Tarefa crb_papers.q010

Veredito

Pergunta feita ao analista

By how many percentage points did year-over-year agency budget growth in 2007 differ for agencies that became ideologically misaligned versus those that stayed aligned across 2006-2007?

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

   By how many percentage points did year-over-year agency budget growth in 2007 differ for agencies that became ideologically misaligned versus those that stayed aligned across 2006-2007?

   ds_0006 (agent)

   | Variable Name | Description | | --- | --- | | year | Fiscal year label for the observation. Sweden shifted to a calendar-aligned fiscal year in 1997; earlier years follow the pre-reform fiscal calendar but are recorded with a single year identifier. | | agency_id | Stable numeric identifier for each central government agency, used to track agencies over time. | | head_id | Identifier for the individual serving as agency head (Director General) in that year; constant for a person across years and agencies where applicable. | | agency_name | Official agency name as reported in government publications (often in Swedish). | | ministry_name | Name of the supervising ministry responsible for the agency in that year. | | ministry_code | Numeric code corresponding to the supervising ministry in that year. | | inflation | Annual price index factor used to express monetary amounts in constant prices; applied to convert nominal SEK to inflation-adjusted SEK for comparability over time. | | incumbent | Indicator for leadership continuity: 1 if the listed agency head is a holdover continuing in office that year; 0 if newly installed. | | conflict | Indicator of ideological misalignment: 1 if the cabinet that appointed the agency head belongs to a different ideological bloc than the parliamentary majority in that year; 0 if aligned. | | academic | Indicator that the agency head holds a PhD or comparable academic-sector credential that year (1) versus not (0). | | political | Indicator that the agency head has prior experience in political office/positions (1) versus not (0). | | private | Indicator that the agency head has work experience from the private sector (1) versus not (0). | | public | Indicator that the agency head has work experience from the public sector (1) versus not (0). | | council | Indicator that the agency had an advisory council in place that year (1) versus not (0). | | board | Indicator that the agency had a management board in place that year (1) versus not (0). | | creator | Indicator that the agency was originally established by a political bloc opposed to the current parliamentary majority (1) versus not (0). | | budget | Agency’s annual operating appropriation in current (nominal) SEK as recorded in the national budget; excludes programmatic cash transfers. | | i_budget | Agency’s operating appropriation expressed in constant (inflation-adjusted) SEK using the provided price index for intertemporal comparability. | | lni_budget | Natural logarithm of i_budget. | | bgrowth | Year-over-year percentage change in the agency’s inflation-adjusted operating appropriation; may be missing for the first observed year of an agency or after structural breaks. | | bs_agency_id | Technical helper field mirroring the agency identifier; retained from the original processing pipeline. | | odd | Unlabeled technical field carried over from source files; not documented as substantively meaningful in this dataset version. | | lni_budget_control | Natural logarithm of the agency’s inflation-adjusted operating appropriation in the previous fiscal year (lagged real budget). | Dataset Context: This dataset compiles annual records for Sweden’s central executive agencies from 1971 to 2014, combining administrative registers and official publications. Agencies and their life spans were identified from the Swedish Code of Statutes, while annual operating appropriations and organizational details were taken from national budget bills, annexes, and government yearbooks such as the Swedish State Calendar. The resulting panel traces each agency’s supervising ministry, leadership, organizational arrangements (e.g., advisory councils and management boards), and budget in both nominal and constant SEK. Budgets refer to operating appropriations rather than programmatic transfer spending, allowing like-for-like comparisons of agency running costs across time. Monetary amounts are deflated using an annual price index supplied with the data. A major public finance reform in the mid-1990s realigned Sweden’s fiscal year to the calendar year starting in 1997; the dataset reflects the official year labels before and after this change. Leadership information centers on the Director General for each agency, including indicators of sectoral background (academic, political, private, public) and whether the leader is a continuing incumbent or newly installed. Swedish cabinet ministers appoint agency heads on fixed-term contracts, which—together with legislative control over appropriations—creates periods where leaders appointed under one political constellation continue to serve when the parliamentary majority shifts. The data also record whether agencies have advisory councils or management boards in a given year, institutional features that are regularly reported in official yearbooks. Not all agencies appear in every year: the panel includes agencies created, reorganized, or terminated over time. The coverage nonetheless spans hundreds of organizations and thousands of agency-year observations, providing a comprehensive view of the Swedish central administration’s structure and operating appropriations across more than four decades.

