Tarefa crb_papers.q076

Veredito

Pergunta feita ao analista

What is the causal effect of being enrolled in public health insurance (versus only private insurance) on an individual's support for the Affordable Care Act, measured for the subgroup whose insurance enrollment was shifted by the exogenous eligibility variation exploited in this analysis?

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_0046— | Variable Name | Description | | --- | --- | | row_id | Row identifier / case index (sequential record number). | | educ | Education level (ordered categorical from the CCES; coded 1–6 with higher values indicating more education). | | votereg | Voter registration status (binary indicator; 1 = registered to vote, 0 = not registered). | | child18 | Child under age 18 present in household (binary indicator; 1 = yes, 0 = no). | | newsint | How closely respondent follows news about government and public affairs (ordinal scale; CCES item typically coded 1–4 with higher values = follows more closely). | | suppafford | Support for the Affordable Care Act (ACA) (dichotomous; 1 = support, 0 = oppose). | | dontcutmedicare | Budget-priority item: respondent selected “Don’t cut domestic spending such as Medicare and Social Security” as the option they would least prefer among deficit-reduction choices (dichotomous; 1 = selected this option, 0 = otherwise). | | byr4647 | Indicator for birth cohort in this extract: 1 = born in 1946–1947, 0 = born in 1948–1949 (no other birth years included in this file). | | privpubins3r | Health insurance type (recoded dichotomy used in the study): 1 = has any public insurance (including public-only or public + private), 0 = private insurance only. (Respondents with other/none/unknown coverage may appear as missing, depending on CCES source coding.) | | con | Ideology: conservative (binary indicator; 1 = self-identifies as conservative, 0 = otherwise). | | mod | Ideology: moderate (binary indicator; 1 = self-identifies as moderate, 0 = otherwise). | | ideostrength | Strength of ideological identification (ordinal measure; higher values indicate stronger ideology). | | rep | Party identification: Republican (binary indicator; 1 = Republican, 0 = otherwise). | | ind | Party identification: Independent (binary indicator; 1 = Independent, 0 = otherwise). | | employed | Employment status (binary indicator; 1 = employed full-time or part-time, 0 = otherwise). | | healthcaresupport | Occupation indicator: works in “healthcare support” occupation category (binary indicator; 1 = yes, 0 = no). | | hcsocial | Industry indicator: works in “healthcare or social assistance” industry category (binary indicator; 1 = yes, 0 = no). | | fininsur | Industry indicator: works in “finance or insurance” industry category (binary indicator; 1 = yes, 0 = no). | | hispanic2 | Hispanic ethnicity (binary indicator; 1 = Hispanic, 0 = not Hispanic). | | black | Race: Black/African American (binary indicator; 1 = Black, 0 = not Black). | | military | Military household affiliation (binary indicator; 1 = respondent or household member is affiliated with the military, 0 = otherwise). | | mobility | Residential mobility (binary indicator; 1 = recently moved, 0 = otherwise; exact reference period follows CCES coding). | | married | Marital status (binary indicator; 1 = married, 0 = not married). | | homeowner | Homeownership (binary indicator; 1 = owns home, 0 = does not own). | | labor | Labor union household indicator (binary indicator; 1 = respondent or someone in household belongs to a labor union, 0 = otherwise). | | publicemp | Public-sector employment (binary indicator; 1 = public employee, 0 = otherwise). | | vote08 | Self-reported voting in the 2008 election (binary indicator; 1 = voted, 0 = did not vote). | | bornagain | Religious identification as “born again” (binary indicator; 1 = yes, 0 = no). | | religimp | Importance of religion (ordinal scale; higher values indicate religion is more important to the respondent). | | male | Gender (binary indicator; 1 = male, 0 = not male). | | fincome | Family income (ordered categorical bins from the CCES; coded 1–16 with higher values indicating higher income; some respondents may have missing/refused). | Dataset Context: This file is an analysis-ready extract derived from the 2012 Cooperative Congressional Election Study (CCES), an online national survey administered by YouGov/Polymetrix. It contains individual-level responses covering demographic characteristics (education, income, sex, race/ethnicity, marital and homeownership status), political identifiers and behavior (party identification, ideology and its strength, voter registration and 2008 turnout, news consumption), work/household indicators (employment, public-employee and military household flags, union household, mobility), religiosity measures, and items about health insurance source and attitudes toward health-related policy questions. Variable codings mix binary indicators and ordered categorical scales; the table above summarizes each column as present in the provided CSV. This extract is restricted to respondents born in 1946–1949 (corresponding to roughly ages 63–66 at the time of the October–November 2012 CCES field period).
   ref_iv
   Transformação "select" aplicada à base "ref_iv" (5221 → 5221 linhas). {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}
   Transformação "dropna" aplicada à base "ref_iv" (5221 → 4389 linhas). {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}
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_85585a75 nos detalhes

