Tarefa crb_papers.q006

Veredito

Pergunta feita ao analista

How much did logged municipal property tax (IPTU) revenue change after municipalities that updated the cadastre in 2013, relative to municipalities that had not yet updated by 2013?

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

   How much did logged municipal property tax (IPTU) revenue change after municipalities that updated the cadastre in 2013, relative to municipalities that had not yet updated by 2013?

   ds_0003 (agent)

   | Variable Name | Description | | --- | --- | | c6_ibge | Six-digit IBGE municipality identifier used in Brazilian statistical and administrative systems to uniquely identify each locality. | | codigouf | Numeric code of the state (Unidade da Federação) in which the municipality is located. | | year | Calendar year of observation (primarily 2004–2015). | | term | Start year of the mayoral electoral term associated with the record when available (four-year cycles in Brazil). | | cad_update | Indicator equal to 1 in and after years when the municipality reports having completed a comprehensive cadastre (land-parcel register) update; 0 otherwise. | | planta_update | Indicator equal to 1 in years when the municipality reports revising the generic land valuation schedule (Planta Genérica de Valores) used to assess property values for taxation; 0 otherwise. | | iptu_law_update | Indicator equal to 1 in years when the municipality reports reforming or updating its local property tax (IPTU) law or regulations; 0 otherwise. | | logiptu | Natural log of annual IPTU (urban property tax) revenue collected by the municipality, in Brazilian reais (R$). | | logissqn | Natural log of annual ISSQN (services tax) revenue collected by the municipality, in Brazilian reais (R$). | | Llogtransfers | One-year lag of the natural log of total intergovernmental transfers received by the municipality (federal and state transfers), in Brazilian reais (R$). | | pre_2004_cad_update_yr | Year of the last comprehensive municipal cadastre update reported prior to 2004. | | t | Integer index for years relative to the last pre-2004 cadastre update (e.g., 0 in the update year, positive thereafter, negative before). | | switchcadaster | Binary flag tied to cadastre timing that marks the reference update year used to construct relative-year indicators. | | F3switchcadaster | Indicator equal to 1 three years before the referenced cadastre update; 0 otherwise. | | F2switchcadaster | Indicator equal to 1 two years before the referenced cadastre update; 0 otherwise. | | Lswitchcadaster | Indicator equal to 1 one year after the referenced cadastre update; 0 otherwise. | | L2switchcadaster | Indicator equal to 1 two years after the referenced cadastre update; 0 otherwise. | | L3cad_update | Indicator equal to 1 three years after the referenced cadastre update; 0 otherwise. | | F4cad_update | Indicator equal to 1 four years before the referenced cadastre update; 0 otherwise. | | anyswitch_period | Municipality-level flag equal to 1 for places that experience a change in cadastre-update status within the analysis window; 0 for those that remain in the same status throughout. | | loggdppc | Natural log of municipal GDP per capita, in Brazilian reais (R$), based on national accounts statistics. | | logdespobras | Natural log of annual municipal expenditures on public works (obras), in Brazilian reais (R$). | | id_c6_electoral_term | Concatenated identifier for municipality-by-electoral-term (c6_ibge + term start year), allowing records to be grouped by mayoral administration. | | yr_adm | Year index within the current mayoral administration (typically 1–4), counting elapsed years since the start of the term; resets at the beginning of a new administration. | Dataset Context: This municipality–year panel brings together administrative and survey-based information to follow local public finance and land administration practices across Brazil during the mid-2000s to mid-2010s. Fiscal data on locally collected taxes and expenditures are drawn from Brazil’s Treasury systems—FINBRA for earlier years and SICONFI for later years—harmonized to annual totals and recorded in Brazilian reais. These sources capture the main municipal own-revenue streams, notably the urban property tax (IPTU) and the services tax (ISSQN), as well as the flows of intergovernmental transfers from federal and state governments. Continuous variables appearing in logarithms are natural logs of annual amounts. Information on land administration comes from IBGE’s Pesquisa de Informações Básicas Municipais (MUNIC), an institutional survey fielded to all municipalities. MUNIC asks local governments about the status and timing of their real estate cadastre—an official register of parcels, ownership, and fiscal valuation—and related legal or administrative changes. The dataset records whether and when municipalities report: conducting a comprehensive cadastre update; revising the generic valuation schedule (Planta Genérica de Valores) used to appraise taxable values; and reforming local property tax rules. Because the cadastre underpins assessment and billing for the IPTU, these administrative markers help track when municipalities refresh the underlying property records and valuation parameters. Socioeconomic indicators such as GDP per capita are taken from IBGE national accounts and municipal statistics, allowing fiscal figures to be interpreted in light of local economic conditions. Additional budget aggregates, like spending on public works, come from the same Treasury systems as revenue data, providing a view of how local governments allocate resources. The panel further includes identifiers that align fiscal and administrative observations with mayoral terms. Brazil’s municipalities elect mayors to four-year administrations; the dataset includes a composite municipality-by-term key and an index of years elapsed within each administration to facilitate analyses organized by political cycles. Finally, a small set of variables encodes the calendar position of observations relative to reported cadastre updates (e.g., indicators for specific years before or after an update). These timing markers are constructed from MUNIC responses on the last update year and allow users to describe patterns around the moments when registers are overhauled. Overall, the dataset enables longitudinal tracking of land administration practices, revenue collection, and budgetary activity across thousands of municipalities under a common institutional framework.

