Tarefa crb_papers.q081

Veredito

Pergunta feita ao analista

What is the causal effect of a proposed wind turbine being located within 3 km of a precinct (prop_3km) on the change in the provincial Liberal Party's vote share between 2007 and 2011 (chng_lib), estimated for comparable precincts in the 2011 selected comparison sample?

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

   What is the causal effect of a proposed wind turbine being located within 3 km of a precinct (prop_3km) on the change in the provincial Liberal Party's vote share between 2007 and 2011 (chng_lib), estimated for comparable precincts in the 2011 selected comparison sample?

   ds_0051 (agent)

   | Variable Name | Description | |-------------------------|-------------------------------------------------------------------------------------------------------------| | X | Original row index or internal identifier for the observation (file artefact). | | poll_id_07 | Polling-division identifier for the 2007 provincial election. | | year | Election year for the record; all 708 records in this released extract have year=2011, with earlier-election measurements retained in separate fields. | | ed_id | Numeric identifier for the provincial electoral district (riding). | | ed_name | Name of the provincial electoral district (riding). | | poll_id_11 | Polling-division identifier for the 2011 provincial election. | | poll_id_03 | Polling-division identifier for the 2003 provincial election. | | master_id | Cross-year master identifier linking the same polling division across elections. | | area_m2_07 | Precinct land area in square meters (2007). | | area_m2_11 | Precinct land area in square meters (2011). | | area_m2_03 | Precinct land area in square meters (2003). | | redist | Indicator for whether the precinct/district experienced redistricting or boundary changes. | | km2_07 | Precinct area in square kilometers (2007). | | km2_11 | Precinct area in square kilometers (2011). | | km2_03 | Precinct area in square kilometers (2003). | | id_count | Count of overlapping census dissemination areas or sub-units matched to the precinct. | | electors | Number of registered electors (voters) in the precinct (year-specific). | | voter_turnout | Number of ballots cast in the precinct (absolute turnout count). | | perc_turnout | Turnout rate in the precinct (proportion of electors who voted). | | votes_lib | Number of votes received by the provincial Liberal Party candidate in the precinct. | | perc_lib | Share of votes for the provincial Liberal Party in the precinct (proportion). | | prop | Indicator (0/1) for presence of at least one proposed wind turbine within the precinct boundary. | | op | Indicator (0/1) for presence of at least one operational wind turbine within the precinct boundary. | | treat_p | Derived analysis flag indicating the precinct was coded as having a proposed turbine (derived indicator). | | treat_o | Derived analysis flag indicating the precinct was coded as having an operational turbine (derived indicator).| | op_1km | Indicator for precinct overlapping a 1 km buffer around an operational turbine. | | treat_o_1km | Derived analysis indicator for being within 1 km of an operational turbine. | | op_2km | Indicator for precinct overlapping a 2 km buffer around an operational turbine. | | treat_o_2km | Derived analysis indicator for being within 2 km of an operational turbine. | | op_3km | Indicator for precinct overlapping a 3 km buffer around an operational turbine. | | treat_o_3km | Derived analysis indicator for being within 3 km of an operational turbine. | | op_4km | Indicator for precinct overlapping a 4 km buffer around an operational turbine. | | treat_o_4km | Derived analysis indicator for being within 4 km of an operational turbine. | | op_5km | Indicator for precinct overlapping a 5 km buffer around an operational turbine. | | treat_o_5km | Derived analysis indicator for being within 5 km of an operational turbine. | | op_6km | Indicator for precinct overlapping a 6 km buffer around an operational turbine. | | treat_o_6km | Derived analysis indicator for being within 6 km of an operational turbine. | | prop_1km | Indicator for precinct overlapping a 1 km buffer around a proposed turbine. | | treat_p_1km | Derived analysis indicator for being within 1 km of a proposed turbine. | | prop_2km | Indicator for precinct overlapping a 2 km buffer around a proposed turbine. | | treat_p_2km | Derived analysis indicator for being within 2 km of a proposed turbine. | | prop_3km | Indicator for precinct overlapping a 3 km buffer around a proposed turbine. | | treat_p_3km | Derived analysis indicator for being within 3 km of a proposed turbine. | | prop_4km | Indicator for precinct overlapping a 4 km buffer around a proposed turbine. | | treat_p_4km | Derived analysis indicator for being within 4 km of a proposed turbine. | | prop_5km | Indicator for precinct overlapping a 5 km buffer around a proposed turbine. | | treat_p_5km | Derived analysis indicator for being within 5 km of a proposed turbine. | | prop_6km | Indicator for precinct overlapping a 6 km buffer around a proposed turbine. | | treat_p_6km | Derived analysis indicator for being within 6 km of a proposed turbine. | | avg_pwr | Average wind power resource value for the precinct calculated from spatial wind rasters (native units). | | count_pwr | Count of wind-power raster cells or points intersecting the precinct used to compute avg_pwr. | | pop | Estimated resident population in the precinct (from census-derived mapping). | | pop_denc | Population density for the precinct (people per unit area). | | p_uni_degree | Proportion of the adult population with a university degree. | | unemploy_rate | Unemployment rate (proportion) in the precinct. | | median_inc | Median household or individual income in the precinct (local currency). | | p_immigrant | Proportion of the population that are immigrants. | | avg_home_val | Average home value or assessed property value in the precinct (currency). | | p_housing_own | Proportion of housing units that are owner-occupied. | | log_pop | Natural logarithm of the precinct population. | | log_pop_denc | Natural logarithm of population density. | | log_median_inc | Natural logarithm of median income. | | avg_home_val_log | Natural logarithm of average home value (contemporaneous measure). | | longitude | Longitude coordinate of the precinct centroid (decimal degrees). | | latitude | Latitude coordinate of the precinct centroid (decimal degrees). | | mindistlake | Distance from the precinct centroid to the nearest Great Lake (units consistent with dataset, e.g., km). | | fed_id | Identifier for the corresponding federal electoral district. | | fed_dist | Federal electoral district name or code. | | votes_lib_fed | Number of votes for the federal Liberal Party candidate in the precinct (federal election). | | turnout_fed | Number of ballots cast in the federal election at the precinct-level. | | electors_fed | Number of registered electors for the federal election in the precinct. | | perc_lib_fed | Share of votes for the federal Liberal Party in the precinct (proportion). | | perc_turnout_fed | Turnout rate in the federal election (proportion). | | log_pop_sq | Squared term of log_pop (quadratic covariate). | | log_pop_denc_sq | Squared term of log_pop_denc. | | log_median_inc_sq | Squared term of log_median_inc. | | avg_home_val_log_sq | Squared term of avg_home_val_log. | | unemploy_rate_sq | Squared term of unemployment rate. | | p_uni_degree_sq | Squared term of p_uni_degree. | | p_immigrant_sq | Squared term of p_immigrant. | | p_housing_own_sq | Squared term of p_housing_own. | | weights_p | Retained observation weight associated with the proposed-turbine sample; equal to 1 for every row of this released extract. | | weights_o | Retained observation weight associated with the operational-turbine sample; equal to 1 for every row of this released extract. | | chng_lib | Provincial Liberal Party vote share in 2011 minus its 2007 share, expressed as a proportion; negative values denote declining support. | | avg_home_val_log_07 | Natural logarithm of average home value measured for 2007. | | avg_pwr_log | Natural logarithm of avg_pwr (logged wind power). | | mindistlake_log | Natural logarithm of mindistlake. | | mindistlake_sq | Squared distance to nearest Great Lake (quadratic term). | | long_sq | Squared term of longitude. | | lat_sq | Squared term of latitude. | | long_lat | Interaction term: longitude multiplied by latitude. | Data Description: This released file is a selected 2011 comparison sample of 708 Ontario polling divisions: 354 with a proposed wind turbine within 3 km (prop_3km=1) and 354 without one within that distance (prop_3km=0). It is not the full multi-year provincial panel. The groups were selected for comparability on recorded pre-existing characteristics; this does not by itself establish that all determinants of turbine proposals or voting are comparable. The source data link Ontario provincial election returns across years, corresponding federal returns, geospatial information on onshore wind projects and wind resources, and census-derived socioeconomic indicators. Turbines were geolocated from project lists and spatially joined to precinct polygons to produce presence flags, buffer indicators (1–6 km), counts, and average wind-power measures from Land Information Ontario rasters. Census dissemination-area data were geographically weighted into precincts to produce demographic and housing variables. Logged and squared transformations and precomputed weight fields are retained; weights_p and weights_o both equal 1 throughout this extract. chng_lib records the change in provincial Liberal vote share from 2007 to 2011.

