Tarefa crb_papers.q059

Veredito

Pergunta feita ao analista

Did cities whose first Fair Housing Act lawsuit decision occurred in 1997 experience a greater decline in the share of non-Hispanic White residents from 1996 to 1998 than cities without such a decision by 1998?

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

   Did cities whose first Fair Housing Act lawsuit decision occurred in 1997 experience a greater decline in the share of non-Hispanic White residents from 1996 to 1998 than cities without such a decision by 1998?

   ds_0031 (agent)

   | Variable Name | Description | | --- | --- | | geo_id2 | Numeric identifier for each incorporated place, based on Census place/GeoID conventions, used to link the same city across years. | | year | Calendar year of the observation. The released panel covers 1969–2011. Census-derived measures are interpolated between decennial observations. | | pctnhwht_cityi | Share of the city’s population that is non-Hispanic White (0–1). Built from U.S. Census counts with intercensal interpolation to provide annualized series. | | pctrenter_cityi | Share of households in the city that are renters (0–1). Compiled from Census housing tenure tabulations with interpolation between decennial enumerations. | | pctwlth_city2i | Share of city residents classified as “wealthy” (0–1). In the replication data, this flags residents in the top end of the income distribution (e.g., at or above the 90th percentile), based on harmonized Census measures. | | fairhousingimp_v1 | Indicator equal to 1 beginning in the year of the first recorded court decision in a Fair Housing Act lawsuit involving the municipality, and 0 before that decision or if no such decision is recorded. Once 1, it remains 1. It does not separately establish that injunctive relief was awarded, implemented, or still operative. | | logpop | Natural logarithm (base e) of total city population. Population counts derive from the Census with values interpolated between decennial years. | | homevali | Average value of owner-occupied housing units in the city, expressed in constant dollars as provided in the replication files. Constructed from Census housing value data with inflation adjustment for comparability over time. | | pct_vacanti | Share of housing units that are vacant (0–1). Derived from Census housing stock and vacancy tabulations, interpolated between decennial measurements. | | minyear | City-specific year field retained from the source data. Its construction is not documented in the available materials. It is constant within each city and must not be interpreted as the first observed year of this released panel. | Dataset Context: This dataset is a city–year panel for incorporated places in U.S. metropolitan areas from the late 1960s through 2011. It integrates demographic and housing indicators from the U.S. Census of Population and Housing, with values smoothed to annual frequency via interpolation between decennial enumerations. The panel uses stable Census geographic identifiers to consistently track places over time. Housing-related measures include tenure (owner vs. renter shares), vacancy rates, and inflation-adjusted home values, providing a view of local housing market conditions across multiple decades of suburbanization, population growth, and changing development patterns. Demographic composition is recorded in a manner consistent with Census definitions for race and ethnicity used during the period. The legal-event records identify court decisions in Fair Housing Act cases involving municipal governments. Cases were collected from a legal database using housing-law and injunctive-relief search terms, then screened for municipal involvement. The recorded date of a city's first decision determines when fairhousingimp_v1 changes from 0 to 1. Case outcomes were established for only a subset, so the indicator does not identify the date when a remedy was implemented. These records are linked to demographic and housing measures from the U.S. Census of Population and Housing for incorporated cities in metropolitan areas. Together, these sources produce a large, harmonized panel suitable for studying long-run patterns in city demographics and housing markets within the broader policy environment shaped by mid-century federal housing reforms, evolving local land-use processes, and metropolitan growth. The combination of standardized Census measures, consistent geographic linkages, and legally sourced policy indicators facilitates comparisons across places and years.

   file 9cf2b539f2ae691db6984fdc60436eb5ca81449a8dbfcc8103d00549db014b66 · frame —

   ref_adopt (reference)

   file 9cf2b539f2ae691db6984fdc60436eb5ca81449a8dbfcc8103d00549db014b66 · frame —

   1. select {"columns":["geo_id2","year","pctnhwht_cityi","fairhousingimp_v1"]}
   2. collapse {"aggs":[{"column":"pctnhwht_cityi","fn":"mean","name":"pctnhwht_cityi"},{"column":"fairhousingimp_v1","fn":"max","name":"fairhousingimp_v1"}],"by":["geo_id2","year"]}
   3. filter {"query":"fairhousingimp_v1 == 1"}
   4. collapse {"aggs":[{"column":"year","fn":"min","name":"adoption_year"}],"by":["geo_id2"]}

   ref_panel (reference)