   file 782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e · frame —

   ref_f0 (reference)

   file 782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e · frame —

   1. select {"columns":["c6_ibge","year","cad_update","logiptu"]}
   2. derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}
   3. dropna {"columns":["logiptu"]}
   4. filter {"query":"cad_update == 0"}
   5. collapse {"aggs":[{"column":"year","fn":"min","name":"f0"}],"by":["c6_ibge"]}

   ref_f1 (reference)

   file 782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e · frame —

   1. select {"columns":["c6_ibge","year","cad_update","logiptu"]}
   2. derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}
   3. dropna {"columns":["logiptu"]}
   4. filter {"query":"cad_update == 1"}
   5. collapse {"aggs":[{"column":"year","fn":"min","name":"f1"}],"by":["c6_ibge"]}

   ref_panel (reference)

   file 782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e · frame —

   1. select {"columns":["c6_ibge","year","cad_update","logiptu"]}
   2. derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}
   3. dropna {"columns":["logiptu"]}
   4. filter {"query":"year in [2012, 2014]"}
   5. collapse {"aggs":[{"column":"cad_update","fn":"max","name":"cad_update"},{"column":"logiptu","fn":"mean","name":"logiptu"}],"by":["c6_ibge","year"]}
   6. merge {"how":"left","on":["c6_ibge"],"with":"ref_f1"}
   7. merge {"how":"left","on":["c6_ibge"],"with":"ref_f0"}
   8. merge {"how":"left","on":["c6_ibge"],"with":"ref_wpre"}
   9. merge {"how":"left","on":["c6_ibge"],"with":"ref_wpost"}
   10. filter {"query":"(f1 == 2013 and f0 < f1 and w2012 == 0 and w2014 == 1) or ((f1 != f1 or f0 != f0 or f0 > f1 or f1 > 2014) and w2012 == 0 and w2014 == 0)"}
   11. derive {"args":{"cmp":"eq","column":"w2014","value":1},"name":"G","op":"indicator"}
   12. derive {"args":{"cmp":"eq","column":"year","value":2014},"name":"Post","op":"indicator"}
   13. derive {"args":{"columns":["G","Post"]},"name":"DiD","op":"interaction"}

   ref_wpost (reference)

   file 782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e · frame —

   1. select {"columns":["c6_ibge","year","cad_update","logiptu"]}
   2. derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}
   3. dropna {"columns":["logiptu"]}
   4. filter {"query":"year == 2014"}
   5. collapse {"aggs":[{"column":"cad_update","fn":"max","name":"w2014"}],"by":["c6_ibge"]}

   ref_wpre (reference)

   file 782907b47602fe74cee05183b860ba35e3892024f5ffcd11841e2e4b706fa49e · frame —

   1. select {"columns":["c6_ibge","year","cad_update","logiptu"]}
   2. derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}
   3. dropna {"columns":["logiptu"]}
   4. filter {"query":"year == 2012"}
   5. collapse {"aggs":[{"column":"cad_update","fn":"max","name":"w2012"}],"by":["c6_ibge"]}
2. 1Transformação
   ref_f1 · select {"columns":["c6_ibge","year","cad_update","logiptu"]}

   64698 → 64698 linhas

   Frame antes → depois: eca689c33394 → edd36479e520
3. 2Transformação
   ref_f1 · derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}

   64698 → 64698 linhas

   Frame antes → depois: edd36479e520 → 52374a39ed22
4. 3Transformação
   ref_f1 · dropna {"columns":["logiptu"]}

   64698 → 64081 linhas

   Frame antes → depois: 52374a39ed22 → e8b2ea39e30e
5. 4Transformação
   ref_f1 · filter {"query":"cad_update == 1"}

   64081 → 29965 linhas

   Frame antes → depois: e8b2ea39e30e → ed93512a9075
6. 5Transformação
   ref_f1 · collapse {"aggs":[{"column":"year","fn":"min","name":"f1"}],"by":["c6_ibge"]}

   29965 → 5039 linhas

   Frame antes → depois: ed93512a9075 → 7b303113bd19
7. 6Transformação
   ref_f0 · select {"columns":["c6_ibge","year","cad_update","logiptu"]}

