Tarefa crb_papers.q075

Veredito

Pergunta feita ao analista

What is the causal effect of a one-unit increase in log(number of broadband providers) on affective partisan polarization (outcome scaled 0-1), estimated for compliers using county-level data pooled across 2004 and 2008?

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_0045— | Variable Name | Description | |--------------------|-----------------------------------------------------------------------------| | (unnamed) | Row identifier / dataset index (row number) | | D | Log of the number of broadband providers in the county (natural log of provider count from FCC records) | | outcome | Rescaled measure of partisan affect: difference in feeling‑thermometer scores toward the in‑ and out‑party presidential candidates (scaled 0–1) | | Total_log | Log of the state right‑of‑way (ROW) regulation index (Beyer & Kende 2003) | | region | Census region categorical label (e.g., "1: Northeast", "2: Midwest", "3: South", "4: West") | | percent_black | Proportion of the county population identifying as Black (fraction between 0 and 1) | | percent_white | Proportion of the county population identifying as White (fraction between 0 and 1) | | percent_male | Proportion of the county population that is male (fraction between 0 and 1) | | lowed | Indicator for low‑education county status (e.g., "0: Not low‑education type") | | unemploymentrate | County unemployment rate (percent) | | density | County population density (as provided in the ICPSR County Characteristics file) | | HHINC_log | Log of median household income in the county (natural log of HH income) | | year | Survey year (2004 or 2008) | | state | U.S. state name (state of residence / county mapping) | Data Description: This merged file brings together individual survey records from national rolling‑cross‑section studies (NAES 2004 and 2008) with county‑level socioeconomic indicators (ICPSR County Characteristics), broadband provider counts (FCC Form 477), and a state‑level right‑of‑way (ROW) regulation index (Beyer & Kende 2003). Each row represents a respondent observation linked to county and state characteristics for the survey year; variables include a constructed index of partisan affect, log‑transformed measures of broadband/provider and income, regional codes, and standard county demographic and economic controls. The data were assembled to enable descriptive and comparative analyses of regional policy indicators, local broadband availability, and survey‑measured attitudes.
   ref_iv
   Transformação "select" aplicada à base "ref_iv" (114803 → 114803 linhas). {"columns":["outcome","D","Total_log","percent_black","percent_white","percent_male","unemploymentrate","density","HHINC_log","region","lowed","year","state"]}
   Transformação "dummies" aplicada à base "ref_iv" (114803 → 114803 linhas). {"column":"region","drop_first":true,"prefix":"region"}
   Transformação "dummies" aplicada à base "ref_iv" (114803 → 114803 linhas). {"column":"lowed","drop_first":true,"prefix":"lowed"}
   Transformação "dummies" aplicada à base "ref_iv" (114803 → 114803 linhas). {"column":"year","drop_first":true,"prefix":"year"}
2. 2Chamada que gerou a resposta finalinfer.iv_2sls(Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E))citada na respostacoincide com a referência

   Ver chamada call_af215a4e nos detalhes

   IV2SLS.from_formula('outcome ~ 1 + controls + C(region) + C(lowed) + C(year) + [D ~ Total_log]').fit(cov_type='clustered', clusters=state)

   Por que este é o caminho de referência: IV2SLS.from_formula('outcome ~ 1 + controls + C(region) + C(lowed) + C(year) + [D ~ Total_log]').fit(cov_type='clustered', clusters=state)
   Argumentos principais

   cluster:

   "state"

   dataset:

   "ref_iv"

   endog:

   ["D"]

   exog:

   ["percent_black","percent_white","percent_male","unemploymentrate","density","HHINC_log","region_2: Midwest","region_3: South","region_4: West","lowed_1: Low-education type","year_2008.0"]

   instruments:

   ["Total_log"]

   level:

   0,950.95

   se:

   "HC1"

   y:

   "outcome"