   file 9cf2b539f2ae691db6984fdc60436eb5ca81449a8dbfcc8103d00549db014b66 · frame —

   1. select {"columns":["geo_id2","year","pctnhwht_cityi","fairhousingimp_v1"]}
   2. collapse {"aggs":[{"column":"pctnhwht_cityi","fn":"mean","name":"pctnhwht_cityi"},{"column":"fairhousingimp_v1","fn":"max","name":"fairhousingimp_v1"}],"by":["geo_id2","year"]}
   3. filter {"query":"year == 1996"}
   4. dropna {"columns":["pctnhwht_cityi"]}
   5. rename {"map":{"pctnhwht_cityi":"y_pre"}}
   6. select {"columns":["geo_id2","y_pre"]}
   7. merge {"how":"inner","on":["geo_id2"],"with":"ref_post"}
   8. merge {"how":"left","on":["geo_id2"],"with":"ref_adopt"}
   9. filter {"query":"adoption_year == 1997 or adoption_year != adoption_year or adoption_year > 1998"}
   10. derive {"args":{"column":"adoption_year","default":0,"keep_unmapped":false,"mapping":[{"source":1997,"target":1}]},"name":"treated","op":"map"}
   11. derive {"args":{"left":"y_post","right":"y_pre"},"name":"d","op":"diff"}

   ref_post (reference)

   file 9cf2b539f2ae691db6984fdc60436eb5ca81449a8dbfcc8103d00549db014b66 · frame —

   1. select {"columns":["geo_id2","year","pctnhwht_cityi","fairhousingimp_v1"]}
   2. collapse {"aggs":[{"column":"pctnhwht_cityi","fn":"mean","name":"pctnhwht_cityi"},{"column":"fairhousingimp_v1","fn":"max","name":"fairhousingimp_v1"}],"by":["geo_id2","year"]}
   3. filter {"query":"year == 1998"}
   4. dropna {"columns":["pctnhwht_cityi"]}
   5. rename {"map":{"pctnhwht_cityi":"y_post"}}
   6. select {"columns":["geo_id2","y_post"]}
2. 1Transformação
   ref_adopt · select {"columns":["geo_id2","year","pctnhwht_cityi","fairhousingimp_v1"]}

   182809 → 182809 linhas

   Frame antes → depois: 741444a4c336 → 436889f8f7e1
3. 2Transformação
   ref_adopt · collapse {"aggs":[{"column":"pctnhwht_cityi","fn":"mean","name":"pctnhwht_cityi"},{"column":"fairhousingimp_v1","fn":"max","name":"fairhousingimp_v1"}],"by":["geo_id2","year"]}

   182809 → 182809 linhas

   Frame antes → depois: 436889f8f7e1 → 436889f8f7e1
4. 3Transformação
   ref_adopt · filter {"query":"fairhousingimp_v1 == 1"}

   182809 → 2826 linhas

   Frame antes → depois: 436889f8f7e1 → edb65ede1305
5. 4Transformação
   ref_adopt · collapse {"aggs":[{"column":"year","fn":"min","name":"adoption_year"}],"by":["geo_id2"]}

   2826 → 199 linhas

   Frame antes → depois: edb65ede1305 → 1816a139bd30
6. 5Transformação
   ref_post · select {"columns":["geo_id2","year","pctnhwht_cityi","fairhousingimp_v1"]}

   182809 → 182809 linhas

   Frame antes → depois: 741444a4c336 → 436889f8f7e1
7. 6Transformação
   ref_post · collapse {"aggs":[{"column":"pctnhwht_cityi","fn":"mean","name":"pctnhwht_cityi"},{"column":"fairhousingimp_v1","fn":"max","name":"fairhousingimp_v1"}],"by":["geo_id2","year"]}

   182809 → 182809 linhas

   Frame antes → depois: 436889f8f7e1 → 436889f8f7e1
8. 7Transformação
   ref_post · filter {"query":"year == 1998"}

   182809 → 4566 linhas

   Frame antes → depois: 436889f8f7e1 → bcfa51a0bbf9
9. 8Transformação
   ref_post · dropna {"columns":["pctnhwht_cityi"]}

   4566 → 4566 linhas

   Frame antes → depois: bcfa51a0bbf9 → bcfa51a0bbf9
10. 9Transformação
    ref_post · rename {"map":{"pctnhwht_cityi":"y_post"}}

    4566 → 4566 linhas

    Frame antes → depois: bcfa51a0bbf9 → 3468024fd0ee
11. 10Transformação
    ref_post · select {"columns":["geo_id2","y_post"]}

    4566 → 4566 linhas

    Frame antes → depois: 3468024fd0ee → fcc19f4c5b8c
12. 11Transformação
    ref_panel · select {"columns":["geo_id2","year","pctnhwht_cityi","fairhousingimp_v1"]}

    182809 → 182809 linhas

    Frame antes → depois: 741444a4c336 → 436889f8f7e1
13. 12Transformação
    ref_panel · collapse {"aggs":[{"column":"pctnhwht_cityi","fn":"mean","name":"pctnhwht_cityi"},{"column":"fairhousingimp_v1","fn":"max","name":"fairhousingimp_v1"}],"by":["geo_id2","year"]}

    182809 → 182809 linhas

    Frame antes → depois: 436889f8f7e1 → 436889f8f7e1
14. 13Transformação
    ref_panel · filter {"query":"year == 1996"}

