Tarefa crb_papers.q004

Veredito

Pergunta feita ao analista

How much did state policy liberalism change, by the governor's first year in office, in states that elected a Democratic governor in 2003 relative to comparable states that remained under Republican governors?

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Verificações

Caminho de referência e caminho do agente

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Trajetória

Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

1. 0Início

   How much did state policy liberalism change, by the governor's first year in office, in states that elected a Democratic governor in 2003 relative to comparable states that remained under Republican governors?

   ds_0002 (agent)

   | Variable Name | Description | | --- | --- | | year | Calendar year of the observation (1936–2014). Each row represents a state-year in the panel. | | Policy | Continuous, standardized index summarizing the ideological orientation of a state’s policy outputs in that year. The score is estimated from nearly 150 statutory and regulatory indicators spanning domains such as social welfare, taxation, labor, civil rights, women’s rights, morality laws, family planning, environment, religion, criminal justice, and drug policy. Higher values indicate a more liberal policy orientation. The scale is standardized to be comparable across states and years (centered roughly at 0 with a typical standard deviation near 1). | | sen_dem_control | Indicator (0/1) that the Democratic Party held a majority of seats in the state senate (upper chamber) in that year, based on official election returns and chamber composition. For Nebraska’s nonpartisan unicameral legislature, this variable is generally coded as missing. | | hs_dem_control | Indicator (0/1) that the Democratic Party held a majority of seats in the state house/lower chamber in that year, based on official election returns and chamber composition. For Nebraska, typically coded as missing due to nonpartisan unicameral structure. | | Policy_L1 | One-year lag of the policy index (value from t−1). Included to capture persistence and gradualism in state policy change over time. | | Policy_L2 | Two-year lag of the policy index (value from t−2). Provides an additional look-back to characterize medium-run policy momentum. | | South11 | Indicator (0/1) for membership in the traditional 11-state U.S. South: AL, AR, FL, GA, LA, MS, NC, SC, TN, TX, VA. Useful for distinguishing long-standing regional political and institutional contexts. | | gov_dem | Indicator (0/1) that the sitting governor was affiliated with the Democratic Party for the relevant portion of the calendar year, as compiled from official administrative records and election data. | | gov_dem_X_hs_dem | Product term (0/1) equal to 1 when both gov_dem = 1 and hs_dem_control = 1 in the same year; 0 otherwise. Encodes concurrent Democratic control of the governorship and the lower chamber. | | gov_dem_X_sen_dem | Product term (0/1) equal to 1 when both gov_dem = 1 and sen_dem_control = 1 in the same year; 0 otherwise. Encodes concurrent Democratic control of the governorship and the upper chamber. | | both_dem | Indicator (0/1) that Democrats controlled both legislative chambers in that year (hs_dem_control = 1 and sen_dem_control = 1). | | full_dem | Indicator (0/1) that Democrats held a “trifecta” in that year: governorship plus majorities in both legislative chambers. | | state | Numeric state identifier for the panel unit. Codes map to U.S. states included in the panel; Nebraska’s unicameral, nonpartisan legislature is handled specially for chamber-control variables. | Dataset Context: This dataset is an annual, state-year panel that tracks the ideological orientation of U.S. state policies alongside the party composition of key state institutions from the mid-1930s through the early 2010s. The central measure is a dynamic policy index that pools information from nearly 150 codings of enacted state statutes and regulatory provisions. The underlying source material includes historical statutory compilations, administrative records, and archival policy datasets, covering areas such as safety-net programs, taxation structures, labor and employment regulations, civil and voting rights, women’s rights, morality and family policy, environmental regulation, religion in public institutions, criminal justice, and drug laws. Because most policy indicators are dichotomous (present/absent) and some are continuous (e.g., benefit levels), the index is estimated using a modeling approach that accommodates mixed data types and allows comparability over time. Scores are standardized so that higher values represent more liberal policy bundles, facilitating interpretation across decades when the national policy environment shifted. Party control variables are constructed from state election returns and official chamber rosters, identifying which party held the governor’s office and whether Democrats held majorities in the state house and senate in each year. Nebraska’s unique nonpartisan unicameral legislature is typically excluded from chamber-majority coding or marked as missing for those fields. The dataset also includes composite indicators for concurrent control across branches (e.g., both chambers, or governorship plus both chambers) and simple product terms that register simultaneous control by party across institutions. Coverage spans 1936–2014, a period that includes major institutional realignments, changes in party competition, and significant waves of policy innovation at the state level. The inclusion of lagged versions of the policy index reflects the empirical regularity that state policy changes are often incremental and path dependent. A regional flag for the traditional 11-state South is provided to facilitate analyses that account for historically distinct political and legal environments in that region. Together, these features make the file a versatile panel for describing long-run patterns in state policy content and the composition of state political institutions.