   Resultado calculado

   • coef.D = 0,0315775 0.031577464920755464
     2SLS effect of D on outcome
   Diagnósticos: first_stage_F.D = 11,163211.163168071167823 (Wald F of the excluded instruments in the first stage for D (cluster-robust standard errors (CR1) by 'state')) · partial_r2.D = 0,07662620.07662616610864015 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · n_obs = 114.803114803 · se_type = "cluster" (cluster-robust standard errors (CR1) by 'state') · n_clusters = 4848 (48 clusters of 'state')
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: 0,03158
   Figuras citadas: call_af215a4e:coef.D
   • ✓
     #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.q075.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

   What is the causal effect of a one-unit increase in log(number of broadband providers) on affective partisan polarization (outcome scaled 0-1), estimated for compliers using county-level data pooled across 2004 and 2008?

   ds_0045 (agent)

   | Variable Name | Description | |--------------------|-----------------------------------------------------------------------------| | (unnamed) | Row identifier / dataset index (row number) | | D | Log of the number of broadband providers in the county (natural log of provider count from FCC records) | | outcome | Rescaled measure of partisan affect: difference in feeling‑thermometer scores toward the in‑ and out‑party presidential candidates (scaled 0–1) | | Total_log | Log of the state right‑of‑way (ROW) regulation index (Beyer & Kende 2003) | | region | Census region categorical label (e.g., "1: Northeast", "2: Midwest", "3: South", "4: West") | | percent_black | Proportion of the county population identifying as Black (fraction between 0 and 1) | | percent_white | Proportion of the county population identifying as White (fraction between 0 and 1) | | percent_male | Proportion of the county population that is male (fraction between 0 and 1) | | lowed | Indicator for low‑education county status (e.g., "0: Not low‑education type") | | unemploymentrate | County unemployment rate (percent) | | density | County population density (as provided in the ICPSR County Characteristics file) | | HHINC_log | Log of median household income in the county (natural log of HH income) | | year | Survey year (2004 or 2008) | | state | U.S. state name (state of residence / county mapping) | Data Description: This merged file brings together individual survey records from national rolling‑cross‑section studies (NAES 2004 and 2008) with county‑level socioeconomic indicators (ICPSR County Characteristics), broadband provider counts (FCC Form 477), and a state‑level right‑of‑way (ROW) regulation index (Beyer & Kende 2003). Each row represents a respondent observation linked to county and state characteristics for the survey year; variables include a constructed index of partisan affect, log‑transformed measures of broadband/provider and income, regional codes, and standard county demographic and economic controls. The data were assembled to enable descriptive and comparative analyses of regional policy indicators, local broadband availability, and survey‑measured attitudes.

   file 36b44bf602af6eec1054d3cfc3659122577e1a00422a6c5d5bfd6a18280aa1ff · frame —

   ref_iv (reference)

   file 36b44bf602af6eec1054d3cfc3659122577e1a00422a6c5d5bfd6a18280aa1ff · frame —

   1. select {"columns":["outcome","D","Total_log","percent_black","percent_white","percent_male","unemploymentrate","density","HHINC_log","region","lowed","year","state"]}
   2. dummies {"column":"region","drop_first":true,"prefix":"region"}
   3. dummies {"column":"lowed","drop_first":true,"prefix":"lowed"}
   4. dummies {"column":"year","drop_first":true,"prefix":"year"}
2. 1Transformação
   ref_iv · select {"columns":["outcome","D","Total_log","percent_black","percent_white","percent_male","unemploymentrate","density","HHINC_log","region","lowed","year","state"]}

   114803 → 114803 linhas

   Frame antes → depois: 543a25f838c9 → f608159cfd04
3. 2Transformação
   ref_iv · dummies {"column":"region","drop_first":true,"prefix":"region"}

   114803 → 114803 linhas

   Frame antes → depois: f608159cfd04 → dd99e25b0cb0
4. 3Transformação
   ref_iv · dummies {"column":"lowed","drop_first":true,"prefix":"lowed"}

   114803 → 114803 linhas

   Frame antes → depois: dd99e25b0cb0 → 24907b990ab9
5. 4Transformação
   ref_iv · dummies {"column":"year","drop_first":true,"prefix":"year"}

   114803 → 114803 linhas

   Frame antes → depois: 24907b990ab9 → 111dfbde9448
6. 5Turno do modelo
   call_9be3da67referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   IV2SLS.from_formula('outcome ~ 1 + controls + C(region) + C(lowed) + C(year) + [D ~ Total_log]').fit(cov_type='clustered', clusters=state)

