Tarefa crb_papers.q030

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Pergunta feita ao analista

For cities that switched to single-member district elections in 2016, how did the switch affect the natural log of multifamily units permitted between 2015 and 2017 compared to otherwise-similar cities that had not switched by 2017, adjusting for population, homeownership and vacancy rates, median home value and income, and prior minority electoral success?

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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 2016, how did the switch affect the natural log of multifamily units permitted between 2015 and 2017 compared to otherwise-similar cities that had not switched by 2017, 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_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 [2015, 2017]"}
   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 == 2016 or gfirst != gfirst or gfirst > 2017)"}
   6. derive {"args":{"cmp":"eq","column":"gfirst","value":2016},"name":"TreatGroup","op":"indicator"}
   7. derive {"args":{"column":"TreatGroup","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"TreatGroup","op":"map"}
   8. derive {"args":{"cmp":"eq","column":"year","value":2017},"name":"Post","op":"indicator"}
   9. 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 [2015, 2017]"}
   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 [2015, 2017]"}

   400 → 80 linhas

   Frame antes → depois: aeead8c6d201 → 6a9003025e87
7. 6Transformação
   ref_years · collapse {"aggs":[{"column":"treat","fn":"count","name":"n"}],"by":["location","year"]}

   80 → 80 linhas

   Frame antes → depois: 6a9003025e87 → 31d1e87202aa
8. 7Transformação
   ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["location"]}

   80 → 40 linhas

   Frame antes → depois: 31d1e87202aa → 36bb4b806f98
9. 8Transformação
   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
10. 9Transformação
    ref_panel · filter {"query":"year in [2015, 2017]"}

    400 → 80 linhas

    Frame antes → depois: aeead8c6d201 → 6a9003025e87
11. 10Transformação
    ref_panel · merge {"how":"left","on":["location"],"with":"ref_first"}

    80 → 80 linhas

    Frame antes → depois: 6a9003025e87 → ae30ed843613
12. 11Transformação
    ref_panel · merge {"how":"left","on":["location"],"with":"ref_years"}

    80 → 80 linhas

    Frame antes → depois: ae30ed843613 → 1015eb6b31e5
13. 12Transformação
    ref_panel · filter {"query":"nyears == 2 and (gfirst == 2016 or gfirst != gfirst or gfirst > 2017)"}

    80 → 74 linhas

    Frame antes → depois: 1015eb6b31e5 → 73f6e4ad0217
14. 13Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"gfirst","value":2016},"name":"TreatGroup","op":"indicator"}

    74 → 74 linhas

    Frame antes → depois: 73f6e4ad0217 → 5a133da0ec74
15. 14Transformação
    ref_panel · derive {"args":{"column":"TreatGroup","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"TreatGroup","op":"map"}

    74 → 74 linhas

    Frame antes → depois: 5a133da0ec74 → b99030280464
16. 15Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2017},"name":"Post","op":"indicator"}

    74 → 74 linhas

    Frame antes → depois: b99030280464 → 864f099f1484
17. 16Transformação
    ref_panel · derive {"args":{"columns":["TreatGroup","Post"]},"name":"D","op":"interaction"}

    74 → 74 linhas

    Frame antes → depois: 864f099f1484 → b6874e3cf4c2
18. 17Turno do modelo
    call_4ba86c6areferenceFim: 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
19. 18Ferramenta
    infer.olsv0.1.0call_876b0f98decidido 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_876b0f98

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.D Coefficient on D in the regression of l.multi	-0,503106	-0.5031064147781698	1,146981.14697534219182	-2,82928 – 1,82307

    Diagnósticos

    • n_obs = 7474
    • k = 1111
    • r2 = 0,3197410.31974100652832693
    • df_resid = 6363
    • se_type = "cluster"
    • n_clusters = 3737