   file cde09ab5c48545fe73a7e45bba4331903448516e4a49ebe1e1c8b00723f91b7e · frame —

   ref_g0 (reference)

   file cde09ab5c48545fe73a7e45bba4331903448516e4a49ebe1e1c8b00723f91b7e · frame —

   1. select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}
   2. derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}
   3. derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}
   4. dropna {"columns":["year","state","Policy","gov_dem"]}
   5. filter {"query":"year == 2002"}
   6. select {"columns":["state","gov_dem"]}
   7. rename {"map":{"gov_dem":"g0"}}

   ref_g1 (reference)

   file cde09ab5c48545fe73a7e45bba4331903448516e4a49ebe1e1c8b00723f91b7e · frame —

   1. select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}
   2. derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}
   3. derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}
   4. dropna {"columns":["year","state","Policy","gov_dem"]}
   5. filter {"query":"year == 2003"}
   6. select {"columns":["state","gov_dem"]}
   7. rename {"map":{"gov_dem":"g1"}}

   ref_panel (reference)

   file cde09ab5c48545fe73a7e45bba4331903448516e4a49ebe1e1c8b00723f91b7e · frame —

   1. select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}
   2. derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}
   3. derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}
   4. dropna {"columns":["year","state","Policy","gov_dem"]}
   5. filter {"query":"year in [2002, 2003]"}
   6. merge {"how":"inner","on":["state"],"with":"ref_g0"}
   7. merge {"how":"inner","on":["state"],"with":"ref_g1"}
   8. filter {"query":"g0 == 0 and (g1 == 1 or g1 == 0)"}
   9. derive {"args":{"cmp":"eq","column":"year","value":2003},"name":"Post","op":"indicator"}
   10. derive {"args":{"cmp":"eq","column":"g1","value":1},"name":"TreatGroup","op":"indicator"}
   11. derive {"args":{"columns":["TreatGroup","Post"]},"name":"TreatGroup_Post","op":"interaction"}
2. 1Transformação
   ref_g0 · select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}

   3950 → 3950 linhas

   Frame antes → depois: 3dd8fb457073 → 4e40f664002a
3. 2Transformação
   ref_g0 · derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}

   3950 → 3950 linhas

   Frame antes → depois: 4e40f664002a → 9fba4ffcce62
4. 3Transformação
   ref_g0 · derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}

   3950 → 3950 linhas

   Frame antes → depois: 9fba4ffcce62 → 4e40f664002a
5. 4Transformação
   ref_g0 · dropna {"columns":["year","state","Policy","gov_dem"]}

   3950 → 3904 linhas

   Frame antes → depois: 4e40f664002a → af646c5211f2
6. 5Transformação
   ref_g0 · filter {"query":"year == 2002"}

   3904 → 50 linhas

   Frame antes → depois: af646c5211f2 → 413faee12e65
7. 6Transformação
   ref_g0 · select {"columns":["state","gov_dem"]}

   50 → 50 linhas

   Frame antes → depois: 413faee12e65 → ba49ca640d36
8. 7Transformação
   ref_g0 · rename {"map":{"gov_dem":"g0"}}

   50 → 50 linhas

   Frame antes → depois: ba49ca640d36 → 810213d4a231
9. 8Transformação
   ref_g1 · select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}

