Pular para o conteúdo

Tarefa crb_papers.q080

crb_papersagentT2publicépoca 1, tentativa 1Execução: 20261015T170925Z-suite-crb_papers-scripted-2dbcf5

Veredito

passouPassou em todas as verificações obrigatórias.

Família do método (das chamadas de ferramentas): iv (two-stage least squares)

Pergunta feita ao analista

What is the local causal effect of a higher average polling-station size on municipalities' counts of citizens' reports of vote buying? Report the local average effect for municipalities whose mean station size is shifted by exogenous institution-determined changes in the number of stations, controlling for municipal population and potential electorate and clustering standard errors by municipality.

Resposta

Estimativa (infer.iv_2sls): coef.lm_pob_mesa = -0,9835. Reference path transcribed from the item's own estimation script.

Valores esperados e de onde vêm

crb_papers.q080.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 80 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['80'].effect and .standard_error
Trecho
index 80: effect -0.983511, standard_error 0.142278
Valor como publicado
-0.9835113358726062
Erro-padrão como publicado
0.14227784489218315
Valor
-0,983511-0.9835113358726062 (se 0,1422780.14227784489218315)
Conferido por
arquivo do benchmark · Read from the catalog at the pinned revision by scripts/freeze_crb_papers.py; the catalog states the value its estimation script prints.
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/causal_queries.json
Licença
MIT
  • #2 figura da resposta próxima: -0,983511-0.9835113358726062 · relativa 0.000001 · absoluta 1.4227784489218315e-7
  • #3 figura da resposta próxima: 0,1422780.14227784489218315 · relativa 0.000001 · absoluta 1.4227784489218315e-7

crb_papers.q080.script

Fonte
CausalReasoningBenchmark reference estimation script for index 80
Tipo
software
Localização
solutions/research_papers/IV/Rueda2017/question/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 662 bytes at the pinned revision; the reference path of the item is a transcription of this script into typed transforms and tool calls (never executed here).
Implementação
independente da ferramenta
sha256 da cópia lida
c0fdc0cfcf6ffcbf82a88b27bc2abf800c82417f91569a72569c0e07cc18f8eb (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/IV/Rueda2017/question/estimation.py
Licença
MIT

    Verificações

    Cada verificação, o que esperava, o que observou e a chamada que leu
    #VerificaçãoObrigatóriaResultadoEsperadoObservadoTolerânciaFonteChamada
    0resposta com lastro
    every number backed
    obrigatóriapassou—[]———
    1figura da resposta próxima
    -0.9835113359 in [-1.262375912, -0.7046467599]?
    obrigatóriapassou[-1.2623759118612852,-0.7046467598839272]-0,983511-0.9835113358719844interval · [-1.2623759118612852, -0.7046467598839272]—call_582c0e22
    2figura da resposta próxima
    -0.9835113359 vs -0.9835113358726062
    registradapassou-0,983511-0.9835113358726062-0,983511-0.9835113358719844relativa 0.000001 · absoluta 1.4227784489218315e-7crb_papers.q080.goldcall_582c0e22
    3figura da resposta próxima
    0.1423917765 vs 0.14227784489218315
    registradafalhou0,1422780.142277844892183150,1423920.14239177652238938relativa 0.000001 · absoluta 1.4227784489218315e-7crb_papers.q080.goldcall_582c0e22

    Caminho de referência e caminho do agente

    A chamada de referência ao lado da chamada que deu a resposta, argumento por argumento.

    caminho de referência do item · benchmark_script

    Referência: infer.iv_2slsChamada do agente: infer.iv_2sls (call_582c0e22)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"muni_code""muni_code"igual
    dataset"ref_iv""ref_iv"igual
    endog["lm_pob_mesa"]["lm_pob_mesa"]igual
    exog["lpopulation","lpotencial"]["lpopulation","lpotencial"]igual
    instruments["lz_pob_mesa_f"]["lz_pob_mesa_f"]igual
    level—0,950.95não fixado pela referência
    se—"HC1"não fixado pela referência
    subset——não fixado pela referência
    y"e_vote_buying""e_vote_buying"igual

    Pendências do caminho de referência: needs_op:iv_cluster_cr0

    Trajetória

    Cada evento na ordem em que aconteceu: turnos do modelo, chamadas de ferramentas, guardas, transformações e pontuação.

