Tarefa crb_papers.q049

Veredito

Pergunta feita ao analista

Did cities that newly experienced partisan incongruence with their state lower-house delegation between 2008 and 2009 change their likelihood of reporting state-level lobbying relative to comparable cities that remained aligned?

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 that newly experienced partisan incongruence with their state lower-house delegation between 2008 and 2009 change their likelihood of reporting state-level lobbying relative to comparable cities that remained aligned?

   ds_0026 (agent)

   | Variable Name | Description | | --- | --- | | fips | City identifier formed by concatenating the two-letter state abbreviation and the five-digit Census place code. | | year | Calendar year of the observation. | | cog.id | Census of Governments (CoG) unit code identifying the municipal government. | | abbr | Two-letter U.S. Postal Service state abbreviation. | | place.fips | Five-digit Census place FIPS code for the municipality. | | inflation | Price index factor supplied to convert nominal dollar amounts in this file to constant-dollar terms; values are multiplicative. | | state | State name. | | state.code | Numeric state identifier used in source files. | | state.fips | Two-digit FIPS code for the state. | | city | City name as recorded in the source administrative files. | | place.code | Census place code (numeric), without the state prefix. | | fips.city | City/place name in mixed case for readability (not an identifier). | | county | County or county-equivalent containing the city (as recorded by CoG/ACS). | | pop.orig | Municipal population from the original source (typically CoG/ACS), prior to any imputation. | | imputed.pop | Population value after applying the dataset’s imputation procedure when pop.orig is missing. | | log.pop | Natural logarithm of the population measure used for analysis (typically imputed.pop where available). | | lobby.lag1 | Indicator equal to 1 if the city reported any state-level lobbying activity in the previous year, 0 otherwise. | | lobby | Indicator equal to 1 if the city reported any state-level lobbying activity in the current year, 0 otherwise. | | lobby.lead1 | Indicator equal to 1 if the city reported any state-level lobbying activity in the following year, 0 otherwise. | | ever.lobby | Indicator equal to 1 if the city reports any state-level lobbying in any year of the panel, 0 otherwise. | | lobby.amount | Reported municipal lobbying expenditures from state disclosure files (nominal dollars); available only where states report amounts. | | log.lobby.amount | Natural logarithm of lobbying.amount (where available). | | total.rev.orig | Total municipal revenue (nominal/original dollars) from the Census of Governments. | | total.rev.own.sources.orig | Own-source revenue (nominal/original dollars) from the Census of Governments. | | total.taxes.orig | Total tax revenue (nominal/original dollars) from the Census of Governments. | | property.tax.orig | Property tax revenue (nominal/original dollars). | | total.ig.rev.orig | Total intergovernmental revenue received (nominal/original dollars). | | total.fed.rev.orig | Federal intergovernmental revenue (nominal/original dollars). | | total.state.rev.orig | State intergovernmental revenue (nominal/original dollars). | | tot.chgs.and.misc.rev.orig | Charges for services plus miscellaneous revenue (nominal/original dollars). | | interest.rev.orig | Interest earnings revenue (nominal/original dollars). | | fines.and.forfeits.orig | Revenue from fines and forfeitures (nominal/original dollars). | | total.expenditure.orig | Total municipal expenditures (nominal/original dollars). | | total.current.operating.orig | Current operating expenditures (nominal/original dollars). | | total.capital.outlays.orig | Capital outlay expenditures (nominal/original dollars). | | total.construction.orig | Construction expenditures (nominal/original dollars). | | total.salaries.wages.orig | Salaries and wages (nominal/original dollars). | | general.expenditure.orig | General expenditures (nominal/original dollars) in CoG categories. | | housing.com.total.exp.orig | Housing and community development expenditures (nominal/original dollars). | | libraries.total.exp.orig | Library service expenditures (nominal/original dollars). | | parks.total.exp.orig | Parks and recreation expenditures (nominal/original dollars). | | police.prot.total.exp.orig | Police protection expenditures (nominal/original dollars). | | public.welf.total.exp.orig | Public welfare expenditures (nominal/original dollars). | | total.debt.outstanding.orig | Debt outstanding at fiscal year end (nominal/original dollars). | | mrp.estimate | City-level ideological position estimated via multilevel regression and poststratification (MRP); higher precision for larger cities. | | mrp.sd | Standard error (uncertainty) of the city-level MRP ideology estimate. | | pres.dem.2008 | Democratic presidential vote share in 2008 aggregated to city boundaries. | | pres.dem.tercile | Tercile group of pres.dem.2008 (e.g., lowest/middle/highest third). | | pres.dem.median | Indicator denoting whether pres.dem.2008 is above (1) or below (0) the median across cities. | | mrp.tercile | Tercile group of the city’s MRP ideology estimate. | | log.total.rev | Natural logarithm of total municipal revenue. | | log.own.source | Natural logarithm of own-source revenue. | | log.taxes | Natural logarithm of total tax revenue. | | log.state | Natural logarithm of state intergovernmental revenue. | | log.salaries | Natural logarithm of salaries and wages. | | log.exp | Natural logarithm of total expenditures. | | log.debt | Natural logarithm of total debt outstanding. | | state.by.year | Convenience label combining state and year (e.g., “AK 2006”). | | acs.id | ACS GEOID for the place (state FIPS + place FIPS). | | acs.pop | Population estimate from the American Community Survey (ACS). | | white | Count of residents identifying as White (ACS). | | black | Count of residents identifying as Black (ACS). | | pct.white | Percentage of the population identifying as White (ACS). | | pct.black | Percentage of the population identifying as Black (ACS). | | unemployed | Labor-market indicator from the ACS related to unemployment; definition follows the ACS measure used in construction. | | median.income | Median household income (ACS; nominal dollars). | | mean.income | Mean household income (ACS; nominal dollars). | | poverty | Share of individuals below the federal poverty threshold (ACS, percent). | | median.house.value | Median value of owner-occupied housing units (ACS; nominal dollars). | | bachelors.degree | Share of adults (typically age 25+) with a bachelor’s degree or higher (ACS, percent). | | govparty | Party affiliation of the state governor in that year (e.g., D/R). | | sen.rep.prop | Proportion of state senate seats held by Republicans in that year. | | sen.dem.prop | Proportion of state senate seats held by Democrats in that year. | | house.dem.prop | Proportion of state lower-house seats held by Democrats in that year. | | house.rep.prop | Proportion of state lower-house seats held by Republicans in that year. | | log.income | Natural logarithm of median household income. | | log.income.sq | Square of log.income. | | pct.white.sq | Square of pct.white. | | log.med.house | Natural logarithm of median owner-occupied home value. | | mean.own.source | City-level average of own-source revenue across years (construction as in source files; constant within city). | | num.reps | Count of state lower-house legislators whose districts overlap the city. | | num.D | Number of Democratic state lower-house legislators representing the city. | | num.R | Number of Republican state lower-house legislators representing the city. | | min.np | Minimum Shor–McCarty ideological score among the city’s lower-house delegation. | | max.np | Maximum Shor–McCarty ideological score among the city’s lower-house delegation. | | sd.np | Standard deviation of Shor–McCarty ideological scores in the city’s lower-house delegation. | | mean.np.lower | Mean Shor–McCarty ideological score of the city’s lower-house delegation. | | np.quantile | Qualitative bin of the delegation’s ideological position (e.g., Conservative/Liberal categories based on quantiles). | | prop.R | Share of the city’s lower-house delegation that is Republican. | | prop.D | Share of the city’s lower-house delegation that is Democratic. | | mismatch.lag1 | Indicator for partisan incongruence between the city and its state lower-house delegation in the prior year. | | mismatch | Indicator for partisan incongruence between the city and its state lower-house delegation in the current year. | | mismatch.lead1 | Indicator for partisan incongruence between the city and its state lower-house delegation in the next year. | | mismatch.lead2 | Indicator for partisan incongruence two years ahead. | | median.mismatch | Summary classification indicating whether the city is above/below the panel median in frequency of incongruence. | | scaled.year1 | Rescaled time index used for modeling convenience (e.g., centered or standardized year). | Dataset Context: This file is a city–year panel covering U.S. municipalities, generally those with populations of at least 20,000, from 2006 through 2014. It integrates several administrative and survey-based sources to characterize local finances, demographics, political context, and intergovernmental activity. State lobbying information was compiled from all 50 states’ disclosure systems. States require lobbyists and, in many cases, client organizations to report activity; however, reporting rules, thresholds, and data formats vary. Some states record only whether a client lobbied in a year, while others also report spending amounts. The dataset standardizes these records into annual city-level indicators of any lobbying and, where available, expenditures. Because disclosure regimes differ, the presence of expenditure data varies by state; the binary indicator of any reported activity is more consistently observed. Financial variables come from the U.S. Census Bureau’s Census of Governments and reflect municipal revenues, expenditures, and debt. These amounts are provided in nominal dollars as reported by governments for their fiscal years. An accompanying inflation factor enables conversion to constant dollars if desired. Demographic and socioeconomic measures—population, racial composition, educational attainment, household income, poverty, and housing values—are merged from the American Community Survey to align with city boundaries over time. Political environment measures include the party of the state governor and the partisan composition of each state’s legislative chambers by year. The dataset also maps each city to the state lower-house districts that overlap it and summarizes the size and partisan/ideological makeup of the city’s state legislative delegation using widely used legislator ideology scores. City-level political tendencies are summarized using a presidential vote-share measure from 2008 and a separate public-opinion-based ideology index estimated via multilevel regression and poststratification. Together, these components provide a consistent, longitudinal view of municipal characteristics, finances, and their interactions with state-level political institutions during a period marked by substantial variation in state policies and disclosure practices.

