     "y": "dv"
   }

   Figuras call_8c09aeb1

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.ucdp_lead Coefficient on ucdp_lead in the regression of dv	2,66131	2.661306316484741	0,2835890.28358851463203155	2,10527 – 3,21734

   Diagnósticos

   • n_obs = 3.1863186 (observations used)
   • k = 22 (parameters estimated, fixed-effect dummies included)
   • r2 = 0,02534050.02534047668387529
   • df_resid = 3.1843184
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.ucdp_lead

   1. Coeficiente de ucdp_lead na regressão por mínimos quadrados ordinários de dv.→ 2,66131 (2.661306316484741)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,283589 (0.28358851463203155)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 3184 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": 9.470788605928105,
         "ci_low": 8.807655767880265,
         "coef": 9.139222186904185,
         "p": 0,
         "se": 0.16910534117379913,
         "t": 54.04455071298599,
         "term": "Intercept"
       },
       {
         "ci_high": 3.217340961045238,
         "ci_low": 2.1052716719242435,
         "coef": 2.661306316484741,
         "p": 1.1663999211684164e-20,
         "se": 0.28358851463203155,
         "t": 9.38439386354522,
         "term": "ucdp_lead"
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   ds_0001 6fc175d00f591712a12119577295b02c2ba59fe5887bf75a4f7ace2838ffc2e2

   sha256 do resultado

   bb939af839916901a85c8f10e72709f63706aca2f9d2892ccb262b1e3b582d7e

   Resultado mostrado ao modelo
4. 3Turno do modelo
   call_103fd8f0naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa ingênua (infer.ols): coef.ucdp_lead = 2,661.",
       "caveats": [],
       "figure_refs": [
         "call_8c09aeb1:coef.ucdp_lead"
       ]
     }

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

   Números encontrados

   2,661

   Sem lastro

   —

   Não citados

   —

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

   Estimativa ingênua (infer.ols): coef.ucdp_lead = 2,661.

   Figuras citadas: call_8c09aeb1:coef.ucdp_lead · Parada: answered

   Figura da resposta: call_8c09aeb1:coef.ucdp_lead = 2,661312.661306316484741 (se 0,2835890.28358851463203155)
7. 6Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
8. 7Pontuação
   #1figura da resposta próximapassouobrigatória2.661306316 in [-5.057521342, 3.867665168]?
9. 8Pontuação
   #2figura da resposta próximafalhouregistrada2.661306316 vs -0.5949280867362181
10. 9Pontuação
    #3figura da resposta próximafalhouregistrada0.2835885146 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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