    1. 0Início

      What is the local causal effect of a higher average polling-station size on municipalities' counts of citizens' reports of vote buying? Report the local average effect for municipalities whose mean station size is shifted by exogenous institution-determined changes in the number of stations, controlling for municipal population and potential electorate and clustering standard errors by municipality.

      ds_0050 (agent)

      | Variable Name | Description | |----------------------|-----------------------------------------------------------------------------------------------------| | muni_code | Municipality numeric identifier (unique code for each municipality). | | year | Calendar year / election year of the observation. | | departamento | Department (first-level administrative division) name or code. | | municipio | Municipality name. | | sum_vb | Total count / aggregate of reported vote-buying incidents (municipality-year) (aggregate measure). | | e_vote_buying | Citizens' reports of vote-buying incidents filed in regional offices of the Attorney General (count).| | e_neg_t_buying | Citizens' reports of turnout-suppression / negative-turnout incidents (municipality-year count). | | neg_t_buying_moe | MOE monitors' reports of turnout suppression (municipality-year count). | | pob_mesa | Number of registered voters per polling station (polling-place size, raw). | | lpob_mesa | Natural log of pob_mesa (logged registered voters per polling station). | | m_pob_mesa | Mean registered voters per polling station in the municipality (municipality average). | | lm_pob_mesa | Natural log of m_pob_mesa (logged mean polling-place size). | | z_pob_mesa_f | Average polling-station size calculated by dividing the potential electorate by the number of stations implied by the election-year voter-cap rule. This is a rule-based value, distinct from the observed average m_pob_mesa. | | lz_pob_mesa_f | Natural log of z_pob_mesa_f (logged rule-predicted size). | | lown_resources | Log of municipality own resources (local revenue; logged local fiscal resources). | | larmed_actor | Lagged indicator for presence of nonstate armed actors in the municipality (e.g., guerrillas/paramilitaries). | | margin_index2 | Index measuring electoral competitiveness / margin (competitiveness indicator). | | nbi_i | Unsatisfied Basic Needs (NBI) poverty index (possibly standardized / imputed). | | nbi | Unsatisfied Basic Needs (NBI) raw measure (poverty indicator). | | lnbi_i | Natural log of nbi_i (logged poverty index variant). | | llnbi_i | Transformed / double-log version of the NBI index (log of log or alternative transform). | | lsize | Logged version of a polling-station size measure (log of size). | | lpotencial | Natural log of 'potencial' (logged measure of potential electorate or voting potential). | | potencial_cu | Cubed transformation of 'potencial' (potencial^3). | | potencial_sq | Squared transformation of 'potencial' (potencial^2). | | trend_f | Time trend or election-wave variable (numeric trend / period indicator). | | Population20 | Population aged 20 or older (used to construct per–polling‑station measures). | | lroyalties2_d_pc | Log of royalties (natural-resource transfers) per capita (logged royalties per person). | | local_e | Indicator for local/regional election year (binary flag). | | ingresos_totales | Total municipal revenues / total income (nominal value). | | own_resources_p | Share (proportion) of municipal revenue coming from own sources (self-generated revenue). | | revenue_current_p | Share (percent) of current revenue in total municipal revenue. | | tax_revenue_p | Share (percent) of revenue coming from taxes. | | population | Total municipal population (raw count). | | depto_code | Department code (numeric identifier for department). | | pot_mesa_mean | Mean of 'potential per mesa' (average potential electorate per polling station). | | potencial | Measure of potential electorate / voting potential (raw value used in controls). | | mesas | Number of polling stations ("mesas") in the municipality. | | pot_mesa_med | Median potential per mesa in the municipality. | | lpopulation | Natural log of population (logged municipal population). | | lpot_mesa_med | Natural log of pot_mesa_med (logged median potential per mesa). | | lpot_mesa_mean | Natural log of pot_mesa_mean (logged mean potential per mesa). | | lvoter_mesa | Natural log of voter_mesa (logged number of actual voters per mesa / turnout per mesa). | | voter_mesa | Number of actual voters per mesa (turnout per polling station). | | armed_actor | Indicator for presence of nonstate armed actors (guerrillas/paramilitaries) in the municipality. | | size2 | Alternative polling-station size measure (variant / secondary definition of size). | | lsize2 | Natural log of size2 (logged alternative size). | | size | Polling-station size (alternative / additional size metric). | | le_parties_av | Logged average of party-related measure (e.g., logged average number of parties or lists). | | closeness_CG | Measure of electoral closeness (e.g., closeness of congressional/general contest). | | l4margin_index2 | Four-period lag (or 4-election lag) of margin_index2 (lagged competitiveness measure). | | lcoca_area | Log of area under coca cultivation (proxy for illicit-crop presence / local illicit activity). | | l4lsize | Four-period lag of lsize (lagged log-size of polling stations). | | l4lpob_mesa | Four-period lag of lpob_mesa (lagged logged registered voters per polling station). | | pob_mesa_q | Polling-station size quartile (categorical indicator for pob_mesa quartiles). | | discont_f | Retained indicator for inclusion in a near-threshold subset of municipalities around the electorate levels at which the voter-cap rule adds a polling station; 1 denotes inclusion and 0 exclusion. | | e_manipulating_results | Citizens' reports of manipulated election results / result tampering (municipality-year count). | | moving_votes_moe | MOE monitors' reports of votes being moved or misallocated between stations (count). | | vote_buying_moe | MOE monitors' reports of vote-buying incidents (municipality-year count). | | manipulating_results_moe | MOE monitors' reports of manipulated results (municipality-year count). | | ltrend_f | Logged / transformed version of trend_f (transformed time trend variable). | | VB_pc | Vote-buying reports per capita (vote-buying incidents scaled by population). | | monitor_MOE | Indicator for presence of MOE domestic election monitors in the municipality (binary). | | lpot_mesa | Natural log of pot_mesa (logged potential per polling station). | | pot_mesa | Potential electorate per mesa (potential votes / potential voters per polling station). | | sample | Indicator for inclusion in the analytical sample (binary flag). | Data Description: This is a municipality‑year panel assembled for Colombian elections in the mid‑2000s to early‑2010s (covering multiple general and regional election cycles). The file merges administrative incident records (citizen reports filed with regional offices of the Attorney General), incident logs collected by the domestic observer Misión de Observación Electoral (MOE), official polling‑station result files from the National Registrar (used to derive polling‑station size and rule‑based size measures), municipal demographic and fiscal indicators from administrative sources, and survey‑based individual measures where available (LAPOP). Variables include identifiers (department and municipality), election‑year indicators, several alternative and transformed measures of polling‑station and electorate size, presence/lagged indicators for nonstate armed actors and monitors, municipal socioeconomic controls (population, revenue and poverty measures), and multiple municipality‑level aggregates of reported electoral irregularities. The released extract contains 4,352 municipality-year observations from elections in 2006, 2007, 2010, and 2011. The National Registrar sets voter-cap rules, and registered voters within a precinct are allocated to stations alphabetically. In the supplied construction, the cap is 400 in 2006 and 2007, 500 in 2010, and 350 in 2011. For potential electorate V and cap C, z_pob_mesa_f is V divided by (floor((V−1)/C)+1). Crossing a multiple of the cap adds a predicted station and lowers the predicted average size. Actual station arrangements can differ because of geography and special voting locations. The near-threshold subset flag is retained as supplied; it is separate from the full-file sample flag.