   file 74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55 · frame —

   ref_panel (reference)

   file 74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55 · frame —

   1. select {"columns":["fips","year","lobby","mismatch","log.pop"]}
   2. dropna {"columns":["fips","year","lobby","mismatch","log.pop"]}
   3. filter {"query":"year in [2008, 2009]"}
   4. merge {"how":"inner","on":["fips"],"with":"ref_pre"}
   5. merge {"how":"inner","on":["fips"],"with":"ref_post"}
   6. filter {"query":"m0 == 0 and (m1 == 1 or m1 == 0)"}
   7. derive {"args":{"cmp":"eq","column":"year","value":2009},"name":"post","op":"indicator"}
   8. derive {"args":{"cmp":"eq","column":"m1","value":1},"name":"treat_group","op":"indicator"}
   9. rename {"map":{"log.pop":"log_pop"}}
   10. derive {"args":{"columns":["treat_group","post"]},"name":"post_treat_group","op":"interaction"}

   ref_post (reference)

   file 74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55 · frame —

   1. select {"columns":["fips","year","lobby","mismatch","log.pop"]}
   2. dropna {"columns":["fips","year","lobby","mismatch","log.pop"]}
   3. filter {"query":"year == 2009"}
   4. select {"columns":["fips","mismatch"]}
   5. rename {"map":{"mismatch":"m1"}}

   ref_pre (reference)

   file 74d8cd66b33c0a11414abd8dd1b5f3d63e976b62735d826de8cafe9f2f9e6c55 · frame —

   1. select {"columns":["fips","year","lobby","mismatch","log.pop"]}
   2. dropna {"columns":["fips","year","lobby","mismatch","log.pop"]}
   3. filter {"query":"year == 2008"}
   4. select {"columns":["fips","mismatch"]}
   5. rename {"map":{"mismatch":"m0"}}
2. 1Transformação
   ref_pre · select {"columns":["fips","year","lobby","mismatch","log.pop"]}

