Tarefa crb_papers.q031

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Pergunta feita ao analista

For cities that switched to single-member district elections in 2018, how did the switch affect the natural log of multifamily units permitted between 2017 and 2019 compared to otherwise-similar cities that had not switched by 2019, adjusting for population, homeownership and vacancy rates, median home value and income, and prior minority electoral success?

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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

   For cities that switched to single-member district elections in 2018, how did the switch affect the natural log of multifamily units permitted between 2017 and 2019 compared to otherwise-similar cities that had not switched by 2019, adjusting for population, homeownership and vacancy rates, median home value and income, and prior minority electoral success?

   ds_0016 (agent)

   | Variable Name | Description | | --- | --- | | l.multi | Natural log of the number of multifamily housing units authorized by building permits in the city and year, computed as log(units + 1) to accommodate zero-permit years. Counts come from the U.S. Census Bureau’s Building Permits Survey and refer to residential structures with two or more units. | | treat | Electoral system indicator for the city-year: 1 if city council seats were elected from single-member districts, 0 if elected at-large. Many California cities transitioned during the 2010s in response to state voting-rights litigation and related commitments. | | seg.low.third | Indicator that the city falls in the lowest tercile of residential segregation within the study sample, based on Theil’s H index calculated from population distributions across neighborhoods. Lower values indicate less segregation. This tercile classification is based on baseline (pretreatment) measures and is constant across years for a city. | | max.high.third | Indicator that the council-dominant group’s population share places the city in the highest tercile of “majority size” within the sample at baseline. Used to summarize where the dominant group constitutes a relatively large share of residents. | | population.th | Total resident population of the city (in thousands), taken from the American Community Survey (ACS) 5-year estimates aligned to each year. | | own_rate | Homeownership rate: share of occupied housing units that are owner-occupied, from ACS 5-year estimates. | | vacancy_rate | Residential vacancy rate: share of housing units that are vacant, from ACS 5-year estimates. | | med_value.th | Median value of owner-occupied housing units (in thousands of dollars), from ACS 5-year estimates. | | med_income.th | Median household income (in thousands of dollars), from ACS 5-year estimates. | | prop_min_elected_past12 | Proportion of city council seats won over the prior 12 years by the study-identified underrepresented minority group in that city. Constructed from administrative election records and updated each year using a rolling 12-year window. | | year | Calendar year of observation (2010–2019). | | location | Municipality name (city identifier). | | mht | Indicator derived from baseline council representation: equals 1 for cities in the highest tercile of “majority control” (dominant group’s past seat share scaled by its population share), summarizing overrepresentation on council. | Dataset Context: This panel assembles city-year observations for a subset of California municipalities during 2010–2019 to study how local political institutions and neighborhood context align with the production of housing. The housing measures come from the U.S. Census Bureau’s Building Permits Survey, an annual administrative program covering jurisdictions that issue residential building permits. The survey reports counts of authorized dwelling units by structure type; multifamily units refer to buildings with two or more dwellings. Because permitting can be zero in some years, logged measures add one unit before transformation. Demographic and housing-market indicators—population, income, home values, homeownership, and vacancy—are drawn from the American Community Survey (ACS) 5-year estimates, which pool multiple years of responses to improve precision for cities. Values are scaled where noted (e.g., thousands of residents or dollars) to ease interpretation. Electoral context is captured using municipal sources and statewide administrative election records that list candidates and winners for city council races. These records are used to construct rolling measures of representation over the prior 12 years for racial and ethnic groups within each city. The dataset also summarizes each city’s baseline residential segregation using Theil’s H index and classifies cities into terciles (low, middle, high). Similarly, tercile-based indicators describe the size of the council-dominant group in the population and the degree to which that group’s past council seat share exceeds its population share. These tercile classifications are based on pretreatment baselines and remain fixed across years for a given city. The municipal institutions captured here reflect a period when many California cities shifted from at-large to single-member district council elections following the California Voting Rights Act and related legal actions. The sample focuses on cities meeting size and demographic thresholds commonly considered in that policy environment. Observations are indexed by city name and year, providing a consistent framework to link building permits, ACS measures, and electoral structure over time.