      file 5074479809b19d8b0940810ad65624efcd6d2593a1768c4c44406560c5ae6d69 · frame —
      ref_iv (reference)
      file 5074479809b19d8b0940810ad65624efcd6d2593a1768c4c44406560c5ae6d69 · frame —
      1. select {"columns":["e_vote_buying","lm_pob_mesa","lpopulation","lpotencial","lz_pob_mesa_f","muni_code"]}
      2. dropna {"columns":["e_vote_buying","lm_pob_mesa","lpopulation","lpotencial","lz_pob_mesa_f","muni_code"]}
    2. 1Transformação
      ref_iv · select {"columns":["e_vote_buying","lm_pob_mesa","lpopulation","lpotencial","lz_pob_mesa_f","muni_code"]}
      4352 → 4352 linhas
      Frame antes → depois: 40a0ce2605ca → 98d485bbb714
    3. 2Transformação
      ref_iv · dropna {"columns":["e_vote_buying","lm_pob_mesa","lpopulation","lpotencial","lz_pob_mesa_f","muni_code"]}
      4352 → 4352 linhas
      Frame antes → depois: 98d485bbb714 → 98d485bbb714
    4. 3Turno do modelo
      call_50e5330ereferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      IV2SLS(y, [Intercept, lpopulation, lpotencial], lm_pob_mesa, lz_pob_mesa_f).fit(cov_type='clustered', clusters=muni_code)