   Chamadas pedidas

   • infer.iv_2sls

     {
       "cluster": "state",
       "dataset": "ref_iv",
       "endog": [
         "D"
       ],
       "exog": [
         "percent_black",
         "percent_white",
         "percent_male",
         "unemploymentrate",
         "density",
         "HHINC_log",
         "region_2: Midwest",
         "region_3: South",
         "region_4: West",
         "lowed_1: Low-education type",
         "year_2008.0"
       ],
       "instruments": [
         "Total_log"
       ],
       "y": "outcome"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
7. 6Ferramenta
   infer.iv_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_af215a4edecidido pelo modeloreexecutável

   Motivo: IV2SLS.from_formula('outcome ~ 1 + controls + C(region) + C(lowed) + C(year) + [D ~ Total_log]').fit(cov_type='clustered', clusters=state)

   Argumentos

   {
     "cluster": "state",
     "dataset": "ref_iv",
     "endog": [
       "D"
     ],
     "exog": [
       "percent_black",
       "percent_white",
       "percent_male",
       "unemploymentrate",
       "density",
       "HHINC_log",
       "region_2: Midwest",
       "region_3: South",
       "region_4: West",
       "lowed_1: Low-education type",
       "year_2008.0"
     ],
     "instruments": [
       "Total_log"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "outcome"
   }

   Figuras call_af215a4e

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.D 2SLS effect of D on outcome	0,0315775	0.031577464920755464	0,01411960.014119552673326995	0,00317259 – 0,0599823

   Diagnósticos

   • first_stage_F.D = 11,163211.163168071167823 (Wald F of the excluded instruments in the first stage for D (cluster-robust standard errors (CR1) by 'state'))
   • partial_r2.D = 0,07662620.07662616610864015 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • n_obs = 114.803114803
   • se_type = "cluster" (cluster-robust standard errors (CR1) by 'state')
   • n_clusters = 4848 (48 clusters of 'state')

   Derivação matemática e pressupostos

   coef.D

   1. Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de Total_log na forma reduzida (outcome sobre Total_log e os controles) e no primeiro estágio (D sobre Total_log e os controles).β = ρ / π→ 0,0315775 (0.031577464920755464)
   2. Erro-padrão robusto por conglomerado (CR1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0141196 (0.014119552673326995)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 47 graus de liberdade vezes o erro-padrão.IC = β ± c · ep

   Pressupostos do estimador

   • the instruments affect the outcome only through the endogenous regressors (exclusion restriction)
   • the instruments are as good as randomly assigned given the controls
   • errors are independent across clusters of 'state' and may be correlated within them