   file 5e162a6286b73906441536ef428f81ba09456bb80ab810f43eaefaa7a6572a70 · frame —

   ref_iv (reference)

   file 5e162a6286b73906441536ef428f81ba09456bb80ab810f43eaefaa7a6572a70 · frame —

   1. select {"columns":["chng_lib","prop_3km","avg_pwr_log","mindistlake","mindistlake_sq","longitude","long_sq","latitude","lat_sq","long_lat","ed_id"]}
   2. dropna {"columns":["chng_lib","prop_3km","avg_pwr_log","mindistlake","mindistlake_sq","longitude","long_sq","latitude","lat_sq","long_lat","ed_id"]}
   3. dummies {"column":"ed_id","drop_first":true,"prefix":"ed_id"}
2. 1Transformação
   ref_iv · select {"columns":["chng_lib","prop_3km","avg_pwr_log","mindistlake","mindistlake_sq","longitude","long_sq","latitude","lat_sq","long_lat","ed_id"]}

   708 → 708 linhas

   Frame antes → depois: 333f41bca5e8 → ca0f5a2f2a93
3. 2Transformação
   ref_iv · dropna {"columns":["chng_lib","prop_3km","avg_pwr_log","mindistlake","mindistlake_sq","longitude","long_sq","latitude","lat_sq","long_lat","ed_id"]}

   708 → 708 linhas

   Frame antes → depois: ca0f5a2f2a93 → ca0f5a2f2a93
4. 3Transformação
   ref_iv · dummies {"column":"ed_id","drop_first":true,"prefix":"ed_id"}

   708 → 708 linhas

   Frame antes → depois: ca0f5a2f2a93 → a557ee5d40af
5. 4Turno do modelo
   call_9f7862d0referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Texto visível

   IV2SLS.from_formula('chng_lib ~ controls + C(ed_id) + [prop_3km ~ avg_pwr_log]').fit(cov_type='robust')

   Chamadas pedidas

   • infer.iv_2sls

     {
       "dataset": "ref_iv",
       "endog": [
         "prop_3km"
       ],
       "exog": [
         "mindistlake",
         "mindistlake_sq",
         "longitude",
         "long_sq",
         "latitude",
         "lat_sq",
         "long_lat",
         "ed_id_10.0",
         "ed_id_14.0",
         "ed_id_18.0",
         "ed_id_19.0",
         "ed_id_21.0",
         "ed_id_22.0",
         "ed_id_28.0",
         "ed_id_29.0",
         "ed_id_34.0",
         "ed_id_36.0",
         "ed_id_40.0",
         "ed_id_41.0",
         "ed_id_42.0",
         "ed_id_55.0",
         "ed_id_58.0",
         "ed_id_67.0",
         "ed_id_69.0",
         "ed_id_70.0",
         "ed_id_73.0",
         "ed_id_85.0",
         "ed_id_87.0",
         "ed_id_90.0",
         "ed_id_91.0",
         "ed_id_98.0",
         "ed_id_105.0"
       ],
       "instruments": [
         "avg_pwr_log"
       ],
       "se": "HC1",
       "y": "chng_lib"
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
6. 5Ferramenta
   infer.iv_2slsv0.1.0call_bbb9191ddecidido pelo modeloreexecutável

   Motivo: IV2SLS.from_formula('chng_lib ~ controls + C(ed_id) + [prop_3km ~ avg_pwr_log]').fit(cov_type='robust')

   Argumentos

   {
     "cluster": null,
     "dataset": "ref_iv",
     "endog": [
       "prop_3km"
     ],
     "exog": [
       "mindistlake",
       "mindistlake_sq",
       "longitude",
       "long_sq",
       "latitude",
       "lat_sq",
       "long_lat",
       "ed_id_10.0",
       "ed_id_14.0",
       "ed_id_18.0",
       "ed_id_19.0",
       "ed_id_21.0",
       "ed_id_22.0",
       "ed_id_28.0",
       "ed_id_29.0",
       "ed_id_34.0",
       "ed_id_36.0",
       "ed_id_40.0",
       "ed_id_41.0",
       "ed_id_42.0",
       "ed_id_55.0",
       "ed_id_58.0",
       "ed_id_67.0",
       "ed_id_69.0",
       "ed_id_70.0",
       "ed_id_73.0",
       "ed_id_85.0",
       "ed_id_87.0",
       "ed_id_90.0",
       "ed_id_91.0",
       "ed_id_98.0",
       "ed_id_105.0"
     ],
     "instruments": [
       "avg_pwr_log"
     ],
     "level": 0.95,
     "se": "HC1",
     "subset": null,
     "y": "chng_lib"
   }

   Figuras call_bbb9191d

   Chave	Valor	Valor bruto	Erro-padrão	IC
   coef.prop_3km 2SLS effect of prop_3km on chng_lib	-0,0769913	-0.07699133652445038	0,02821330.028213289250702607	-0,132388 – -0,0215948