      Chamadas pedidas
      • infer.iv_2sls
        {
          "cluster": "muni_code",
          "dataset": "ref_iv",
          "endog": [
            "lm_pob_mesa"
          ],
          "exog": [
            "lpopulation",
            "lpotencial"
          ],
          "instruments": [
            "lz_pob_mesa_f"
          ],
          "y": "e_vote_buying"
        }
    5. 4Ferramenta
      infer.iv_2slsv0.1.0call_582c0e22decidido pelo modeloreexecutável

      Motivo: IV2SLS(y, [Intercept, lpopulation, lpotencial], lm_pob_mesa, lz_pob_mesa_f).fit(cov_type='clustered', clusters=muni_code)

      Argumentos
      {
        "cluster": "muni_code",
        "dataset": "ref_iv",
        "endog": [
          "lm_pob_mesa"
        ],
        "exog": [
          "lpopulation",
          "lpotencial"
        ],
        "instruments": [
          "lz_pob_mesa_f"
        ],
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "y": "e_vote_buying"
      }
      Figuras call_582c0e22
      ChaveValorValor brutoErro-padrãoIC
      coef.lm_pob_mesa
      2SLS effect of lm_pob_mesa on e_vote_buying
      -0,983511 -0.98351133587198440,1423920.14239177652238938-1,2629 – -0,70412
      Diagnósticos
      • first_stage_F.lm_pob_mesa = 8.598,338598.326402210985
      • partial_r2.lm_pob_mesa = 0,4167190.41671930271655977
      • weak_instrument = false
      • n_obs = 4.3524352
      • se_type = "cluster"
      • n_clusters = 1.0981098
      Tabelas (3)
      {
        "coefficients": [
          {
            "ci_high": 4.506129634765411,
            "ci_low": 2.052064142440678,
            "coef": 3.2790968886030445,
            "p": 1.8889105977161856e-7,
            "se": 0.6253578874327064,
            "t": 5.243552459320187,
            "term": "Intercept"
          },
          {
            "ci_high": -0.1273439048996614,
            "ci_low": -0.3450648694935662,
            "coef": -0.2362043871966138,
            "p": 0.000022450535313337847,
            "se": 0.05548080232336315,
            "t": -4.257407559103509,
            "term": "lpopulation"
          },
          {
            "ci_high": 0.7227308224818391,
            "ci_low": 0.3625123512961167,
            "coef": 0.5426215868889779,
            "p": 4.524942702702201e-9,
            "se": 0.09179276708770671,
            "t": 5.911376289272448,
            "term": "lpotencial"
          },
          {
            "ci_high": -0.7041203245992487,
            "ci_low": -1.26290234714472,
            "coef": -0.9835113358719844,
            "p": 8.365083817639631e-12,
            "se": 0.14239177652238938,
            "t": -6.9070796073489475,
            "term": "lm_pob_mesa"
          }
        ],
        "first_stage": [
          {
            "ci_high": 0.8125697349964491,
            "ci_low": 0.7788939832243948,
            "coef": 0.795731859110422,
            "endog": "lm_pob_mesa",
            "instrument": "lz_pob_mesa_f",
            "p": 0,
            "se": 0.008581432342268313,
            "t": 92.72716108137348
          }
        ],
        "reduced_form": [
          {
            "ci_high": -0.5620107105541738,
            "ci_low": -1.0032118969450372,
            "coef": -0.7826113037496055,
            "instrument": "lz_pob_mesa_f",
            "p": 5.807979274655961e-12,
            "se": 0.11242920888505958,
            "t": -6.9609251146621265
          }
        ]
      }
      Digests dos dados
      ref_iv 98d485bbb714d22b22ebc635f55a26f316c7d307ad083d1a904b3ba362f172f0
      sha256 do resultado
      79f4e5a57c777986f43c616b8d79992ddf2b0f1e951cf4703538e8f9dae99e77
    6. 5Turno do modelo
      call_18386581referenceFim: 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.lm_pob_mesa = -0,9835. Reference path transcribed from the item's own estimation script.",
          "caveats": [],
          "figure_refs": [
            "call_582c0e22:coef.lm_pob_mesa"
          ]
        }
    7. 6Guarda
      resposta aceita
      Números encontrados
      -0,9835
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    8. 7Resposta

      Estimativa (infer.iv_2sls): coef.lm_pob_mesa = -0,9835. Reference path transcribed from the item's own estimation script.