   64698 → 64698 linhas

   Frame antes → depois: eca689c33394 → edd36479e520
8. 7Transformação
   ref_f0 · derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}

   64698 → 64698 linhas

   Frame antes → depois: edd36479e520 → 52374a39ed22
9. 8Transformação
   ref_f0 · dropna {"columns":["logiptu"]}

   64698 → 64081 linhas

   Frame antes → depois: 52374a39ed22 → e8b2ea39e30e
10. 9Transformação
    ref_f0 · filter {"query":"cad_update == 0"}

    64081 → 34116 linhas

    Frame antes → depois: e8b2ea39e30e → c2392a8fdf0d
11. 10Transformação
    ref_f0 · collapse {"aggs":[{"column":"year","fn":"min","name":"f0"}],"by":["c6_ibge"]}

    34116 → 4280 linhas

    Frame antes → depois: c2392a8fdf0d → 44074bd49491
12. 11Transformação
    ref_wpre · select {"columns":["c6_ibge","year","cad_update","logiptu"]}

    64698 → 64698 linhas

    Frame antes → depois: eca689c33394 → edd36479e520
13. 12Transformação
    ref_wpre · derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}

    64698 → 64698 linhas

    Frame antes → depois: edd36479e520 → 52374a39ed22
14. 13Transformação
    ref_wpre · dropna {"columns":["logiptu"]}

    64698 → 64081 linhas

    Frame antes → depois: 52374a39ed22 → e8b2ea39e30e
15. 14Transformação
    ref_wpre · filter {"query":"year == 2012"}

    64081 → 5175 linhas

    Frame antes → depois: e8b2ea39e30e → 482708a71e5f
16. 15Transformação
    ref_wpre · collapse {"aggs":[{"column":"cad_update","fn":"max","name":"w2012"}],"by":["c6_ibge"]}

    5175 → 5175 linhas

    Frame antes → depois: 482708a71e5f → 2490b57a5a52
17. 16Transformação
    ref_wpost · select {"columns":["c6_ibge","year","cad_update","logiptu"]}

    64698 → 64698 linhas

    Frame antes → depois: eca689c33394 → edd36479e520
18. 17Transformação
    ref_wpost · derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}

    64698 → 64698 linhas

    Frame antes → depois: edd36479e520 → 52374a39ed22
19. 18Transformação
    ref_wpost · dropna {"columns":["logiptu"]}

    64698 → 64081 linhas

    Frame antes → depois: 52374a39ed22 → e8b2ea39e30e
20. 19Transformação
    ref_wpost · filter {"query":"year == 2014"}

    64081 → 4973 linhas

    Frame antes → depois: e8b2ea39e30e → 18ca762b692f
21. 20Transformação
    ref_wpost · collapse {"aggs":[{"column":"cad_update","fn":"max","name":"w2014"}],"by":["c6_ibge"]}

    4973 → 4973 linhas

    Frame antes → depois: 18ca762b692f → c369f3394e56
22. 21Transformação
    ref_panel · select {"columns":["c6_ibge","year","cad_update","logiptu"]}

    64698 → 64698 linhas

    Frame antes → depois: eca689c33394 → edd36479e520
23. 22Transformação
    ref_panel · derive {"args":{"column":"cad_update","default":0,"keep_unmapped":false,"mapping":[{"source":0,"target":0},{"source":1,"target":1}]},"name":"cad_update","op":"map"}

    64698 → 64698 linhas

    Frame antes → depois: edd36479e520 → 52374a39ed22
24. 23Transformação
    ref_panel · dropna {"columns":["logiptu"]}

    64698 → 64081 linhas

    Frame antes → depois: 52374a39ed22 → e8b2ea39e30e
25. 24Transformação
    ref_panel · filter {"query":"year in [2012, 2014]"}

    64081 → 10148 linhas

    Frame antes → depois: e8b2ea39e30e → 8366ed2508e1
26. 25Transformação
    ref_panel · collapse {"aggs":[{"column":"cad_update","fn":"max","name":"cad_update"},{"column":"logiptu","fn":"mean","name":"logiptu"}],"by":["c6_ibge","year"]}

    10148 → 10148 linhas

    Frame antes → depois: 8366ed2508e1 → 8366ed2508e1
27. 26Transformação
    ref_panel · merge {"how":"left","on":["c6_ibge"],"with":"ref_f1"}

    10148 → 10148 linhas

    Frame antes → depois: 8366ed2508e1 → 1bdedbb9086f
28. 27Transformação
    ref_panel · merge {"how":"left","on":["c6_ibge"],"with":"ref_f0"}

    10148 → 10148 linhas

    Frame antes → depois: 1bdedbb9086f → 34170a6d8090
29. 28Transformação
    ref_panel · merge {"how":"left","on":["c6_ibge"],"with":"ref_wpre"}