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 0.8495866450365945,
         "ci_low": 0.40588193853094934,
         "coef": 0.6277342917837719,
         "p": 7.842625236153413e-7,
         "se": 0.1102788116751243,
         "t": 5.692247515624714,
         "term": "Intercept"
       },
       {
         "ci_high": 0.15274065850585156,
         "ci_low": 0.054957036737102,
         "coef": 0.10384884762147678,
         "p": 0.00009331479981602386,
         "se": 0.024303239185519708,
         "t": 4.273045532274222,
         "term": "percent_black"
       },
       {
         "ci_high": 0.14788562825048102,
         "ci_low": 0.051412990467204905,
         "coef": 0.09964930935884296,
         "p": 0.00013588381657756942,
         "se": 0.023977405914149413,
         "t": 4.155967068148872,
         "term": "percent_white"
       },
       {
         "ci_high": 0.29982254623635657,
         "ci_low": -0.13250129548416018,
         "coef": 0.08366062537609818,
         "p": 0.44011839746196757,
         "se": 0.10745020015503606,
         "t": 0.7785990650123243,
         "term": "percent_male"
       },
       {
         "ci_high": 0.0017851814009369087,
         "ci_low": -0.0037729762831072468,
         "coef": -0.0009938974410851691,
         "p": 0.47541385712628204,
         "se": 0.0013814300716495824,
         "t": -0.7194699619491736,
         "term": "unemploymentrate"
       },
       {
         "ci_high": 0.05413338277974225,
         "ci_low": -0.017896999568422996,
         "coef": 0.01811819160565963,
         "p": 0.31669706767560335,
         "se": 0.017902503294180066,
         "t": 1.012047941449042,
         "term": "density"
       },
       {
         "ci_high": 0.016223437342713747,
         "ci_low": -0.04128182813971865,
         "coef": -0.012529195398502453,
         "p": 0.3851451016363781,
         "se": 0.014292416216199194,
         "t": -0.8766324188279455,
         "term": "HHINC_log"
       },
       {
         "ci_high": 0.021669876161284265,
         "ci_low": 0.007469627104537114,
         "coef": 0.01456975163291069,
         "p": 0.00014847901479671642,
         "se": 0.00352934410770997,
         "t": 4.128175430976702,
         "term": "region_2: Midwest"
       },
       {
         "ci_high": 0.02730229681338871,
         "ci_low": 0.010487717404844502,
         "coef": 0.018895007109116606,
         "p": 0.00004156445170796245,
         "se": 0.004179112388945727,
         "t": 4.521296713411263,
         "term": "region_3: South"
       },
       {
         "ci_high": 0.03724920215556011,
         "ci_low": 0.015950704563694205,
         "coef": 0.026599953359627157,
         "p": 0.000007730584064161387,
         "se": 0.005293549900324495,
         "t": 5.0249745181388725,
         "term": "region_4: West"
       },
       {
         "ci_high": -0.008662655701671957,
         "ci_low": -0.02645918144282662,
         "coef": -0.017560918572249288,
         "p": 0.0002446660617616693,
         "se": 0.004423166312875987,
         "t": -3.970214396218577,
         "term": "lowed_1: Low-education type"
       },
       {
         "ci_high": -0.06543774436239111,
         "ci_low": -0.10006065616456417,
         "coef": -0.08274920026347764,
         "p": 1.1152407730880538e-12,
         "se": 0.008605213139040039,
         "t": -9.61617091017327,
         "term": "year_2008.0"
       },
       {
         "ci_high": 0.0599823410694288,
         "ci_low": 0.0031725887720821314,
         "coef": 0.031577464920755464,
         "p": 0.03010763553541286,
         "se": 0.014119552673326995,
         "t": 2.236435222229661,
         "term": "D"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.16697571716832865,
         "ci_low": 0.041468611613175124,
         "coef": 0.10422216439075188,
         "endog": "D",
         "instrument": "Total_log",
         "p": 0.0016421957085471248,
         "se": 0.03119366158274146,
         "t": 3.3411327527004704
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.006360161320027556,
         "ci_low": 0.00022198216000086403,
         "coef": 0.00329107174001421,
         "instrument": "Total_log",
         "p": 0.03613129299587695,
         "se": 0.001525589189593471,
         "t": 2.1572463691167036
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   ref_iv 111dfbde9448b9cebbd7c50a4610902e4c7f63033d5cf4b4692d40c99192ec5d

   sha256 do resultado

   4c87eaf3db003665d623ee5fe000a5b81ec641d80e869ea3381e464ca52b99a4

   Resultado mostrado ao modelo
8. 7Turno do modelo
   call_6c6c63b8referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.iv_2sls): coef.D = 0,03158. Reference path transcribed from the item's own estimation script.",
       "caveats": [],
       "figure_refs": [
         "call_af215a4e:coef.D"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
9. 8Guarda
   resposta aceita

   Números encontrados

   0,03158

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
10. 9Resposta

    Estimativa (infer.iv_2sls): coef.D = 0,03158. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_af215a4e:coef.D · Parada: answered

    Figura da resposta: call_af215a4e:coef.D = 0,03157750.031577464920755464 (se 0,01411960.014119552673326995)
11. 10Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
12. 11Pontuação
    #1figura da resposta próximapassouobrigatória0.03157746492 in [0.004194364417, 0.05896056542]?
13. 12Pontuação
    #2figura da resposta próximapassouregistrada0.03157746492 vs 0.03157746492058777
14. 13Pontuação
    #3figura da resposta próximafalhouregistrada0.01411955267 vs 0.01397096964478184

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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