   file 487a8169235b77887c54fc38ae45b997c3dc7a8ea1da83811988a925d2640023 · frame —

   ref_c_post (reference)

   file 487a8169235b77887c54fc38ae45b997c3dc7a8ea1da83811988a925d2640023 · frame —

   1. select {"columns":["year","agency_id","conflict","bgrowth"]}
   2. derive {"args":{"cmp":"gt","column":"conflict","value":0},"name":"conflict","op":"indicator"}
   3. derive {"args":{"column":"conflict","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"conflict","op":"map"}
   4. dropna {"columns":["year","agency_id","conflict","bgrowth"]}
   5. filter {"query":"year == 2007"}
   6. select {"columns":["agency_id","conflict"]}
   7. rename {"map":{"conflict":"conflict_post"}}

   ref_c_pre (reference)

   file 487a8169235b77887c54fc38ae45b997c3dc7a8ea1da83811988a925d2640023 · frame —

   1. select {"columns":["year","agency_id","conflict","bgrowth"]}
   2. derive {"args":{"cmp":"gt","column":"conflict","value":0},"name":"conflict","op":"indicator"}
   3. derive {"args":{"column":"conflict","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"conflict","op":"map"}
   4. dropna {"columns":["year","agency_id","conflict","bgrowth"]}
   5. filter {"query":"year == 2006"}
   6. select {"columns":["agency_id","conflict"]}
   7. rename {"map":{"conflict":"conflict_pre"}}

   ref_panel (reference)

   file 487a8169235b77887c54fc38ae45b997c3dc7a8ea1da83811988a925d2640023 · frame —

   1. select {"columns":["year","agency_id","conflict","bgrowth"]}
   2. derive {"args":{"cmp":"gt","column":"conflict","value":0},"name":"conflict","op":"indicator"}
   3. derive {"args":{"column":"conflict","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"conflict","op":"map"}
   4. dropna {"columns":["year","agency_id","conflict","bgrowth"]}
   5. filter {"query":"year in [2006, 2007]"}
   6. merge {"how":"inner","on":["agency_id"],"with":"ref_c_pre"}
   7. merge {"how":"inner","on":["agency_id"],"with":"ref_c_post"}
   8. filter {"query":"conflict_pre == 0 and (conflict_post == 1 or conflict_post == 0)"}
   9. derive {"args":{"cmp":"eq","column":"year","value":2007},"name":"post","op":"indicator"}
   10. derive {"args":{"cmp":"eq","column":"conflict_post","value":1},"name":"treat","op":"indicator"}
   11. derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}
2. 1Transformação
   ref_c_pre · select {"columns":["year","agency_id","conflict","bgrowth"]}

   7055 → 7055 linhas

   Frame antes → depois: 08fe94782681 → 83f177d8d8ff
3. 2Transformação
   ref_c_pre · derive {"args":{"cmp":"gt","column":"conflict","value":0},"name":"conflict","op":"indicator"}

   7055 → 7055 linhas

   Frame antes → depois: 83f177d8d8ff → ab398002023b
4. 3Transformação
   ref_c_pre · derive {"args":{"column":"conflict","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"conflict","op":"map"}

   7055 → 7055 linhas

   Frame antes → depois: ab398002023b → 83f177d8d8ff
5. 4Transformação
   ref_c_pre · dropna {"columns":["year","agency_id","conflict","bgrowth"]}

