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Tarefa crb_papers.q008

crb_papersagentT2publicépoca 1, tentativa 1Execução: 20261015T171027Z-suite-crb_papers-naive-54c3f2

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
1.479833797 in [-0.9180917126, 1.464641327]?

Família do método (das chamadas de ferramentas): ols (regression)

Pergunta feita ao analista

Among countries that adopted a parliamentary gender quota in 1997, how did their public-health spending share change from 1997 to 1998 relative to countries that never adopted a quota?

Resposta

Estimativa ingênua (infer.ols): coef.adopt_quotalag = 1,480.

Valores esperados e de onde vêm

crb_papers.q008.gold

Fonte
CausalReasoningBenchmark, causal_queries.json, index 8 (revision edd0fe1c1d1c)
Tipo
benchmark_gold
Localização
causal_queries.json['8'].effect and .standard_error
Trecho
index 8: effect 0.273275, standard_error 0.60784
Valor como publicado
0.27327480718163333
Erro-padrão como publicado
0.6078400610971942
Valor
0,2732750.27327480718163333 (se 0,607840.6078400610971942)
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: 0,2732750.27327480718163333 · relativa 0.000001 · absoluta 6.078400610971943e-7
  • #3 figura da resposta próxima: 0,607840.6078400610971942 · relativa 0.000001 · absoluta 6.078400610971943e-7

crb_papers.q008.script

Fonte
CausalReasoningBenchmark reference estimation script for index 8
Tipo
software
Localização
solutions/research_papers/DiD/Clayton_Zetterberg_2018/question_1/estimation.py
Valor
—
Conferido por
arquivo do benchmark · sha256 of the script's 2468 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
9d5ca79a37faed385ded230ba83c8a14772cdc02b27ce7babe652163abf2ff81 (identifica a cópia lida, não um arquivo canônico)
Endereço
https://huggingface.co/datasets/syrgkanislab/CausalReasoningBenchmark/resolve/edd0fe1c1d1c5c0a4f0e3e67a50481ac679fb888/solutions/research_papers/DiD/Clayton_Zetterberg_2018/question_1/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
    1.479833797 in [-0.9180917126, 1.464641327]?
    obrigatóriafalhou[-0.9180917125688675,1.4646413269321341]1,479831.4798337972707618interval · [-0.9180917125688675, 1.4646413269321341]—call_e3f66099
    2figura da resposta próxima
    1.479833797 vs 0.27327480718163333
    registradafalhou0,2732750.273274807181633331,479831.4798337972707618relativa 0.000001 · absoluta 6.078400610971943e-7crb_papers.q008.goldcall_e3f66099
    3figura da resposta próxima
    0.2235896409 vs 0.6078400610971942
    registradafalhou0,607840.60784006109719420,223590.22358964094553635relativa 0.000001 · absoluta 6.078400610971943e-7crb_papers.q008.goldcall_e3f66099

    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.olsChamada do agente: infer.ols (call_e3f66099)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster"country"—diferente
    controls["treat","post","demlag","dem_cumlag","autoclag","leftlag","left_cumlag","lnODAlag","lnOillag","lnTradelag"][]diferente
    dataset"ref_panel""ds_0005"diferente
    fe—[]não fixado pela referência
    intercept—truenão fixado pela referência
    level—0,950.95não fixado pela referência
    se—"HC1"não fixado pela referência
    subset——não fixado pela referência
    weights——não fixado pela referência
    x["treat_post"]["adopt_quotalag"]diferente
    y"percenthealth""percenthealth"igual

    Trajetória

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

    1. 0Início

      Among countries that adopted a parliamentary gender quota in 1997, how did their public-health spending share change from 1997 to 1998 relative to countries that never adopted a quota?

      ds_0005 (agent)

