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Tarefa qrdata_socsci.382

qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170010Z-suite-qrdata_socsci-scripted-7912b3

Veredito

falhouDecidida pela verificação 1: figura da resposta próxima, fora da tolerância.
0.062 in (-0.06386, -0.06014)?

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

Pergunta feita ao analista

Estimate the average treatment effect of raising the mininum wage on the proportion of full-time employees using difference-in-differences (DiD). Please round to the nearest thousandth. The answer is positive if raising the mininum wage increases the proportion of full-time employees.

Resposta

Estimativa (infer.ols): coef.nj = 0,06156. Caminho de referência inferido à mão.

Valores esperados e de onde vêm

qrdata.gold.382

Fonte
QRData benchmark/QRData.json at de450af45ff7101b328bb064c6b475f73414a7ed (Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Findings of the Association for Computational Linguistics: ACL 2024, pages 9215-9235.)
Tipo
benchmark_gold
Localização
QRData.json item 382 (0-based), field 'answer'
Trecho
item 382 answer -0.062
Valor
-0,062-0.062
Conferido por
arquivo do benchmark · The printed gold is in the excerpt and index.json. It has fewer than 3 significant digits, so it is not set as value_as_published: the isolation gate searches that field verbatim, and a string this short can occur anywhere in a prompt (the gate does not search such numbers either).
sha256 da cópia lida
e076d960f19434adf728aab70480ce34808c06066fcea48eaa38b145d1a111a1 (identifica a cópia lida, não um arquivo canônico)
Desvio conhecido
não conferido
The gold -0.062 has the opposite sign of the difference in differences the question defines (positive if raising the minimum wage increases the full-time proportion): the NJ change minus the PA change in the mean full-time proportion is +0.0616. Found by an independent pandas computation before any MAGI tool ran.
Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/QRData.json
Licença
CC BY-NC 4.0
  • #1 figura da resposta próxima: [-0.06386,-0.06014] · interval · [-0.06386, -0.06014] ()

qrdata.eval_py

Fonte
QRData benchmark/eval.py at de450af45ff7101b328bb064c6b475f73414a7ed (Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu, Kai-Wei Chang, Yansong Feng. Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Data. Findings of the Association for Computational Linguistics: ACL 2024, pages 9215-9235.)
Tipo
software
Localização
benchmark/eval.py: extract_first_number, calc_acc
Valor
—
Conferido por
arquivo do benchmark · Vendored byte for byte as suites/data/qrdata_socsci/vendor/benchmark_eval.py.txt; a parity test runs its functions against MAGI's port and the answer_figure_close check.
Implementação
independente da ferramenta
sha256 da cópia lida
d6e5c1f7535ac9413951996c15544beb6f184244acb2da56e1fcd0942837e33d (identifica a cópia lida, não um arquivo canônico)
Endereço
https://raw.githubusercontent.com/xxxiaol/QRData/de450af45ff7101b328bb064c6b475f73414a7ed/benchmark/eval.py
Licença
CC BY-NC 4.0

    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
    0.062 in (-0.06386, -0.06014)?
    obrigatóriafalhou[-0.06386,-0.06014]0,0620.062interval · [-0.06386, -0.06014] ()qrdata.gold.382call_d0b3daab

    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 · hand_inferred

    Referência: infer.olsChamada do agente: infer.ols (call_d0b3daab)igual
    Argumentos da referência e do agente
    ArgumentoReferênciaAgente
    cluster——não fixado pela referência
    controls—[]não fixado pela referência
    dataset"minwage_props""minwage_props"igual
    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["nj"]["nj"]igual
    y"fullPropChange""fullPropChange"igual

    Trajetória

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

    1. 0Início

      Estimate the average treatment effect of raising the mininum wage on the proportion of full-time employees using difference-in-differences (DiD). Please round to the nearest thousandth. The answer is positive if raising the mininum wage increases the proportion of full-time employees.

      minwage (agent)

      Two social science researchers examined the impact of raising the minimum wage on employment in the fast-food industry. In 1992, the state of New Jersey (NJ) in the United States raised the minimum wage from $4.25 to $5.05 per hour. Did such an increase in the minimum wage reduce employment as economic theory predicts? As discussed above, answering this question requires inference about the NJ employment rate in the absence of such a raise in the minimum wage. Since this counterfactual outcome is not observable, we must somehow estimate it using observed data. One possible strategy is to look at another state in which the minimum wage did not increase. For example, the researchers of this study chose the neighboring state, Pennsylvania (PA), on the grounds that NJ’s economy resembles that of Pennsylvania, and hence the fast-food restaurants in the two states are comparable. Under this cross-section comparison design, therefore, the fast-food restaurants in NJ serve as the treatment group receiving the treatment (i.e., the increase in the minimum wage), whereas those in PA represent the control group, which did not receive such a treatment. To collect pretreatment and outcome measures, the researchers surveyed the fast-food restaurants before and after the minimum wage increase. Specifically, they gathered information about the number of full-time employees, the number of part-time employees, and their hourly wages, for each restaurant. The CSV file minwage.csv contains this data set. Variable Description chain: name of the fast-food restaurant chain location: location of the restaurants (centralNJ, northNJ, PA, shoreNJ, southNJ) wageBefore: wage before the minimum-wage increase wageAfter: wage after the minimum-wage increase fullBefore: number of full-time employees before the minimum-wage increase fullAfter: number of full-time employees after the minimum-wage increase partBefore: number of part-time employees before the minimum-wage increase partAfter: number of part-time employees after the minimum-wage increase