   2SLS by matrices: X = [1, controls, D], Z = [1, controls, byr4647]

   Por que este é o caminho de referência: 2SLS by matrices: X = [1, controls, D], Z = [1, controls, byr4647]
   Argumentos principais

   dataset:

   "ref_iv"

   endog:

   ["privpubins3r"]

   exog:

   ["rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]

   instruments:

   ["byr4647"]

   level:

   0,950.95

   se:

   "HC1"

   y:

   "suppafford"

   Resultado calculado

   • coef.privpubins3r = 0,0459886 0.04598858983362574
     2SLS effect of privpubins3r on suppafford
   Diagnósticos: first_stage_F.privpubins3r = 1.186,481186.4814767053833 (Wald F of the excluded instruments in the first stage for privpubins3r (heteroskedasticity-robust standard errors (HC1))) · partial_r2.privpubins3r = 0,2252660.22526566832883976 (share of the variation the instruments explain beyond the controls) · weak_instrument = false (every first-stage F is at least 10) · n_obs = 4.3894389 · 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: 0,04599
   Figuras citadas: call_85585a75:coef.privpubins3r
   • ✓
     #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.q076.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 being enrolled in public health insurance (versus only private insurance) on an individual's support for the Affordable Care Act, measured for the subgroup whose insurance enrollment was shifted by the exogenous eligibility variation exploited in this analysis?

   ds_0046 (agent)