    10148 → 10148 linhas

    Frame antes → depois: 34170a6d8090 → 97ac5684b7d2
30. 29Transformação
    ref_panel · merge {"how":"left","on":["c6_ibge"],"with":"ref_wpost"}

    10148 → 10148 linhas

    Frame antes → depois: 97ac5684b7d2 → 9fdd773b1d40
31. 30Transformação
    ref_panel · filter {"query":"(f1 == 2013 and f0 < f1 and w2012 == 0 and w2014 == 1) or ((f1 != f1 or f0 != f0 or f0 > f1 or f1 > 2014) and w2012 == 0 and w2014 == 0)"}

    10148 → 2680 linhas

    Frame antes → depois: 9fdd773b1d40 → e266690df3b5
32. 31Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"w2014","value":1},"name":"G","op":"indicator"}

    2680 → 2680 linhas

    Frame antes → depois: e266690df3b5 → 256ef7f86e8a
33. 32Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2014},"name":"Post","op":"indicator"}

    2680 → 2680 linhas

    Frame antes → depois: 256ef7f86e8a → 1a7d62dfd192
34. 33Transformação
    ref_panel · derive {"args":{"columns":["G","Post"]},"name":"DiD","op":"interaction"}

    2680 → 2680 linhas

    Frame antes → depois: 1a7d62dfd192 → b96767f15a56
35. 34Turno do modelo
    call_afdc96a9referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(logiptu ~ const + G + Post + DiD), cluster by c6_ibge

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "c6_ibge",
        "controls": [
          "G",
          "Post"
        ],
        "dataset": "ref_panel",
        "x": [
          "DiD"
        ],
        "y": "logiptu"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
36. 35Ferramenta
    infer.olsv0.1.0call_7f2727b6decidido pelo modeloreexecutável

    Motivo: sm.OLS(logiptu ~ const + G + Post + DiD), cluster by c6_ibge

    Argumentos

    {
      "cluster": "c6_ibge",
      "controls": [
        "G",
        "Post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "DiD"
      ],
      "y": "logiptu"
    }

    Figuras call_7f2727b6

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.DiD Coefficient on DiD in the regression of logiptu	0,0686574	0.06865741478880677	0,094710.09471003768469696	-0,117139 – 0,254454

    Diagnósticos

    • n_obs = 2.6802680
    • k = 44
    • r2 = 0,0192240.019223984724429766
    • df_resid = 2.6762676
    • se_type = "cluster"
    • n_clusters = 1.3401340

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 10.60886819353427,
          "ci_low": 10.195137826858186,
          "coef": 10.402003010196228,
          "p": 0,
          "se": 0.10544999594674581,
          "t": 98.64393940279933,
          "term": "Intercept"
        },
        {
          "ci_high": 0.2544536221162325,
          "ci_low": -0.11713879253861897,
          "coef": 0.06865741478880677,
          "p": 0.4686263796406632,
          "se": 0.09471003768469696,
          "t": 0.7249222623833913,
          "term": "DiD"
        },
        {
          "ci_high": 1.1184235069906698,
          "ci_low": 0.37534365534198116,
          "coef": 0.7468835811663255,
          "p": 0.0000844365261569433,
          "se": 0.18939331906910734,
          "t": 3.9435582249540526,
          "term": "G"
        },
        {
          "ci_high": 0.5784491298877744,
          "ci_low": 0.34679253508228014,
          "coef": 0.4626208324850273,
          "p": 9.472965619791929e-15,
          "se": 0.05904373705883041,
          "t": 7.835222760782874,
          "term": "Post"
        }
      ]
    }

    Digests dos dados

    ref_panel b96767f15a56b578a86283522fa2f6f589e534b3f45aa6c5fc8222afa6c33fce

    sha256 do resultado

    a4610b933baacb69422e775a03ca7ecf997d58865241966cbe8f87f893552fb2

    Resultado mostrado ao modelo
37. 36Turno do modelo
    call_e9f56825referenceFim: 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.DiD = 0,06866. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_7f2727b6:coef.DiD"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
38. 37Guarda
    resposta aceita

    Números encontrados

    0,06866

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
39. 38Resposta

    Estimativa (infer.ols): coef.DiD = 0,06866. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_7f2727b6:coef.DiD · Parada: answered

    Figura da resposta: call_7f2727b6:coef.DiD = 0,06865740.06865741478880677 (se 0,094710.09471003768469696)
40. 39Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
41. 40Pontuação
    #1figura da resposta próximapassouobrigatória0.06865741479 in [-0.1169742591, 0.2542890887]?
42. 41Pontuação
    #2figura da resposta próximapassouregistrada0.06865741479 vs 0.06865741478880348
43. 42Pontuação
    #3figura da resposta próximapassouregistrada0.09471003768 vs 0.0947100376846976

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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