   file 778e172115c77f17d53cb64fe51310978b03e71a9e6d40a0e605b96ee1dbff3a · frame —

   ref_complete (reference)

   file 778e172115c77f17d53cb64fe51310978b03e71a9e6d40a0e605b96ee1dbff3a · frame —

   1. select {"columns":["l.multi","treat","year","location","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}
   2. filter {"query":"year in [2017, 2019]"}
   3. dropna {"columns":["l.multi","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}
   4. collapse {"aggs":[{"column":"treat","fn":"count","name":"n"}],"by":["location","year"]}
   5. collapse {"aggs":[{"column":"n","fn":"count","name":"ncomplete"}],"by":["location"]}

   ref_first (reference)

   file 778e172115c77f17d53cb64fe51310978b03e71a9e6d40a0e605b96ee1dbff3a · frame —

   1. select {"columns":["l.multi","treat","year","location","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}
   2. filter {"query":"treat == 1"}
   3. collapse {"aggs":[{"column":"year","fn":"min","name":"gfirst"}],"by":["location"]}

   ref_panel (reference)

   file 778e172115c77f17d53cb64fe51310978b03e71a9e6d40a0e605b96ee1dbff3a · frame —

   1. select {"columns":["l.multi","treat","year","location","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}
   2. filter {"query":"year in [2017, 2019]"}
   3. merge {"how":"left","on":["location"],"with":"ref_first"}
   4. merge {"how":"left","on":["location"],"with":"ref_years"}
   5. filter {"query":"nyears == 2 and (gfirst == 2018 or gfirst != gfirst or gfirst > 2019)"}
   6. dropna {"columns":["l.multi","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}
   7. merge {"how":"left","on":["location"],"with":"ref_complete"}
   8. filter {"query":"ncomplete == 2"}
   9. derive {"args":{"cmp":"eq","column":"gfirst","value":2018},"name":"TreatGroup","op":"indicator"}
   10. derive {"args":{"column":"TreatGroup","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"TreatGroup","op":"map"}
   11. derive {"args":{"cmp":"eq","column":"year","value":2019},"name":"Post","op":"indicator"}
   12. derive {"args":{"columns":["TreatGroup","Post"]},"name":"D","op":"interaction"}

   ref_years (reference)

   file 778e172115c77f17d53cb64fe51310978b03e71a9e6d40a0e605b96ee1dbff3a · frame —

   1. select {"columns":["l.multi","treat","year","location","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}
   2. filter {"query":"year in [2017, 2019]"}
   3. collapse {"aggs":[{"column":"treat","fn":"count","name":"n"}],"by":["location","year"]}
   4. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["location"]}
2. 1Transformação
   ref_first · select {"columns":["l.multi","treat","year","location","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}

   400 → 400 linhas

   Frame antes → depois: 3b42b1f43b9a → aeead8c6d201
3. 2Transformação
   ref_first · filter {"query":"treat == 1"}

   400 → 98 linhas

   Frame antes → depois: aeead8c6d201 → 2ee8683fd750
4. 3Transformação
   ref_first · collapse {"aggs":[{"column":"year","fn":"min","name":"gfirst"}],"by":["location"]}

   98 → 32 linhas

   Frame antes → depois: 2ee8683fd750 → adbc30f2dc39
5. 4Transformação
   ref_years · select {"columns":["l.multi","treat","year","location","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}

   400 → 400 linhas

   Frame antes → depois: 3b42b1f43b9a → aeead8c6d201
6. 5Transformação
   ref_years · filter {"query":"year in [2017, 2019]"}

   400 → 80 linhas

   Frame antes → depois: aeead8c6d201 → add82a5b215a
7. 6Transformação
   ref_years · collapse {"aggs":[{"column":"treat","fn":"count","name":"n"}],"by":["location","year"]}

   80 → 80 linhas

   Frame antes → depois: add82a5b215a → 2c66b4069f3a
8. 7Transformação
   ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["location"]}

   80 → 40 linhas

   Frame antes → depois: 2c66b4069f3a → 36bb4b806f98
9. 8Transformação
   ref_complete · select {"columns":["l.multi","treat","year","location","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}

   400 → 400 linhas

   Frame antes → depois: 3b42b1f43b9a → aeead8c6d201
10. 9Transformação
    ref_complete · filter {"query":"year in [2017, 2019]"}

    400 → 80 linhas

    Frame antes → depois: aeead8c6d201 → add82a5b215a
11. 10Transformação
    ref_complete · dropna {"columns":["l.multi","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}