      | Variable Name | Description | | --- | --- | | year | Calendar year of observation (1995–2012). Each row is a country–year. | | percenthealth | Share of total general government expenditure allocated to public health (%). Drawn from the World Bank’s World Development Indicators (sectoral spending as a fraction of total government spending). | | country | Country name corresponding to the national polity whose lower or single parliamentary chamber is referenced for legislative variables. | | ever_high_adopt_treat | Indicator equal to 1 if the country ever experienced a large first-year jump in women’s parliamentary seat share associated with the first election using a quota (above the sample mean “shock”) during 1995–2012; 0 otherwise. Time-invariant within a country. | | time_trend | Numeric time index equal to the calendar year, included to capture linear secular change. | | time_trend_2 | Square of the numeric time index (year^2), allowing flexible nonlinear trends over time. | | demlag | Democracy indicator lagged one year: 1 if the prior year’s Polity IV combined score was ≥ 6; 0 otherwise. | | dem_cumlag | Cumulative years of democracy since 1945 up through the previous year (count based on Polity IV threshold ≥ 6). | | autoclag | Autocracy indicator lagged one year: 1 if the prior year’s Polity IV combined score was ≤ −6; 0 otherwise. | | leftlag | Indicator lagged one year for whether the governing/prime minister’s party was coded as left-of-center in the Database of Political Institutions. | | left_cumlag | Cumulative years of left-leaning executive rule since 1945 up through the previous year (based on DPI coding). | | lnODAlag | Natural log of official development assistance received as a percentage of GDP, lagged one year (World Bank). | | lnOillag | Natural log of oil rents as a percentage of GDP, lagged one year (World Bank). Oil rents reflect the value of crude oil production minus costs as a share of the economy. | | lnTradelag | Natural log of total trade as a percentage of GDP, lagged one year (World Bank). Total trade is exports plus imports of goods and services. | | lnFDIlag | Natural log of foreign direct investment, net inflows, as a percentage of GDP, lagged one year (World Bank). | | increaselag | Percentage-point increase in women’s share of seats in the lower/single parliamentary chamber at the first election conducted under a gender quota (“quota shock”), lagged one year. Calculated as the difference between the last pre-quota election and the first quota-based election; coded 0 where no implementation occurred in the period. | | adopt_quotalag | Indicator for the presence of a parliamentary gender quota from the year of adoption onward, lagged one year. Adoption covers reserved-seat provisions, candidate-list gender requirements, and voluntary party quotas that collectively cover at least ~30% of lower-house seats; compiled from the International IDEA Quota Project and harmonized scholarly sources. | Dataset Context: This dataset is a country–year panel spanning 1995–2012, a period marked by widespread introduction and use of electoral gender quota policies across regions. Fiscal variables come from the World Bank’s World Development Indicators, which report sectoral expenditures as shares of total general government spending. The health-spending series used here captures the proportion of national budgets allocated to public health, including both recurrent and capital outlays, enabling consistent comparison across countries and years. Information on quota policies—adoption dates, first election under a quota, and the magnitude of changes in women’s legislative representation—was compiled from public administrative and research sources, notably the International IDEA Quota Project, and cross-checked with scholarly datasets and case documentation. Adoption covers three institutional designs: reserved seats for women, legal candidate-list requirements, and voluntary party quotas that meet a threshold of parliamentary seat coverage. In many systems, there is a lag between legal adoption and first use in an election; some adopters did not functionally implement quotas within the 1995–2012 window. Legislative composition is measured for the lower or single parliamentary chamber. The dataset includes a derived “quota shock” measure that records the first percentage-point jump in women’s seat share at the initial quota-based election; this value reflects the immediate numerical change produced by the reform in each country. To align political and economic covariates with subsequent budgeting, several variables are lagged one year. Regime characteristics (democracy/autocracy status and cumulative democratic experience) are drawn from Polity IV. Governing party ideology is coded using the Database of Political Institutions. Macroeconomic indicators—official development assistance, oil rents, trade openness, and foreign direct investment—come from the World Bank and are expressed as percentages of GDP with natural-log transformations. The panel also includes linear and quadratic time indices to summarize long-run temporal patterns. Coverage emphasizes countries with available fiscal and political data in the post-1995 period. Some early quota adopters prior to 1995 are outside scope to maintain consistent timing and measurement. As with most cross-national administrative series, occasional missingness may reflect gaps in national reporting or differences in when reforms occurred relative to fiscal years. Overall, the file provides a harmonized basis for examining how legislative reforms and macro-political contexts coincide with evolving public budget compositions worldwide.