   | Variable Name | Description | | --- | --- | | row_id | Row identifier / case index (sequential record number). | | educ | Education level (ordered categorical from the CCES; coded 1–6 with higher values indicating more education). | | votereg | Voter registration status (binary indicator; 1 = registered to vote, 0 = not registered). | | child18 | Child under age 18 present in household (binary indicator; 1 = yes, 0 = no). | | newsint | How closely respondent follows news about government and public affairs (ordinal scale; CCES item typically coded 1–4 with higher values = follows more closely). | | suppafford | Support for the Affordable Care Act (ACA) (dichotomous; 1 = support, 0 = oppose). | | dontcutmedicare | Budget-priority item: respondent selected “Don’t cut domestic spending such as Medicare and Social Security” as the option they would least prefer among deficit-reduction choices (dichotomous; 1 = selected this option, 0 = otherwise). | | byr4647 | Indicator for birth cohort in this extract: 1 = born in 1946–1947, 0 = born in 1948–1949 (no other birth years included in this file). | | privpubins3r | Health insurance type (recoded dichotomy used in the study): 1 = has any public insurance (including public-only or public + private), 0 = private insurance only. (Respondents with other/none/unknown coverage may appear as missing, depending on CCES source coding.) | | con | Ideology: conservative (binary indicator; 1 = self-identifies as conservative, 0 = otherwise). | | mod | Ideology: moderate (binary indicator; 1 = self-identifies as moderate, 0 = otherwise). | | ideostrength | Strength of ideological identification (ordinal measure; higher values indicate stronger ideology). | | rep | Party identification: Republican (binary indicator; 1 = Republican, 0 = otherwise). | | ind | Party identification: Independent (binary indicator; 1 = Independent, 0 = otherwise). | | employed | Employment status (binary indicator; 1 = employed full-time or part-time, 0 = otherwise). | | healthcaresupport | Occupation indicator: works in “healthcare support” occupation category (binary indicator; 1 = yes, 0 = no). | | hcsocial | Industry indicator: works in “healthcare or social assistance” industry category (binary indicator; 1 = yes, 0 = no). | | fininsur | Industry indicator: works in “finance or insurance” industry category (binary indicator; 1 = yes, 0 = no). | | hispanic2 | Hispanic ethnicity (binary indicator; 1 = Hispanic, 0 = not Hispanic). | | black | Race: Black/African American (binary indicator; 1 = Black, 0 = not Black). | | military | Military household affiliation (binary indicator; 1 = respondent or household member is affiliated with the military, 0 = otherwise). | | mobility | Residential mobility (binary indicator; 1 = recently moved, 0 = otherwise; exact reference period follows CCES coding). | | married | Marital status (binary indicator; 1 = married, 0 = not married). | | homeowner | Homeownership (binary indicator; 1 = owns home, 0 = does not own). | | labor | Labor union household indicator (binary indicator; 1 = respondent or someone in household belongs to a labor union, 0 = otherwise). | | publicemp | Public-sector employment (binary indicator; 1 = public employee, 0 = otherwise). | | vote08 | Self-reported voting in the 2008 election (binary indicator; 1 = voted, 0 = did not vote). | | bornagain | Religious identification as “born again” (binary indicator; 1 = yes, 0 = no). | | religimp | Importance of religion (ordinal scale; higher values indicate religion is more important to the respondent). | | male | Gender (binary indicator; 1 = male, 0 = not male). | | fincome | Family income (ordered categorical bins from the CCES; coded 1–16 with higher values indicating higher income; some respondents may have missing/refused). | Dataset Context: This file is an analysis-ready extract derived from the 2012 Cooperative Congressional Election Study (CCES), an online national survey administered by YouGov/Polymetrix. It contains individual-level responses covering demographic characteristics (education, income, sex, race/ethnicity, marital and homeownership status), political identifiers and behavior (party identification, ideology and its strength, voter registration and 2008 turnout, news consumption), work/household indicators (employment, public-employee and military household flags, union household, mobility), religiosity measures, and items about health insurance source and attitudes toward health-related policy questions. Variable codings mix binary indicators and ordered categorical scales; the table above summarizes each column as present in the provided CSV. This extract is restricted to respondents born in 1946–1949 (corresponding to roughly ages 63–66 at the time of the October–November 2012 CCES field period).

   file bb1eb952988798d36682e2840f8a80022fb48ddf3c93591bfca5793089f1ee30 · frame —

   ref_iv (reference)

   file bb1eb952988798d36682e2840f8a80022fb48ddf3c93591bfca5793089f1ee30 · frame —

   1. select {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}
   2. dropna {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}
2. 1Transformação
   ref_iv · select {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}

   5221 → 5221 linhas

   Frame antes → depois: 689c88964b32 → dfea1bcb946f
3. 2Transformação
   ref_iv · dropna {"columns":["suppafford","privpubins3r","byr4647","rep","ind","con","mod","ideostrength","hcsocial","fininsur","healthcaresupport","child18","male","married","labor","mobility","homeowner","religimp","employed","votereg","vote08","black","hispanic2","military","fincome","newsint","publicemp","bornagain"]}

   5221 → 4389 linhas

   Frame antes → depois: dfea1bcb946f → c6be60d3c844
4. 3Turno do modelo
   call_75d477cereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   2SLS by matrices: X = [1, controls, D], Z = [1, controls, byr4647]

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "ref_iv",
       "endog": [
         "privpubins3r"
       ],
       "exog": [
         "rep",
         "ind",
         "con",
         "mod",
         "ideostrength",
         "hcsocial",
         "fininsur",
         "healthcaresupport",
         "child18",
         "male",
         "married",
         "labor",
         "mobility",
         "homeowner",
         "religimp",
         "employed",
         "votereg",
         "vote08",
         "black",
         "hispanic2",
         "military",
         "fincome",
         "newsint",
         "publicemp",
         "bornagain"
       ],
       "instruments": [
         "byr4647"
       ],
       "se": "HC1",
       "y": "suppafford"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
5. 4Ferramenta
   infer.iv_2sls— Variáveis instrumentais por mínimos quadrados em dois estágios (MQ2E)v0.1.0call_85585a75decidido pelo modeloreexecutável