   6651 → 6651 linhas

   Frame antes → depois: 5f9377306ffe → eb858284095d
3. 2Transformação
   ref_pre · dropna {"columns":["fips","year","lobby","mismatch","log.pop"]}

   6651 → 6307 linhas

   Frame antes → depois: eb858284095d → 87fb2a7f75a4
4. 3Transformação
   ref_pre · filter {"query":"year == 2008"}

   6307 → 738 linhas

   Frame antes → depois: 87fb2a7f75a4 → 1695cf3425ea
5. 4Transformação
   ref_pre · select {"columns":["fips","mismatch"]}

   738 → 738 linhas

   Frame antes → depois: 1695cf3425ea → 05c97682452d
6. 5Transformação
   ref_pre · rename {"map":{"mismatch":"m0"}}

   738 → 738 linhas

   Frame antes → depois: 05c97682452d → 9a3fd5c8ea8d
7. 6Transformação
   ref_post · select {"columns":["fips","year","lobby","mismatch","log.pop"]}

   6651 → 6651 linhas

   Frame antes → depois: 5f9377306ffe → eb858284095d
8. 7Transformação
   ref_post · dropna {"columns":["fips","year","lobby","mismatch","log.pop"]}

   6651 → 6307 linhas

   Frame antes → depois: eb858284095d → 87fb2a7f75a4
9. 8Transformação
   ref_post · filter {"query":"year == 2009"}

   6307 → 738 linhas

   Frame antes → depois: 87fb2a7f75a4 → 9f17cc0702be
10. 9Transformação
    ref_post · select {"columns":["fips","mismatch"]}

    738 → 738 linhas

    Frame antes → depois: 9f17cc0702be → 7a253ca6ef7c
11. 10Transformação
    ref_post · rename {"map":{"mismatch":"m1"}}

    738 → 738 linhas

    Frame antes → depois: 7a253ca6ef7c → 848326ce5634
12. 11Transformação
    ref_panel · select {"columns":["fips","year","lobby","mismatch","log.pop"]}

    6651 → 6651 linhas

    Frame antes → depois: 5f9377306ffe → eb858284095d
13. 12Transformação
    ref_panel · dropna {"columns":["fips","year","lobby","mismatch","log.pop"]}

    6651 → 6307 linhas

    Frame antes → depois: eb858284095d → 87fb2a7f75a4
14. 13Transformação
    ref_panel · filter {"query":"year in [2008, 2009]"}

    6307 → 1476 linhas

    Frame antes → depois: 87fb2a7f75a4 → 3448d9c4cddc
15. 14Transformação
    ref_panel · merge {"how":"inner","on":["fips"],"with":"ref_pre"}

    1476 → 1476 linhas

    Frame antes → depois: 3448d9c4cddc → f7c772de04dd
16. 15Transformação
    ref_panel · merge {"how":"inner","on":["fips"],"with":"ref_post"}

    1476 → 1476 linhas

    Frame antes → depois: f7c772de04dd → de448a8efcd2
17. 16Transformação
    ref_panel · filter {"query":"m0 == 0 and (m1 == 1 or m1 == 0)"}

    1476 → 1334 linhas

    Frame antes → depois: de448a8efcd2 → 41e95120bf02
18. 17Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2009},"name":"post","op":"indicator"}

    1334 → 1334 linhas

    Frame antes → depois: 41e95120bf02 → b95b76a37780
19. 18Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"m1","value":1},"name":"treat_group","op":"indicator"}

    1334 → 1334 linhas

    Frame antes → depois: b95b76a37780 → 7274f75d24fd
20. 19Transformação
    ref_panel · rename {"map":{"log.pop":"log_pop"}}

    1334 → 1334 linhas

    Frame antes → depois: 7274f75d24fd → f949d9e2299d
21. 20Transformação
    ref_panel · derive {"args":{"columns":["treat_group","post"]},"name":"post_treat_group","op":"interaction"}