   3950 → 3950 linhas

   Frame antes → depois: 3dd8fb457073 → 4e40f664002a
10. 9Transformação
    ref_g1 · derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}

    3950 → 3950 linhas

    Frame antes → depois: 4e40f664002a → 9fba4ffcce62
11. 10Transformação
    ref_g1 · derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}

    3950 → 3950 linhas

    Frame antes → depois: 9fba4ffcce62 → 4e40f664002a
12. 11Transformação
    ref_g1 · dropna {"columns":["year","state","Policy","gov_dem"]}

    3950 → 3904 linhas

    Frame antes → depois: 4e40f664002a → af646c5211f2
13. 12Transformação
    ref_g1 · filter {"query":"year == 2003"}

    3904 → 50 linhas

    Frame antes → depois: af646c5211f2 → 8b18ea7507a0
14. 13Transformação
    ref_g1 · select {"columns":["state","gov_dem"]}

    50 → 50 linhas

    Frame antes → depois: 8b18ea7507a0 → 10d083474106
15. 14Transformação
    ref_g1 · rename {"map":{"gov_dem":"g1"}}

    50 → 50 linhas

    Frame antes → depois: 10d083474106 → 5cfce991145f
16. 15Transformação
    ref_panel · select {"columns":["year","state","Policy","gov_dem","Policy_L1","Policy_L2"]}

    3950 → 3950 linhas

    Frame antes → depois: 3dd8fb457073 → 4e40f664002a
17. 16Transformação
    ref_panel · derive {"args":{"cmp":"gt","column":"gov_dem","value":0.5},"name":"gov_dem","op":"indicator"}

    3950 → 3950 linhas

    Frame antes → depois: 4e40f664002a → 9fba4ffcce62
18. 17Transformação
    ref_panel · derive {"args":{"column":"gov_dem","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"gov_dem","op":"map"}

    3950 → 3950 linhas

    Frame antes → depois: 9fba4ffcce62 → 4e40f664002a
19. 18Transformação
    ref_panel · dropna {"columns":["year","state","Policy","gov_dem"]}

    3950 → 3904 linhas

    Frame antes → depois: 4e40f664002a → af646c5211f2
20. 19Transformação
    ref_panel · filter {"query":"year in [2002, 2003]"}

    3904 → 100 linhas

    Frame antes → depois: af646c5211f2 → 51ac67df24c1
21. 20Transformação
    ref_panel · merge {"how":"inner","on":["state"],"with":"ref_g0"}

    100 → 100 linhas

    Frame antes → depois: 51ac67df24c1 → 00dd20a0605b
22. 21Transformação
    ref_panel · merge {"how":"inner","on":["state"],"with":"ref_g1"}

    100 → 100 linhas

    Frame antes → depois: 00dd20a0605b → 4b413e7483b4
23. 22Transformação
    ref_panel · filter {"query":"g0 == 0 and (g1 == 1 or g1 == 0)"}

    100 → 58 linhas

    Frame antes → depois: 4b413e7483b4 → 84ad3d7ec99a
24. 23Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2003},"name":"Post","op":"indicator"}

    58 → 58 linhas

    Frame antes → depois: 84ad3d7ec99a → 805c2f383f1a
25. 24Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"g1","value":1},"name":"TreatGroup","op":"indicator"}

    58 → 58 linhas

    Frame antes → depois: 805c2f383f1a → cef62159f040
26. 25Transformação
    ref_panel · derive {"args":{"columns":["TreatGroup","Post"]},"name":"TreatGroup_Post","op":"interaction"}

    58 → 58 linhas

    Frame antes → depois: cef62159f040 → ad7c6ca485a6
27. 26Turno do modelo
    call_205ac03freferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('Policy ~ TreatGroup*Post + Policy_L1 + Policy_L2'), cluster by state