   Motivo: 2SLS by matrices: X = [1, controls, D], Z = [1, controls, byr4647]

   Argumentos

   {
     "cluster": null,
     "dataset": "ref_iv",
     "endog": [
       "privpubins3r"
     ],
     "exog": [
       "rep",
       "ind",
       "con",
       "mod",
       "ideostrength",
       "hcsocial",
       "fininsur",
       "healthcaresupport",
       "child18",
       "male",
       "married",
       "labor",
       "mobility",
       "homeowner",
       "religimp",
       "employed",
       "votereg",
       "vote08",
       "black",
       "hispanic2",
       "military",
       "fincome",
       "newsint",
       "publicemp",
       "bornagain"
     ],
     "instruments": [
       "byr4647"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "suppafford"
   }

   Figuras call_85585a75

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.privpubins3r 2SLS effect of privpubins3r on suppafford	0,0459886	0.04598858983362574	0,02294270.022942665851068515	0,00100931 – 0,0909679

   Diagnósticos

   • first_stage_F.privpubins3r = 1.186,481186.4814767053833 (Wald F of the excluded instruments in the first stage for privpubins3r (heteroskedasticity-robust standard errors (HC1)))
   • partial_r2.privpubins3r = 0,2252660.22526566832883976 (share of the variation the instruments explain beyond the controls)
   • weak_instrument = false (every first-stage F is at least 10)
   • n_obs = 4.3894389
   • se_type = "HC1" (heteroskedasticity-robust standard errors (HC1))
   • n_clusters = — (errors not clustered)

   Derivação matemática e pressupostos

   coef.privpubins3r

   1. Com um instrumento e um regressor endógeno, o MQ2E é a razão entre o coeficiente de byr4647 na forma reduzida (suppafford sobre byr4647 e os controles) e no primeiro estágio (privpubins3r sobre byr4647 e os controles).β = ρ / π→ 0,0459886 (0.04598858983362574)
   2. Erro-padrão robusto à heterocedasticidade (HC1): a raiz da variância estimada do coeficiente.ep = √V_jj→ 0,0229427 (0.022942665851068515)
   3. Intervalo de 95% de confiança: a estimativa mais ou menos o valor crítico da distribuição t de Student com 4362 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 observations; their variance may differ