    1334 → 1334 linhas

    Frame antes → depois: f949d9e2299d → 9d0f7ef9f70f
22. 21Turno do modelo
    call_88bd239creferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('lobby ~ post + treat_group + post:treat_group + log_pop').fit(cov_type='cluster', groups=fips) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "fips",
        "controls": [
          "post",
          "treat_group",
          "log_pop"
        ],
        "dataset": "ref_panel",
        "x": [
          "post_treat_group"
        ],
        "y": "lobby"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
23. 22Ferramenta
    infer.olsv0.1.0call_97749fb2decidido pelo modeloreexecutável

    Motivo: smf.ols('lobby ~ post + treat_group + post:treat_group + log_pop').fit(cov_type='cluster', groups=fips) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "fips",
      "controls": [
        "post",
        "treat_group",
        "log_pop"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "post_treat_group"
      ],
      "y": "lobby"
    }

    Figuras call_97749fb2

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.post_treat_group Coefficient on post_treat_group in the regression of lobby	0,00504539	0.005045387591304088	0,009229640.009229638576975008	-0,0130773 – 0,0231681

    Diagnósticos

    • n_obs = 1.3341334
    • k = 55
    • r2 = 0,1542050.154205303006957
    • df_resid = 1.3291329
    • se_type = "cluster"
    • n_clusters = 667667

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": -1.7703992083056437,
          "ci_low": -2.6503314881220974,
          "coef": -2.2103653482138705,
          "p": 1.6222489461915962e-21,
          "se": 0.22406870221927194,
          "t": -9.864676888478712,
          "term": "Intercept"
        },
        {
          "ci_high": 0.023168081288552158,
          "ci_low": -0.013077306105943983,
          "coef": 0.005045387591304088,
          "p": 0.5848018826509441,
          "se": 0.009229638576975008,
          "t": 0.5466506135885661,
          "term": "post_treat_group"
        },
        {
          "ci_high": 0.012905113255653581,
          "ci_low": -0.02333059549186754,
          "coef": -0.005212741118106979,
          "p": 0.5723091251135586,
          "se": 0.009227173976099833,
          "t": -0.5649336548339706,
          "term": "post"
        },
        {
          "ci_high": 0.22892666238645332,
          "ci_low": -0.0018415841710116426,
          "coef": 0.11354253910772084,
          "p": 0.053759420225788876,
          "se": 0.05876354658830561,
          "t": 1.9321934379351546,
          "term": "treat_group"
        },
        {
          "ci_high": 0.2809340705893806,
          "ci_low": 0.20166679740033927,
          "coef": 0.24130043399485993,
          "p": 5.617206637684655e-30,
          "se": 0.020184865857670122,
          "t": 11.954522546562641,
          "term": "log_pop"
        }
      ]
    }

    Digests dos dados

    ref_panel 9d0f7ef9f70fa675a90783eb0205cbf75870c6784ba1b3534c16846aa9516a92

    sha256 do resultado

    7dc923efd0e43e356ef50ec1b2a8d00ad2dc17c3ac3e68e8a12f1fc3103e4475

    Resultado mostrado ao modelo
24. 23Turno do modelo
    call_d270269breferenceFim: 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.post_treat_group = 0,005045. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_97749fb2:coef.post_treat_group"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
25. 24Guarda
    resposta aceita

    Números encontrados

    0,005045

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
26. 25Resposta

    Estimativa (infer.ols): coef.post_treat_group = 0,005045. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_97749fb2:coef.post_treat_group · Parada: answered

    Figura da resposta: call_97749fb2:coef.post_treat_group = 0,005045390.005045387591304088 (se 0,009229640.009229638576975008)
27. 26Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
28. 27Pontuação
    #1figura da resposta próximapassouobrigatória0.005045387591 in [-0.01304470402, 0.0231354792]?
29. 28Pontuação
    #2figura da resposta próximapassouregistrada0.005045387591 vs 0.005045387591305012
30. 29Pontuação
    #3figura da resposta próximapassouregistrada0.009229638577 vs 0.009229638576974869

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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