    80 → 80 linhas

    Frame antes → depois: add82a5b215a → add82a5b215a
12. 11Transformação
    ref_complete · collapse {"aggs":[{"column":"treat","fn":"count","name":"n"}],"by":["location","year"]}

    80 → 80 linhas

    Frame antes → depois: add82a5b215a → 2c66b4069f3a
13. 12Transformação
    ref_complete · collapse {"aggs":[{"column":"n","fn":"count","name":"ncomplete"}],"by":["location"]}

    80 → 40 linhas

    Frame antes → depois: 2c66b4069f3a → 391e51afcc1e
14. 13Transformação
    ref_panel · select {"columns":["l.multi","treat","year","location","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}

    400 → 400 linhas

    Frame antes → depois: 3b42b1f43b9a → aeead8c6d201
15. 14Transformação
    ref_panel · filter {"query":"year in [2017, 2019]"}

    400 → 80 linhas

    Frame antes → depois: aeead8c6d201 → add82a5b215a
16. 15Transformação
    ref_panel · merge {"how":"left","on":["location"],"with":"ref_first"}

    80 → 80 linhas

    Frame antes → depois: add82a5b215a → 55531c12a01d
17. 16Transformação
    ref_panel · merge {"how":"left","on":["location"],"with":"ref_years"}

    80 → 80 linhas

    Frame antes → depois: 55531c12a01d → d859844b8f00
18. 17Transformação
    ref_panel · filter {"query":"nyears == 2 and (gfirst == 2018 or gfirst != gfirst or gfirst > 2019)"}

    80 → 48 linhas

    Frame antes → depois: d859844b8f00 → 3563e5488f72
19. 18Transformação
    ref_panel · dropna {"columns":["l.multi","mht","population.th","own_rate","vacancy_rate","med_value.th","med_income.th","prop_min_elected_past12"]}

    48 → 48 linhas

    Frame antes → depois: 3563e5488f72 → 3563e5488f72
20. 19Transformação
    ref_panel · merge {"how":"left","on":["location"],"with":"ref_complete"}

    48 → 48 linhas

    Frame antes → depois: 3563e5488f72 → 8f17838770f6
21. 20Transformação
    ref_panel · filter {"query":"ncomplete == 2"}

    48 → 48 linhas

    Frame antes → depois: 8f17838770f6 → 8f17838770f6
22. 21Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"gfirst","value":2018},"name":"TreatGroup","op":"indicator"}

    48 → 48 linhas

    Frame antes → depois: 8f17838770f6 → f6e2c146ba22
23. 22Transformação
    ref_panel · derive {"args":{"column":"TreatGroup","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"TreatGroup","op":"map"}

    48 → 48 linhas

    Frame antes → depois: f6e2c146ba22 → 418953078b92
24. 23Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2019},"name":"Post","op":"indicator"}

    48 → 48 linhas

    Frame antes → depois: 418953078b92 → aea86c180876
25. 24Transformação
    ref_panel · derive {"args":{"columns":["TreatGroup","Post"]},"name":"D","op":"interaction"}

    48 → 48 linhas

    Frame antes → depois: aea86c180876 → 0f0a6cf2127c
26. 25Turno do modelo
    call_c5d7d23freferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    sm.OLS(l.multi, add_constant([D, TreatGroup, Post] + controls)).fit(cov_type='cluster') (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "location",
        "controls": [
          "TreatGroup",
          "Post",
          "mht",
          "population.th",
          "own_rate",
          "vacancy_rate",
          "med_value.th",
          "med_income.th",
          "prop_min_elected_past12"
        ],
        "dataset": "ref_panel",
        "x": [
          "D"
        ],
        "y": "l.multi"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
27. 26Ferramenta
    infer.olsv0.1.0call_6460e217decidido pelo modeloreexecutável

    Motivo: sm.OLS(l.multi, add_constant([D, TreatGroup, Post] + controls)).fit(cov_type='cluster') (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "location",
      "controls": [
        "TreatGroup",
        "Post",
        "mht",
        "population.th",
        "own_rate",
        "vacancy_rate",
        "med_value.th",
        "med_income.th",
        "prop_min_elected_past12"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "D"
      ],
      "y": "l.multi"
    }

    Figuras call_6460e217

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.D Coefficient on D in the regression of l.multi	-2,92376	-2.923764271688587	1,198031.1980345994350796	-5,40209 – -0,445441

    Diagnósticos

    • n_obs = 4848
    • k = 1111
    • r2 = 0,2987610.2987607503097527
    • df_resid = 3737
    • se_type = "cluster"
    • n_clusters = 2424