   7055 → 6294 linhas

   Frame antes → depois: 83f177d8d8ff → 78926e73e30d
6. 5Transformação
   ref_c_pre · filter {"query":"year == 2006"}

   6294 → 177 linhas

   Frame antes → depois: 78926e73e30d → f2b394112e7e
7. 6Transformação
   ref_c_pre · select {"columns":["agency_id","conflict"]}

   177 → 177 linhas

   Frame antes → depois: f2b394112e7e → f19a578f3fa0
8. 7Transformação
   ref_c_pre · rename {"map":{"conflict":"conflict_pre"}}

   177 → 177 linhas

   Frame antes → depois: f19a578f3fa0 → e4a0de73dfeb
9. 8Transformação
   ref_c_post · select {"columns":["year","agency_id","conflict","bgrowth"]}

   7055 → 7055 linhas

   Frame antes → depois: 08fe94782681 → 83f177d8d8ff
10. 9Transformação
    ref_c_post · derive {"args":{"cmp":"gt","column":"conflict","value":0},"name":"conflict","op":"indicator"}

    7055 → 7055 linhas

    Frame antes → depois: 83f177d8d8ff → ab398002023b
11. 10Transformação
    ref_c_post · derive {"args":{"column":"conflict","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"conflict","op":"map"}

    7055 → 7055 linhas

    Frame antes → depois: ab398002023b → 83f177d8d8ff
12. 11Transformação
    ref_c_post · dropna {"columns":["year","agency_id","conflict","bgrowth"]}

    7055 → 6294 linhas

    Frame antes → depois: 83f177d8d8ff → 78926e73e30d
13. 12Transformação
    ref_c_post · filter {"query":"year == 2007"}

    6294 → 181 linhas

    Frame antes → depois: 78926e73e30d → bb064b32817e
14. 13Transformação
    ref_c_post · select {"columns":["agency_id","conflict"]}

    181 → 181 linhas

    Frame antes → depois: bb064b32817e → 639fe625c8bc
15. 14Transformação
    ref_c_post · rename {"map":{"conflict":"conflict_post"}}

    181 → 181 linhas

    Frame antes → depois: 639fe625c8bc → 8f4ac2cf0384
16. 15Transformação
    ref_panel · select {"columns":["year","agency_id","conflict","bgrowth"]}

    7055 → 7055 linhas

    Frame antes → depois: 08fe94782681 → 83f177d8d8ff
17. 16Transformação
    ref_panel · derive {"args":{"cmp":"gt","column":"conflict","value":0},"name":"conflict","op":"indicator"}

    7055 → 7055 linhas

    Frame antes → depois: 83f177d8d8ff → ab398002023b
18. 17Transformação
    ref_panel · derive {"args":{"column":"conflict","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"conflict","op":"map"}

    7055 → 7055 linhas

    Frame antes → depois: ab398002023b → 83f177d8d8ff
19. 18Transformação
    ref_panel · dropna {"columns":["year","agency_id","conflict","bgrowth"]}

    7055 → 6294 linhas

    Frame antes → depois: 83f177d8d8ff → 78926e73e30d
20. 19Transformação
    ref_panel · filter {"query":"year in [2006, 2007]"}

    6294 → 358 linhas

    Frame antes → depois: 78926e73e30d → 49724ffea2ba
21. 20Transformação
    ref_panel · merge {"how":"inner","on":["agency_id"],"with":"ref_c_pre"}

    358 → 353 linhas

    Frame antes → depois: 49724ffea2ba → 73b862f409cf
22. 21Transformação
    ref_panel · merge {"how":"inner","on":["agency_id"],"with":"ref_c_post"}

    353 → 352 linhas

    Frame antes → depois: 73b862f409cf → ba39070e8d0b
23. 22Transformação
    ref_panel · filter {"query":"conflict_pre == 0 and (conflict_post == 1 or conflict_post == 0)"}