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 1.1998909964894866,
         "ci_low": 0.926913068832784,
         "coef": 1.0634020326611353,
         "p": 2.359497378462159e-51,
         "se": 0.06961918291453165,
         "t": 15.274554916374505,
         "term": "Intercept"
       },
       {
         "ci_high": -0.495227190530027,
         "ci_low": -0.5760152604390468,
         "coef": -0.535621225484537,
         "p": 1.2150185662878503e-138,
         "se": 0.020603861508470666,
         "t": -25.99615733508654,
         "term": "rep"
       },
       {
         "ci_high": -0.3193219109194207,
         "ci_low": -0.41737871604836413,
         "coef": -0.3683503134838924,
         "p": 5.832571745269853e-48,
         "se": 0.025008009661761674,
         "t": -14.729293473007408,
         "term": "ind"
       },
       {
         "ci_high": -0.2539152786701208,
         "ci_low": -0.3380440911957779,
         "coef": -0.29597968493294935,
         "p": 2.1026790052511775e-42,
         "se": 0.021455870948554532,
         "t": -13.794811016650401,
         "term": "con"
       },
       {
         "ci_high": -0.08950809913534243,
         "ci_low": -0.16548773510375142,
         "coef": -0.12749791711954692,
         "p": 5.271408053407336e-11,
         "se": 0.019377538029068976,
         "t": -6.579675752837253,
         "term": "mod"
       },
       {
         "ci_high": -0.023799983805385613,
         "ci_low": -0.06651587886441755,
         "coef": -0.045157931334901584,
         "p": 0.00003459957081950421,
         "se": 0.010894088533094136,
         "t": -4.145177560997463,
         "term": "ideostrength"
       },
       {
         "ci_high": 0.04330111509311052,
         "ci_low": -0.029954252899757502,
         "coef": 0.00667343109667651,
         "p": 0.7209612823186354,
         "se": 0.018682751779772275,
         "t": 0.35719743939978915,
         "term": "hcsocial"
       },
       {
         "ci_high": 0.06755168298222652,
         "ci_low": -0.02875979705399398,
         "coef": 0.019395942964116272,
         "p": 0.429778505762183,
         "se": 0.024562889032710648,
         "t": 0.7896442042418748,
         "term": "fininsur"
       },
       {
         "ci_high": 0.07848860352225805,
         "ci_low": -0.0300072727010153,
         "coef": 0.024240665410621375,
         "p": 0.3810499709912437,
         "se": 0.027670347991503596,
         "t": 0.8760520618701532,
         "term": "healthcaresupport"
       },
       {
         "ci_high": 0.12063984800406702,
         "ci_low": 0.009957185485303534,
         "coef": 0.06529851674468527,
         "p": 0.020755253235667594,
         "se": 0.02822805709424167,
         "t": 2.3132487130333077,
         "term": "child18"
       },
       {
         "ci_high": 0.004938916397677375,
         "ci_low": -0.038269735559102167,
         "coef": -0.016665409580712394,
         "p": 0.13052475033333047,
         "se": 0.011019759252669051,
         "t": -1.5123206595167615,
         "term": "male"
       },
       {
         "ci_high": 0.029369727192432676,
         "ci_low": -0.020050337160948725,
         "coef": 0.004659695015741976,
         "p": 0.7116218564108542,
         "se": 0.012603892664145614,
         "t": 0.369702848152416,
         "term": "married"
       },
       {
         "ci_high": 0.031911457957140385,
         "ci_low": -0.009288532236595445,
         "coef": 0.011311462860272471,
         "p": 0.28175632439739384,
         "se": 0.010507478307849842,
         "t": 1.0765154615472292,
         "term": "labor"
       },
       {
         "ci_high": 0.011447647618552322,
         "ci_low": -0.011459149112593818,
         "coef": -0.0000057507470207482415,
         "p": 0.9992146312579826,
         "se": 0.005842056481640604,
         "t": -0.0009843703221324009,
         "term": "mobility"
       },
       {
         "ci_high": 0.03975195461408698,
         "ci_low": -0.02275380942450612,
         "coef": 0.008499072594790432,
         "p": 0.5939563440236985,
         "se": 0.015941216409584395,
         "t": 0.5331508196376096,
         "term": "homeowner"
       },
       {
         "ci_high": 0.013461018742039324,
         "ci_low": -0.006526006645719105,
         "coef": 0.003467506048160109,
         "p": 0.4963831490140381,
         "se": 0.005097409846768527,
         "t": 0.6802486267331073,
         "term": "religimp"
       },
       {
         "ci_high": 0.030447759190340443,
         "ci_low": -0.021396970028213037,
         "coef": 0.0045253945810637035,
         "p": 0.7321751008035118,
         "se": 0.013222269351974897,
         "t": 0.3422555130740688,
         "term": "employed"
       },
       {
         "ci_high": 0.04113345080323666,
         "ci_low": -0.15480895577251588,
         "coef": -0.05683775248463961,
         "p": 0.2554403152270909,
         "se": 0.04997235623118302,
         "t": -1.1373838812341721,
         "term": "votereg"
       },
       {
         "ci_high": 0.02898792463357916,
         "ci_low": -0.09673732412912617,
         "coef": -0.033874699747773504,
         "p": 0.29081827482476463,
         "se": 0.03206445724649732,
         "t": -1.0564563587451314,
         "term": "vote08"
       },
       {
         "ci_high": 0.09972560967053624,
         "ci_low": 0.029277983038917058,
         "coef": 0.06450179635472665,
         "p": 0.0003342144680385824,
         "se": 0.017966676816922877,
         "t": 3.5900794015492155,
         "term": "black"
       },
       {
         "ci_high": 0.029475281510118384,
         "ci_low": -0.06600558093961459,
         "coef": -0.018265149714748102,
         "p": 0.45324913333898564,
         "se": 0.024351051694131296,
         "t": -0.7500764215103728,
         "term": "hispanic2"
       },
       {
         "ci_high": 0.027648295974859727,
         "ci_low": -0.019624362742575393,
         "coef": 0.004011966616142166,
         "p": 0.7393228987168285,
         "se": 0.012056227045009358,
         "t": 0.33277132233528306,
         "term": "military"
       },
       {
         "ci_high": 0.004946478130924055,
         "ci_low": -0.00297960798505229,
         "coef": 0.0009834350729358828,
         "p": 0.6266350250532322,
         "se": 0.002021436838653274,
         "t": 0.48650299338121733,
         "term": "fincome"
       },
       {
         "ci_high": 0.05439265333713929,
         "ci_low": 0.015324156191139152,
         "coef": 0.03485840476413922,
         "p": 0.0004725906551866571,
         "se": 0.009963870970636913,
         "t": 3.498480145604595,
         "term": "newsint"
       },
       {
         "ci_high": 0.024319297497587603,
         "ci_low": -0.021328381830185057,
         "coef": 0.0014954578337012746,
         "p": 0.8977941122499721,
         "se": 0.011641798895699327,
         "t": 0.1284559067803277,
         "term": "publicemp"
       },
       {
         "ci_high": 0.006313201045687406,
         "ci_low": -0.046633669284156534,
         "coef": -0.020160234119234562,
         "p": 0.13551483727184058,
         "se": 0.013503354948466975,
         "t": -1.4929796481076236,
         "term": "bornagain"
       },
       {
         "ci_high": 0.09096786937094442,
         "ci_low": 0.0010093102963070547,
         "coef": 0.04598858983362574,
         "p": 0.04507815077608663,
         "se": 0.022942665851068515,
         "t": 2.004500703281781,
         "term": "privpubins3r"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.46375996555239035,
         "ci_low": 0.4138116658017809,
         "coef": 0.43878581567708563,
         "endog": "privpubins3r",
         "instrument": "byr4647",
         "p": 3.367367806798428e-230,
         "se": 0.012738611676254891,
         "t": 34.44534042080849
       }
     ],
     "reduced_form": [
       {
         "ci_high": 0.03987690122823495,
         "ci_low": 0.00048138057573781853,
         "coef": 0.020179140901986383,
         "instrument": "byr4647",
         "p": 0.04466025568095804,
         "se": 0.010047273718659813,
         "t": 2.008419544150534
       }
     ]
   }