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 5.099194810944693,
          "ci_low": -12.342203165186769,
          "coef": -3.6215041771210377,
          "p": 0.3991695566620222,
          "se": 4.215631888110849,
          "t": -0.8590655619942998,
          "term": "Intercept"
        },
        {
          "ci_high": -0.4454408800218732,
          "ci_low": -5.402087663355301,
          "coef": -2.923764271688587,
          "p": 0.022785779925315015,
          "se": 1.1980345994350796,
          "t": -2.4404673062591486,
          "term": "D"
        },
        {
          "ci_high": 4.260597256152204,
          "ci_low": 0.240408666798273,
          "coef": 2.2505029614752385,
          "p": 0.029818378685309926,
          "se": 0.9716901842783833,
          "t": 2.3160704902525633,
          "term": "TreatGroup"
        },
        {
          "ci_high": 3.2471399231044686,
          "ci_low": -0.1416679130142282,
          "coef": 1.5527360050451202,
          "p": 0.07063453117499005,
          "se": 0.8190837911142349,
          "t": 1.895698610923391,
          "term": "Post"
        },
        {
          "ci_high": 4.643294865289551,
          "ci_low": -0.28991105890438185,
          "coef": 2.1766919031925847,
          "p": 0.08093638477447355,
          "se": 1.192368881961721,
          "t": 1.8255188776911266,
          "term": "mht"
        },
        {
          "ci_high": 0.02675404394143739,
          "ci_low": -0.0052731367339704915,
          "coef": 0.010740453603733451,
          "p": 0.17859738674524264,
          "se": 0.007741054032842131,
          "t": 1.3874665592264428,
          "term": "population.th"
        },
        {
          "ci_high": 12.622383527344812,
          "ci_low": -18.67660023649309,
          "coef": -3.027108354574139,
          "p": 0.6927426800430895,
          "se": 7.565046918880321,
          "t": -0.4001440291162361,
          "term": "own_rate"
        },
        {
          "ci_high": 40.98779153501009,
          "ci_low": -15.633624702376256,
          "coef": 12.677083416316915,
          "p": 0.36390246496025547,
          "se": 13.685545629253873,
          "t": 0.9263118738370727,
          "term": "vacancy_rate"
        },
        {
          "ci_high": 0.007357631447255342,
          "ci_low": -0.006065351953587997,
          "coef": 0.0006461397468336725,
          "p": 0.8438899044758221,
          "se": 0.003244370487710685,
          "t": 0.1991572014605539,
          "term": "med_value.th"
        },
        {
          "ci_high": 0.16311732353253888,
          "ci_low": -0.05405710474488472,
          "coef": 0.05453010939382709,
          "p": 0.3096801651072167,
          "se": 0.052491632057329804,
          "t": 1.0388343295226736,
          "term": "med_income.th"
        },
        {
          "ci_high": 13.486385785902257,
          "ci_low": -0.2147742941394819,
          "coef": 6.635805745881387,
          "p": 0.05700783003511419,
          "se": 3.311606524693637,
          "t": 2.0038025944206277,
          "term": "prop_min_elected_past12"
        }
      ]
    }

    Digests dos dados

    ref_panel 0f0a6cf2127c85baa25ac7a257f026fff5ef6412a4b1219bb653313b1e61388b

    sha256 do resultado

    fd4a66b83458dadc2824cac8a23a31e323bd37bbc086f4717388f8556c6d4f62

    Resultado mostrado ao modelo
28. 27Turno do modelo
    call_fdf0839creferenceFim: 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.D = -2,924. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_6460e217:coef.D"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
29. 28Guarda
    resposta aceita

    Números encontrados

    -2,924

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
30. 29Resposta

    Estimativa (infer.ols): coef.D = -2,924. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_6460e217:coef.D · Parada: answered

    Figura da resposta: call_6460e217:coef.D = -2,92376-2.923764271688587 (se 1,198031.1980345994350796)
31. 30Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
32. 31Pontuação
    #1figura da resposta próximapassouobrigatória-2.923764272 in [-5.271912087, -0.5756164568]?
33. 32Pontuação
    #2figura da resposta próximapassouregistrada-2.923764272 vs -2.923764271688611
34. 33Pontuação
    #3figura da resposta próximapassouregistrada1.198034599 vs 1.1980345994350083

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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