      file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
      minwage_props (reference)
      file 3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970 · frame —
      1. derive {"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}
      2. derive {"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}
      3. derive {"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}
      4. derive {"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}
      5. derive {"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}
      6. derive {"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}
    2. 1Transformação
      minwage_props · derive {"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}
      358 → 358 linhas
      Frame antes → depois: 55dec890a044 → 808a12f40fca
    3. 2Transformação
      minwage_props · derive {"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}
      358 → 358 linhas
      Frame antes → depois: 808a12f40fca → 891fe31e6dd3
    4. 3Transformação
      minwage_props · derive {"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}
      358 → 358 linhas
      Frame antes → depois: 891fe31e6dd3 → fc02f54744c1
    5. 4Transformação
      minwage_props · derive {"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}
      358 → 358 linhas
      Frame antes → depois: fc02f54744c1 → 590bda545f53
    6. 5Transformação
      minwage_props · derive {"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}
      358 → 358 linhas
      Frame antes → depois: 590bda545f53 → 3ab27676376a
    7. 6Transformação
      minwage_props · derive {"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}
      358 → 358 linhas
      Frame antes → depois: 3ab27676376a → 67fcfadd313b
    8. 7Turno do modelo
      call_aab06127referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache
      Texto visível

      Difference in differences as QSS 2.5.3 computes it: the NJ mean change in the full-time proportion minus the PA mean change (OLS of the change on the NJ indicator).

      Chamadas pedidas
      • infer.ols
        {
          "dataset": "minwage_props",
          "x": [
            "nj"
          ],
          "y": "fullPropChange"
        }
    9. 8Ferramenta
      infer.olsv0.1.0call_d0b3daabdecidido pelo modeloreexecutável

      Motivo: Difference in differences as QSS 2.5.3 computes it: the NJ mean change in the full-time proportion minus the PA mean change (OLS of the change on the NJ indicator).

      Argumentos
      {
        "cluster": null,
        "controls": [],
        "dataset": "minwage_props",
        "fe": [],
        "intercept": true,
        "level": 0.95,
        "se": "HC1",
        "subset": null,
        "weights": null,
        "x": [
          "nj"
        ],
        "y": "fullPropChange"
      }
      Figuras call_d0b3daab
      ChaveValorValor brutoErro-padrãoIC
      coef.nj
      Coefficient on nj in the regression of fullPropChange
      0,0615583 0.0615583123122471140,04533810.04533812955723252-0,0276059 – 0,150723
      Diagnósticos
      • n_obs = 358358
      • k = 22
      • r2 = 0,006022070.006022068316271945
      • df_resid = 356356
      • se_type = "HC1"
      • n_clusters = —
      Tabelas (1)
      {
        "coefficients": [
          {
            "ci_high": 0.044433215493594386,
            "ci_low": -0.11980035969180886,
            "coef": -0.037683572099107235,
            "p": 0.36740156449902767,
            "se": 0.041754652730595164,
            "t": -0.902499952334536,
            "term": "Intercept"
          },
          {
            "ci_high": 0.15072254458889722,
            "ci_low": -0.027605919964403,
            "coef": 0.061558312312247114,
            "p": 0.175399733065515,
            "se": 0.04533812955723252,
            "t": 1.3577602983056252,
            "term": "nj"
          }
        ]
      }
      Digests dos dados
      minwage_props 67fcfadd313ba1d19f472b3dfda49f69f3c7884474526b8969637c189accc2da
      sha256 do resultado
      c9646f53ed46ac81ae06c564869682b3c6cbb8f1146fe21cd32f448bab96e162
    10. 9Turno do modelo
      call_9ec407c1referenceFim: 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.nj = 0,06156. Caminho de referência inferido à mão.",
          "caveats": [],
          "figure_refs": [
            "call_d0b3daab:coef.nj"
          ]
        }
    11. 10Guarda
      resposta aceita
      Números encontrados
      0,06156
      Sem lastro
      —
      Não citados
      —
      1 nova(s) tentativa(s) restante(s)
    12. 11Resposta

      Estimativa (infer.ols): coef.nj = 0,06156. Caminho de referência inferido à mão.

      Figuras citadas: call_d0b3daab:coef.nj · Parada: answered
      Figura da resposta: call_d0b3daab:coef.nj = 0,06155830.061558312312247114 (se 0,04533810.04533812955723252)
    13. 12Pontuação
      #0resposta com lastropassouobrigatóriaevery number backed
    14. 13Pontuação
      #1figura da resposta próximafalhouobrigatória0.062 in (-0.06386, -0.06014)?

    Épocas × verificações

    Resultado de cada verificação em cada época pontuada
    #01
    Época 1passoufalhou

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