      Figuras citadas: call_582c0e22:coef.lm_pob_mesa · Parada: answered
      Figura da resposta: call_582c0e22:coef.lm_pob_mesa = -0,983511-0.9835113358719844 (se 0,1423920.14239177652238938)
    9. 8Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    10. 9Pontuação
      #1figura da resposta próximapassouobrigatória-0.9835113359 in [-1.262375912, -0.7046467599]?
    11. 10Pontuação
      #2figura da resposta próximapassouregistrada-0.9835113359 vs -0.9835113358726062
    12. 11Pontuação
      #3figura da resposta próximafalhouregistrada0.1423917765 vs 0.14227784489218315

    Épocas × verificações

    Resultado de cada verificação em cada época pontuada
    #0123
    Época 1passoupassoupassoufalhou

    Tentativas

    Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.

    Downloads desta tarefa

    Pacote da tarefa (.zip)
    104,6 KB · sha256 b64dc484cfd9730d60e0ecb71cc7b994d3909307f994d3d2d5e4440d3578b37c

    O hash de um zip é só de transporte: o que ancora a integridade é o RUN_SHA256SUMS que vai dentro dele, cópia exata do SHA256SUMS da execução.

    Downloads desta tarefa
    ArquivoTamanhosha256
    attachments/41650c955eee9a4e73534851a729c235cfef06abc57860993389cf69f0175c67.json427 B41650c955eee9a4e73534851a729c235cfef06abc57860993389cf69f0175c67
    attachments/433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420.json2,4 KB433a6877aa6a378bbd4b7e2752e484d8cb7499c562d68222be231f62ff2fa420
    attachments/52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b.json64,0 KB52ab03d8ec104abb85572294bf8a469d75c5de9e74d6042caabff8017adc572b
    attachments/6301b27b2a012471d0cc9ab4747d16298f7fe8fee0a7fef4d77d952d4343b971.json370 B6301b27b2a012471d0cc9ab4747d16298f7fe8fee0a7fef4d77d952d4343b971
    attachments/7670488aeeb06548e095163131bc9348ae10e61c4e7958a5ba17ec637c52fa72.json13,2 KB7670488aeeb06548e095163131bc9348ae10e61c4e7958a5ba17ec637c52fa72
    attachments/808937c1dbe849d9d7828a394c4e6e3655d603c87fc08ef9e9b4bcac96a417fb.json1,6 KB808937c1dbe849d9d7828a394c4e6e3655d603c87fc08ef9e9b4bcac96a417fb
    attachments/88e89565e05f1fc34e5a67939fc244123ca3f7ec349dbcb16023a9ecb7d88286.json1,7 KB88e89565e05f1fc34e5a67939fc244123ca3f7ec349dbcb16023a9ecb7d88286
    native/crb_papers/crb_papers.q080.e1.a1.json4,3 KB6a3f88878b9a066ae4b3c6d2c524751f44e2e66f8fee14b687661bea8741b42e
    sources/crb_papers.q080.gold.json1,1 KB28c62b66152b885bcba4b117d6308f9f9a72d0ba97c56aef48eac6b227c912cb
    sources/crb_papers.q080.script.json1,1 KBc4028877a3949d608ea9dc0d4a5cbc55b98e3cf9a5d62a971439b8be220f2069
    specs/crb_papers.q080.json18,0 KB0719bcc418c94ba887be735c11606092c5b39a85db167580d6a31e77f59d48da
    traces/crb_papers.q080.e1.a1.jsonl27,8 KB8692431ce74da06e9a44271b1bfc89ca1dc8af377c752904c63063f80c52807f
    trajectories/crb_papers.q080.e1.a1.jsonl23,7 KB4bb248a18a0b27078698ffb8148a86a96914473fc2b0460759d473713df08288