   Diagnósticos

   • first_stage_F.prop_3km = 65,730665.73062834759601
   • partial_r2.prop_3km = 0,09152570.0915257086977963
   • weak_instrument = false
   • n_obs = 708708
   • se_type = "HC1"
   • n_clusters = —

   Tabelas (3)

   {
     "coefficients": [
       {
         "ci_high": 44.34468585434509,
         "ci_low": -32.031809762488166,
         "coef": 6.156438045928459,
         "p": 0.751690760737905,
         "se": 19.449169501627257,
         "t": 0.3165398936655556,
         "term": "Intercept"
       },
       {
         "ci_high": 0.0023819572376751826,
         "ci_low": 0.0003765362137185791,
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         "se": 0.0005106777039461667,
         "t": 2.7008164152047542,
         "term": "mindistlake"
       },
       {
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         "coef": -0.000016557618224722125,
         "p": 0.001507246743081205,
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         "t": -3.1863630744403335,
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       {
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       {
         "ci_high": 0.24859577000998,
         "ci_low": -0.41803526927119217,
         "coef": -0.08471974963060608,
         "p": 0.6178958853975997,
         "se": 0.1697566767539397,
         "t": -0.4990657878712267,
         "term": "ed_id_91.0"
       },
       {
         "ci_high": 0.4151113496106038,
         "ci_low": 0.052137853192794376,
         "coef": 0.23362460140169908,
         "p": 0.011713109290145384,
         "se": 0.09243070134881076,
         "t": 2.5275649539870657,
         "term": "ed_id_98.0"
       },
       {
         "ci_high": 0.2961440201665707,
         "ci_low": -0.000482922886731324,
         "coef": 0.14783054863991968,
         "p": 0.050748513551467034,
         "se": 0.07553564283881199,
         "t": 1.9570965849245525,
         "term": "ed_id_105.0"
       },
       {
         "ci_high": -0.021594828138515244,
         "ci_low": -0.13238784491038552,
         "coef": -0.07699133652445038,
         "p": 0.006520261324127575,
         "se": 0.028213289250702607,
         "t": -2.7289032427345576,
         "term": "prop_3km"
       }
     ],
     "first_stage": [
       {
         "ci_high": 0.9443389888297522,
         "ci_low": 0.5761109162539269,
         "coef": 0.7602249525418395,
         "endog": "prop_3km",
         "instrument": "avg_pwr_log",
         "p": 2.439653185430453e-15,
         "se": 0.09376877193624861,
         "t": 8.107442774857928
       }
     ],
     "reduced_form": [
       {
         "ci_high": -0.016027654969287278,
         "ci_low": -0.10103381534157858,
         "coef": -0.05853073515543293,
         "instrument": "avg_pwr_log",
         "p": 0.007025752782963525,
         "se": 0.021646701755701124,
         "t": -2.703910083669795
       }
     ]
   }

   Digests dos dados

   ref_iv a557ee5d40af1ac580054761390e77a5bfc59b360c197c1ae49827870a918c5d

   sha256 do resultado

   9a9347b4ee2320f4f69fcae04adabcb753c581438bb4dba9e6eb2590534645c6

   Resultado mostrado ao modelo
7. 6Turno do modelo
   call_ce2c08dareferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

   Chamadas pedidas

   • submit_answer (meta-ferramenta)

     {
       "answer": "Estimativa (infer.iv_2sls): coef.prop_3km = -0,07699. Reference path transcribed from the item's own estimation script.",
       "caveats": [],
       "figure_refs": [
         "call_bbb9191d:coef.prop_3km"
       ]
     }

   Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
8. 7Guarda
   resposta aceita

   Números encontrados

   -0,07699

   Sem lastro

   —

   Não citados

   —

   1 nova(s) tentativa(s) restante(s)
9. 8Resposta

   Estimativa (infer.iv_2sls): coef.prop_3km = -0,07699. Reference path transcribed from the item's own estimation script.

   Figuras citadas: call_bbb9191d:coef.prop_3km · Parada: answered

   Figura da resposta: call_bbb9191d:coef.prop_3km = -0,0769913-0.07699133652445038 (se 0,02821330.028213289250702607)
10. 9Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
11. 10Pontuação
    #1figura da resposta próximapassouobrigatória-0.07699133652 in [-0.1309452699, -0.02303740286]?
12. 11Pontuação
    #2figura da resposta próximapassouregistrada-0.07699133652 vs -0.07699133640460332
13. 12Pontuação
    #3figura da resposta próximafalhouregistrada0.02821328925 vs 0.027527517114442512

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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