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "state",
        "controls": [
          "TreatGroup",
          "Post",
          "Policy_L1",
          "Policy_L2"
        ],
        "dataset": "ref_panel",
        "x": [
          "TreatGroup_Post"
        ],
        "y": "Policy"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
28. 27Ferramenta
    infer.olsv0.1.0call_688535e5decidido pelo modeloreexecutável

    Motivo: smf.ols('Policy ~ TreatGroup*Post + Policy_L1 + Policy_L2'), cluster by state

    Argumentos

    {
      "cluster": "state",
      "controls": [
        "TreatGroup",
        "Post",
        "Policy_L1",
        "Policy_L2"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "TreatGroup_Post"
      ],
      "y": "Policy"
    }

    Figuras call_688535e5

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.TreatGroup_Post Coefficient on TreatGroup_Post in the regression of Policy	0,0413059	0.04130590477377361	0,03933980.03933980162408388	-0,039278 – 0,12189

    Diagnósticos

    • n_obs = 5858
    • k = 66
    • r2 = 0,995320.9953196564205142
    • df_resid = 5252
    • se_type = "cluster"
    • n_clusters = 2929

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.025413709743220422,
          "ci_low": -0.038605573277414304,
          "coef": -0.00659593176709694,
          "p": 0.6761781504756229,
          "se": 0.015626601205004483,
          "t": -0.4220963778729162,
          "term": "Intercept"
        },
        {
          "ci_high": 0.1218898353773552,
          "ci_low": -0.03927802582980799,
          "coef": 0.04130590477377361,
          "p": 0.3027069040757272,
          "se": 0.03933980162408388,
          "t": 1.0499774546012475,
          "term": "TreatGroup_Post"
        },
        {
          "ci_high": 0.04188556519523886,
          "ci_low": -0.0429192073808071,
          "coef": -0.0005168210927841233,
          "p": 0.9802583821973239,
          "se": 0.02070017499102307,
          "t": -0.024966991487185505,
          "term": "TreatGroup"
        },
        {
          "ci_high": 0.038685881099714346,
          "ci_low": -0.035730308723504405,
          "coef": 0.00147778618810497,
          "p": 0.9357374772618517,
          "se": 0.01816440401540478,
          "t": 0.08135616158128262,
          "term": "Post"
        },
        {
          "ci_high": 1.465864597374676,
          "ci_low": 0.8702503378025053,
          "coef": 1.1680574675885906,
          "p": 9.49815395470511e-9,
          "se": 0.14538473514844522,
          "t": 8.034251095178215,
          "term": "Policy_L1"
        },
        {
          "ci_high": 0.12951080263600942,
          "ci_low": -0.46024224368704825,
          "coef": -0.16536572052551943,
          "p": 0.2603824808043177,
          "se": 0.1439540593004846,
          "t": -1.1487395446094428,
          "term": "Policy_L2"
        }
      ]
    }

    Digests dos dados

    ref_panel ad7c6ca485a6fa4f9a445a13fa8abbc26ee02c612e6fb503a8d4dd1faa853ff5

    sha256 do resultado

    943b9e975fd44fd38403016f1a63c11629ad44532a9834b3c43202cf4fc5c5a8

    Resultado mostrado ao modelo
29. 28Turno do modelo
    call_f9bdf837referenceFim: 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.TreatGroup_Post = 0,04131. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_688535e5:coef.TreatGroup_Post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
30. 29Guarda
    resposta aceita

    Números encontrados

    0,04131

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
31. 30Resposta

    Estimativa (infer.ols): coef.TreatGroup_Post = 0,04131. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_688535e5:coef.TreatGroup_Post · Parada: answered

    Figura da resposta: call_688535e5:coef.TreatGroup_Post = 0,04130590.04130590477377361 (se 0,03933980.03933980162408388)
32. 31Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
33. 32Pontuação
    #1figura da resposta próximapassouobrigatória0.04130590477 in [-0.03580010641, 0.118411916]?
34. 33Pontuação
    #2figura da resposta próximapassouregistrada0.04130590477 vs 0.04130590477377359
35. 34Pontuação
    #3figura da resposta próximapassouregistrada0.03933980162 vs 0.039339801624083896

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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