   Hashes SHA-256 e metadados de reprodução

   Digests dos dados

   ref_iv c6be60d3c844bbd766dd5bb0d1810d6f2df234c1defba10fd9e06e0b2ed42610

   sha256 do resultado

   74efc30767c964c8635c415fa8efef250c5969f45e22bab410cbe8f65ae3b110

   Resultado mostrado ao modelo
6. 5Turno do modelo
   call_8b0d1e5freferenceFim: 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.privpubins3r = 0,04599. Reference path transcribed from the item's own estimation script.",
       "caveats": [],
       "figure_refs": [
         "call_85585a75:coef.privpubins3r"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
7. 6Guarda
   resposta aceita

   Números encontrados

   0,04599

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
8. 7Resposta

   Estimativa (infer.iv_2sls): coef.privpubins3r = 0,04599. Reference path transcribed from the item's own estimation script.

   Figuras citadas: call_85585a75:coef.privpubins3r · Parada: answered

   Figura da resposta: call_85585a75:coef.privpubins3r = 0,04598860.04598858983362574 (se 0,02294270.022942665851068515)
9. 8Pontuação
   #0resposta com lastropassouobrigatóriaevery number backed
10. 9Pontuação
    #1figura da resposta próximapassouobrigatória0.04598858983 in [0.001159492778, 0.09081768689]?
11. 10Pontuação
    #2figura da resposta próximapassouregistrada0.04598858983 vs 0.045988589833671156
12. 11Pontuação
    #3figura da resposta próximafalhouregistrada0.02294266585 vs 0.022871988293883778

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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