Tarefa crb_papers.q011

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Pergunta feita ao analista

By how many percentage points did year-over-year agency budget growth in 1977 differ for agencies that became ideologically misaligned versus those that stayed aligned across 1976-1977?

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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 1977 differ for agencies that became ideologically misaligned versus those that stayed aligned across 1976-1977?

   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_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 == 1976"}
   6. select {"columns":["agency_id","conflict","bgrowth"]}
   7. rename {"map":{"bgrowth":"bgrowth0","conflict":"conflict_pre"}}
   8. merge {"how":"inner","on":["agency_id"],"with":"ref_post"}
   9. filter {"query":"conflict_pre == 0 and (conflict_post == 1 or conflict_post == 0)"}
   10. derive {"args":{"cmp":"eq","column":"conflict_post","value":1},"name":"treat","op":"indicator"}
   11. reshape_long {"id":["agency_id"],"j":"post","stubs":["bgrowth"]}
   12. derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}

   ref_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 == 1977"}
   6. select {"columns":["agency_id","conflict","bgrowth"]}
   7. rename {"map":{"bgrowth":"bgrowth1","conflict":"conflict_post"}}
2. 1Transformação
   ref_post · select {"columns":["year","agency_id","conflict","bgrowth"]}

   7055 → 7055 linhas

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

   7055 → 7055 linhas

   Frame antes → depois: 83f177d8d8ff → ab398002023b
4. 3Transformação
   ref_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
5. 4Transformação
   ref_post · dropna {"columns":["year","agency_id","conflict","bgrowth"]}

   7055 → 6294 linhas

   Frame antes → depois: 83f177d8d8ff → 78926e73e30d
6. 5Transformação
   ref_post · filter {"query":"year == 1977"}

   6294 → 112 linhas

   Frame antes → depois: 78926e73e30d → 1b3a391d7f68
7. 6Transformação
   ref_post · select {"columns":["agency_id","conflict","bgrowth"]}

   112 → 112 linhas

   Frame antes → depois: 1b3a391d7f68 → 63ec31f52624
8. 7Transformação
   ref_post · rename {"map":{"bgrowth":"bgrowth1","conflict":"conflict_post"}}

   112 → 112 linhas

   Frame antes → depois: 63ec31f52624 → 3e6a3a02e553
9. 8Transformação
   ref_panel · select {"columns":["year","agency_id","conflict","bgrowth"]}

   7055 → 7055 linhas

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

    7055 → 7055 linhas

    Frame antes → depois: 83f177d8d8ff → ab398002023b
11. 10Transformação
    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
12. 11Transformação
    ref_panel · dropna {"columns":["year","agency_id","conflict","bgrowth"]}

    7055 → 6294 linhas

    Frame antes → depois: 83f177d8d8ff → 78926e73e30d
13. 12Transformação
    ref_panel · filter {"query":"year == 1976"}

    6294 → 112 linhas

    Frame antes → depois: 78926e73e30d → d63983c29330
14. 13Transformação
    ref_panel · select {"columns":["agency_id","conflict","bgrowth"]}

    112 → 112 linhas

    Frame antes → depois: d63983c29330 → 248a0e6d8a4f
15. 14Transformação
    ref_panel · rename {"map":{"bgrowth":"bgrowth0","conflict":"conflict_pre"}}

    112 → 112 linhas

    Frame antes → depois: 248a0e6d8a4f → 0526d05a0d76
16. 15Transformação
    ref_panel · merge {"how":"inner","on":["agency_id"],"with":"ref_post"}

    112 → 105 linhas

    Frame antes → depois: 0526d05a0d76 → 7fbe2870c889
17. 16Transformação
    ref_panel · filter {"query":"conflict_pre == 0 and (conflict_post == 1 or conflict_post == 0)"}

    105 → 105 linhas

    Frame antes → depois: 7fbe2870c889 → 7fbe2870c889
18. 17Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"conflict_post","value":1},"name":"treat","op":"indicator"}

    105 → 105 linhas

    Frame antes → depois: 7fbe2870c889 → 5ef55e2f7e86
19. 18Transformação
    ref_panel · reshape_long {"id":["agency_id"],"j":"post","stubs":["bgrowth"]}

    105 → 210 linhas

    Frame antes → depois: 5ef55e2f7e86 → b280b9f230d2
20. 19Transformação
    ref_panel · derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}

    210 → 210 linhas

    Frame antes → depois: b280b9f230d2 → 4fce646ccda2
21. 20Turno do modelo
    call_4f277c29referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('bgrowth ~ treat * post', data=long), 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
22. 21Ferramenta
    infer.olsv0.1.0call_4c93dd09decidido pelo modeloreexecutável

    Motivo: smf.ols('bgrowth ~ treat * post', data=long), 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_4c93dd09

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treat_post Coefficient on treat_post in the regression of bgrowth	4,25512	4.255119515187237	6,392616.392608438238797	-8,42166 – 16,9319

    Diagnósticos

    • n_obs = 210210
    • k = 44
    • r2 = 0,05911960.059119550706493995
    • df_resid = 206206
    • se_type = "cluster"
    • n_clusters = 105105

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 12.111783142003246,
          "ci_low": -10.074775811086424,
          "coef": 1.0185036654584108,
          "p": 0.8558837852202841,
          "se": 5.594084493305162,
          "t": 0.1820679803240953,
          "term": "Intercept"
        },
        {
          "ci_high": 16.931901940331294,
          "ci_low": -8.42166290995682,
          "coef": 4.255119515187237,
          "p": 0.5071204985953194,
          "se": 6.392608438238797,
          "t": 0.665631182685037,
          "term": "treat_post"
        },
        {
          "ci_high": 12.19277911175293,
          "ci_low": -11.171327700385703,
          "coef": 0.5107257056836134,
          "p": 0.931079791895564,
          "se": 5.890989580405798,
          "t": 0.08669608029563554,
          "term": "treat"
        },
        {
          "ci_high": 15.988490845478355,
          "ci_low": -7.100561405002354,
          "coef": 4.443964720238,
          "p": 0.4469810741050829,
          "se": 5.8216377507040065,
          "t": 0.7633530134540911,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel 4fce646ccda24a7d3c77cb21b16bb27adbc854525ef7b50a5fc54c582696e351

    sha256 do resultado

    5ffcb95bc471494a177b0f26db67b03e8a8f8648342303633f9efe43da0cf3c4

    Resultado mostrado ao modelo
23. 22Turno do modelo
    call_52d29c9areferenceFim: 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 = 4,255. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_4c93dd09:coef.treat_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
24. 23Guarda
    resposta aceita

    Números encontrados

    4,255

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
25. 24Resposta

    Estimativa (infer.ols): coef.treat_post = 4,255. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_4c93dd09:coef.treat_post · Parada: answered

    Figura da resposta: call_4c93dd09:coef.treat_post = 4,255124.255119515187237 (se 6,392616.392608438238797)
26. 25Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
27. 26Pontuação
    #1figura da resposta próximapassouobrigatória4.255119515 in [-8.274393024, 16.78463205]?
28. 27Pontuação
    #2figura da resposta próximapassouregistrada4.255119515 vs 4.255119515187162
29. 28Pontuação
    #3figura da resposta próximapassouregistrada6.392608438 vs 6.392608438238792

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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