      file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —
      ref_a_pre (reference)
      file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —
      1. dropna {"columns":["percenthealth","year","country"]}
      2. filter {"query":"year == 1997 and adopt_quotalag == 0"}
      3. collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"a_pre"}],"by":["country"]}
      ref_c_post (reference)
      file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —
      1. dropna {"columns":["percenthealth","year","country"]}
      2. filter {"query":"year == 1998 and adopt_quotalag == 0"}
      3. collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"c_post"}],"by":["country"]}
      ref_never (reference)
      file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —
      1. dropna {"columns":["percenthealth","year","country"]}
      2. collapse {"aggs":[{"column":"adopt_quotalag","fn":"max","name":"amax"}],"by":["country"]}
      3. derive {"args":{"column":"amax","default":1,"keep_unmapped":false,"mapping":[{"source":1,"target":0}]},"name":"never","op":"map"}
      ref_panel (reference)
      file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —
      1. dropna {"columns":["percenthealth","year","country"]}
      2. filter {"query":"year in [1997, 1998]"}
      3. merge {"how":"left","on":["country"],"with":"ref_a_pre"}
      4. merge {"how":"left","on":["country"],"with":"ref_t_post"}
      5. merge {"how":"left","on":["country"],"with":"ref_c_post"}
      6. merge {"how":"left","on":["country"],"with":"ref_never"}
      7. filter {"query":"a_pre == a_pre and (t_post == t_post or (c_post == c_post and never == 1))"}
      8. derive {"args":{"cmp":"ge","column":"t_post","value":1},"name":"treat","op":"indicator"}
      9. derive {"args":{"column":"treat","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"treat","op":"map"}
      10. derive {"args":{"cmp":"eq","column":"year","value":1998},"name":"post","op":"indicator"}
      11. derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}
      ref_t_post (reference)
      file 1cbd3916e41bbdaefe88cecac281fdfc042a1d9424b7a7c5db2963e596c199ee · frame —
      1. dropna {"columns":["percenthealth","year","country"]}
      2. filter {"query":"year == 1998 and adopt_quotalag == 1"}
      3. collapse {"aggs":[{"column":"adopt_quotalag","fn":"count","name":"t_post"}],"by":["country"]}
    2. 1Turno do modelo
      call_777951a5naive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      Linha de base ingênua: regressão do resultado no tratamento, sem controles.

      Chamadas pedidas
      • infer.ols
        {
          "dataset": "ds_0005",
          "x": [
            "adopt_quotalag"
          ],
          "y": "percenthealth"
        }
    3. 2Ferramenta
      infer.olsv0.1.0call_e3f66099decidido pelo modeloreexecutável

      Motivo: Linha de base ingênua: regressão do resultado no tratamento, sem controles.

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "ds_0005",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "adopt_quotalag"
        ],
        "y": "percenthealth"
      }
      Figuras call_e3f66099
      ChaveValorValor brutoErro-padrãoIC
      coef.adopt_quotalag
      Coefficient on adopt_quotalag in the regression of percenthealth
      1,47983 1.47983379727076180,223590.223589640945536351,04138 – 1,91829
      Diagnósticos
      • n_obs = 2.3362336
      • k = 22
      • r2 = 0,02188070.021880733661343243
      • df_resid = 2.3342334
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 10.403315188054128,
            "ci_low": 10.0248661512047,
            "coef": 10.214090669629414,
            "p": 0,
            "se": 0.09649482991485565,
            "t": 105.85117025069677,
            "term": "Intercept"
          },
          {
            "ci_high": 1.918288812632785,
            "ci_low": 1.0413787819087386,
            "coef": 1.4798337972707618,
            "p": 4.485647368389467e-11,
            "se": 0.22358964094553635,
            "t": 6.618525755543526,
            "term": "adopt_quotalag"
          }
        ]
      }
      • 145 rows were dropped because one of 'percenthealth', 'adopt_quotalag' was missing
      Digests dos dados
      ds_0005 cabc9e8f74fe2233ef9169fd8a711f15f5dce99fbe51da889f318efd0436a541
      sha256 do resultado
      ef33102e25e4adf2e2091adbff5d4faa79d3b247c44a05a01b810961ca91b742
    4. 3Turno do modelo
      call_e14a0d4enaive-olsFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Chamadas pedidas
      • submit_answer (meta-ferramenta)
        {
          "answer": "Estimativa ingênua (infer.ols): coef.adopt_quotalag = 1,480.",
          "caveats": [],
          "figure_refs": [
            "call_e3f66099:coef.adopt_quotalag"
          ]
        }
    5. 4Guarda
      resposta aceita
      Números encontrados
      1,480
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    6. 5Resposta

      Estimativa ingênua (infer.ols): coef.adopt_quotalag = 1,480.

      Figuras citadas: call_e3f66099:coef.adopt_quotalag · Parada: answered
      Figura da resposta: call_e3f66099:coef.adopt_quotalag = 1,479831.4798337972707618 (se 0,223590.22358964094553635)
    7. 6Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    8. 7Pontuação
      #1figura da resposta próximafalhouobrigatória1.479833797 in [-0.9180917126, 1.464641327]?
    9. 8Pontuação
      #2figura da resposta próximafalhouregistrada1.479833797 vs 0.27327480718163333
    10. 9Pontuação
      #3figura da resposta próximafalhouregistrada0.2235896409 vs 0.6078400610971942

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