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.4511329174922487,
          "ci_low": -9.093477995308433,
          "coef": -4.321172538908092,
          "p": 0.0745710468730592,
          "se": 2.3530987489205355,
          "t": -1.8363753501166893,
          "term": "Intercept"
        },
        {
          "ci_high": 1.8230673959935597,
          "ci_low": -2.8292802255498994,
          "coef": -0.5031064147781698,
          "p": 0.663545456947683,
          "se": 1.14697534219182,
          "t": -0.43863751579589777,
          "term": "D"
        },
        {
          "ci_high": 1.6957205583348376,
          "ci_low": -1.4078599732681907,
          "coef": 0.1439302925333234,
          "p": 0.8518483282891693,
          "se": 0.76514711105665,
          "t": 0.18810799969506398,
          "term": "TreatGroup"
        },
        {
          "ci_high": 0.8368997463481249,
          "ci_low": -1.3620602614901456,
          "coef": -0.2625802575710103,
          "p": 0.6310685617031341,
          "se": 0.542124774979665,
          "t": -0.48435391572144915,
          "term": "Post"
        },
        {
          "ci_high": 2.2845274076188558,
          "ci_low": -2.2803108493266464,
          "coef": 0.002108279146104873,
          "p": 0.9985156201544103,
          "se": 1.125401055064189,
          "t": 0.0018733580678797423,
          "term": "mht"
        },
        {
          "ci_high": 0.025043000547671765,
          "ci_low": 0.013893630149238181,
          "coef": 0.019468315348454973,
          "p": 2.558315748900446e-8,
          "se": 0.0027487311714949098,
          "t": 7.082655281224552,
          "term": "population.th"
        },
        {
          "ci_high": 17.31224640577422,
          "ci_low": -4.8754611416567615,
          "coef": 6.2183926320587295,
          "p": 0.263132800005332,
          "se": 5.470088550309967,
          "t": 1.1367992629125463,
          "term": "own_rate"
        },
        {
          "ci_high": 17.77607208863172,
          "ci_low": -24.74429099200315,
          "coef": -3.4841094516857143,
          "p": 0.7415426211770194,
          "se": 10.482838334929019,
          "t": -0.33236317687706723,
          "term": "vacancy_rate"
        },
        {
          "ci_high": 0.01097610378497725,
          "ci_low": -0.00017017970031967585,
          "coef": 0.005402962042328788,
          "p": 0.05702744196639731,
          "se": 0.0027479701335116694,
          "t": 1.9661647615595688,
          "term": "med_value.th"
        },
        {
          "ci_high": 0.07331203270679423,
          "ci_low": -0.1054822257018353,
          "coef": -0.01608509649752053,
          "p": 0.7173121534930934,
          "se": 0.04407938150849868,
          "t": -0.3649120279607203,
          "term": "med_income.th"
        },
        {
          "ci_high": 12.689822523502505,
          "ci_low": -0.7095855058030267,
          "coef": 5.990118508849739,
          "p": 0.07813090891932968,
          "se": 3.303448465117445,
          "t": 1.8132925553711572,
          "term": "prop_min_elected_past12"
        }
      ]
    }

    Digests dos dados

    ref_panel b6874e3cf4c2dbdad0445a09aa4c9c961d32fe1d8ed5eab276dcb2ccd5ff018a

    sha256 do resultado

    dca756e079dc986810af70880cdd9d34f07975c8fa2ee96009598557a7b997a5

    Resultado mostrado ao modelo
20. 19Turno do modelo
    call_b01f68f3referenceFim: 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 = -0,5031. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_876b0f98:coef.D"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
21. 20Guarda
    resposta aceita

    Números encontrados

    -0,5031

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
22. 21Resposta

    Estimativa (infer.ols): coef.D = -0,5031. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_876b0f98:coef.D · Parada: answered

    Figura da resposta: call_876b0f98:coef.D = -0,503106-0.5031064147781698 (se 1,146981.14697534219182)
23. 22Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
24. 23Pontuação
    #1figura da resposta próximapassouobrigatória-0.5031064148 in [-2.751178085, 1.744965256]?
25. 24Pontuação
    #2figura da resposta próximapassouregistrada-0.5031064148 vs -0.5031064147781619
26. 25Pontuação
    #3figura da resposta próximapassouregistrada1.146975342 vs 1.1469753421917865

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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