    182809 → 4566 linhas

    Frame antes → depois: 436889f8f7e1 → 1609e200526e
15. 14Transformação
    ref_panel · dropna {"columns":["pctnhwht_cityi"]}

    4566 → 4566 linhas

    Frame antes → depois: 1609e200526e → 1609e200526e
16. 15Transformação
    ref_panel · rename {"map":{"pctnhwht_cityi":"y_pre"}}

    4566 → 4566 linhas

    Frame antes → depois: 1609e200526e → c23a06c197da
17. 16Transformação
    ref_panel · select {"columns":["geo_id2","y_pre"]}

    4566 → 4566 linhas

    Frame antes → depois: c23a06c197da → 74dfac6d0f42
18. 17Transformação
    ref_panel · merge {"how":"inner","on":["geo_id2"],"with":"ref_post"}

    4566 → 4566 linhas

    Frame antes → depois: 74dfac6d0f42 → 707e4f655a5d
19. 18Transformação
    ref_panel · merge {"how":"left","on":["geo_id2"],"with":"ref_adopt"}

    4566 → 4566 linhas

    Frame antes → depois: 707e4f655a5d → 54ecca936354
20. 19Transformação
    ref_panel · filter {"query":"adoption_year == 1997 or adoption_year != adoption_year or adoption_year > 1998"}

    4566 → 4487 linhas

    Frame antes → depois: 54ecca936354 → 31c84371ba2e
21. 20Transformação
    ref_panel · derive {"args":{"column":"adoption_year","default":0,"keep_unmapped":false,"mapping":[{"source":1997,"target":1}]},"name":"treated","op":"map"}

    4487 → 4487 linhas

    Frame antes → depois: 31c84371ba2e → cfd5c79c8a8f
22. 21Transformação
    ref_panel · derive {"args":{"left":"y_post","right":"y_pre"},"name":"d","op":"diff"}

    4487 → 4487 linhas

    Frame antes → depois: cfd5c79c8a8f → 85842bbc753e
23. 22Turno do modelo
    call_2ab10cedreferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    mean(d_t) - mean(d_c) with SE sqrt(s2_t/n_t + s2_c/n_c): OLS of d on the cohort dummy, HC2

    Chamadas pedidas

    • infer.ols

      {
        "dataset": "ref_panel",
        "se": "HC2",
        "x": [
          "treated"
        ],
        "y": "d"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
24. 23Ferramenta
    infer.olsv0.1.0call_b061c3ebdecidido pelo modeloreexecutável

    Motivo: mean(d_t) - mean(d_c) with SE sqrt(s2_t/n_t + s2_c/n_c): OLS of d on the cohort dummy, HC2

    Argumentos

    {
      "cluster": null,
      "controls": [],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC2",
      "subset": null,
      "weights": null,
      "x": [
        "treated"
      ],
      "y": "d"
    }

    Figuras call_b061c3eb

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treated Coefficient on treated in the regression of d	-0,00323652	-0.0032365200463175055	0,004876520.004876518769263424	-0,0127969 – 0,00632386

    Diagnósticos

    • n_obs = 4.4874487
    • k = 22
    • r2 = 0,0001859320.00018593190167903728
    • df_resid = 4.4854485
    • se_type = "HC2"
    • n_clusters = —

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": -0.010642388914420507,
          "ci_low": -0.011417554568493523,
          "coef": -0.011029971741457015,
          "p": 0,
          "se": 0.000197696607586157,
          "t": -55.7924178676162,
          "term": "Intercept"
        },
        {
          "ci_high": 0.006323861154781335,
          "ci_low": -0.012796901247416346,
          "coef": -0.0032365200463175055,
          "p": 0.506919795313174,
          "se": 0.004876518769263424,
          "t": -0.6636947788896478,
          "term": "treated"
        }
      ]
    }

    Digests dos dados

    ref_panel 85842bbc753e8b7bc52b67bd8aecb3a7e4ed38b2a11b64c6106b7de9d8da1dc2

    sha256 do resultado

    3488ca32090fea4f31e7c0fdbb4e36e0bbdce0062115e53e9e1c793eeeb14cb5

    Resultado mostrado ao modelo
25. 24Turno do modelo
    call_626ab928referenceFim: 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.treated = -0,003237. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_b061c3eb:coef.treated"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
26. 25Guarda
    resposta aceita

    Números encontrados

    -0,003237

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
27. 26Resposta

    Estimativa (infer.ols): coef.treated = -0,003237. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_b061c3eb:coef.treated · Parada: answered

    Figura da resposta: call_b061c3eb:coef.treated = -0,00323652-0.0032365200463175055 (se 0,004876520.004876518769263424)
28. 27Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
29. 28Pontuação
    #1figura da resposta próximapassouobrigatória-0.003236520046 in [-0.01279449683, 0.006321456741]?
30. 29Pontuação
    #2figura da resposta próximapassouregistrada-0.003236520046 vs -0.003236520046317505
31. 30Pontuação
    #3figura da resposta próximapassouregistrada0.004876518769 vs 0.004876518769263402

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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