    352 → 348 linhas

    Frame antes → depois: ba39070e8d0b → 9ccbebdb5a1b
24. 23Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2007},"name":"post","op":"indicator"}

    348 → 348 linhas

    Frame antes → depois: 9ccbebdb5a1b → 20b613f8fd93
25. 24Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"conflict_post","value":1},"name":"treat","op":"indicator"}

    348 → 348 linhas

    Frame antes → depois: 20b613f8fd93 → 82cc4be2d93b
26. 25Transformação
    ref_panel · derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}

    348 → 348 linhas

    Frame antes → depois: 82cc4be2d93b → 56a7be1ede1d
27. 26Turno do modelo
    call_51404a7dreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('bgrowth ~ treat * post'), cluster by agency_id

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "agency_id",
        "controls": [
          "treat",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "treat_post"
        ],
        "y": "bgrowth"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
28. 27Ferramenta
    infer.olsv0.1.0call_0e78f29ddecidido pelo modeloreexecutável

    Motivo: smf.ols('bgrowth ~ treat * post'), cluster by agency_id

    Argumentos

    {
      "cluster": "agency_id",
      "controls": [
        "treat",
        "post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treat_post"
      ],
      "y": "bgrowth"
    }

    Figuras call_0e78f29d

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treat_post Coefficient on treat_post in the regression of bgrowth	-2,08942	-2.089422718345652	3,149213.149212644779547	-8,30525 – 4,1264

    Diagnósticos

    • n_obs = 348348
    • k = 44
    • r2 = 0,001800930.0018009254756409243
    • df_resid = 344344
    • se_type = "cluster"
    • n_clusters = 174174

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 4.767700183643239,
          "ci_low": -4.086579183808485,
          "coef": 0.34056049991737714,
          "p": 0.8794950992470076,
          "se": 2.242985092035587,
          "t": 0.151833599396912,
          "term": "Intercept"
        },
        {
          "ci_high": 4.126402933683963,
          "ci_low": -8.305248370375267,
          "coef": -2.089422718345652,
          "p": 0.5079097654102221,
          "se": 3.149212644779547,
          "t": -0.6634746376397574,
          "term": "treat_post"
        },
        {
          "ci_high": 6.995877036338452,
          "ci_low": -2.715194416987057,
          "coef": 2.1403413096756974,
          "p": 0.3854803091184354,
          "se": 2.460029505910012,
          "t": 0.8700470073768257,
          "term": "treat"
        },
        {
          "ci_high": 7.705466925377824,
          "ci_low": -3.564883427143725,
          "coef": 2.0702917491170494,
          "p": 0.4693467515770203,
          "se": 2.8550293901556962,
          "t": 0.7251385068943714,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel 56a7be1ede1dce45e41b80d1127b49f6c745e3fa7aa7bd4698e44cc96b016b9f

    sha256 do resultado

    1d27f679af77d5a2b50c13f192c28af424d8f60cb462866bb8e0e7e3a13790cd

    Resultado mostrado ao modelo
29. 28Turno do modelo
    call_72bb9266referenceFim: 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.treat_post = -2,089. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_0e78f29d:coef.treat_post"
        ]
      }

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

    Números encontrados

    -2,089

    Sem lastro

    —

    Não citados

    —

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

    Estimativa (infer.ols): coef.treat_post = -2,089. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_0e78f29d:coef.treat_post · Parada: answered

    Figura da resposta: call_0e78f29d:coef.treat_post = -2,08942-2.089422718345652 (se 3,149213.149212644779547)
32. 31Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
33. 32Pontuação
    #1figura da resposta próximapassouobrigatória-2.089422718 in [-8.261879502, 4.083034065]?
34. 33Pontuação
    #2figura da resposta próximapassouregistrada-2.089422718 vs -2.089422718345667
35. 34Pontuação
    #3figura da resposta próximapassouregistrada3.149212645 vs 3.149212644779541

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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