Tarefa qrdata_socsci.382
qrdata_socsciagentT2publicépoca 1, tentativa 1Execução: 20261015T170010Z-suite-qrdata_socsci-scripted-7912b3Veredito
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.
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 conferidoThe 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
| # | Verificação | Obrigatória | Resultado | Esperado | Observado | Tolerância | Fonte | Chamada |
|---|---|---|---|---|---|---|---|---|
| 0 | resposta com lastro every number backed | obrigatória | passou | — | [] | — | — | — |
| 1 | figura da resposta próxima 0.062 in (-0.06386, -0.06014)? | obrigatória | falhou | [-0.06386,-0.06014] | 0,0620.062 | interval · [-0.06386, -0.06014] () | qrdata.gold.382 | call_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
| Argumento | Referência | Agente | |
|---|---|---|---|
| cluster | — | — | não fixado pela referência |
| controls | — | [] | não fixado pela referência |
| dataset | "minwage_props" | "minwage_props" | igual |
| fe | — | [] | não fixado pela referência |
| intercept | — | true | não fixado pela referência |
| level | — | 0,950.95 | não 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.
- 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
file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —minwage_props(reference)file3c3a97cca6fdd96f6856f7856b5f8bc786a31d1a9be78c9f610ef3070783e970· frame —derive{"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}derive{"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}derive{"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}derive{"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}derive{"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}derive{"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}
- 1Transformação
minwage_props·derive{"args":{"columns":["fullAfter","partAfter"]},"name":"totalAfter","op":"sum"}358 → 358 linhasFrame antes → depois:55dec890a044→808a12f40fca - 2Transformação
minwage_props·derive{"args":{"left":"fullAfter","right":"totalAfter"},"name":"fullPropAfter","op":"ratio"}358 → 358 linhasFrame antes → depois:808a12f40fca→891fe31e6dd3 - 3Transformação
minwage_props·derive{"args":{"columns":["fullBefore","partBefore"]},"name":"totalBefore","op":"sum"}358 → 358 linhasFrame antes → depois:891fe31e6dd3→fc02f54744c1 - 4Transformação
minwage_props·derive{"args":{"left":"fullBefore","right":"totalBefore"},"name":"fullPropBefore","op":"ratio"}358 → 358 linhasFrame antes → depois:fc02f54744c1→590bda545f53 - 5Transformação
minwage_props·derive{"args":{"left":"fullPropAfter","right":"fullPropBefore"},"name":"fullPropChange","op":"diff"}358 → 358 linhasFrame antes → depois:590bda545f53→3ab27676376a - 6Transformação
minwage_props·derive{"args":{"cmp":"ne","column":"location","value":"PA"},"name":"nj","op":"indicator"}358 → 358 linhasFrame antes → depois:3ab27676376a→67fcfadd313b - 7Turno do modelo
call_aab06127referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheTexto visívelDifference 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 pedidasinfer.ols{ "dataset": "minwage_props", "x": [ "nj" ], "y": "fullPropChange" }
- 8Ferramenta
infer.olsv0.1.0call_d0b3daabdecidido pelo modeloreexecutávelMotivo: 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 Chave Valor Valor bruto Erro-padrão IC coef.nj Coefficient on nj in the regression of fullPropChange0,0615583 0.0615583123122471140,0453381 0.04533812955723252-0,0276059 – 0,150723 Diagnósticosn_obs= 358358k= 22r2= 0,006022070.006022068316271945df_resid= 356356se_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_props67fcfadd313ba1d19f472b3dfda49f69f3c7884474526b8969637c189accc2da- sha256 do resultado
c9646f53ed46ac81ae06c564869682b3c6cbb8f1146fe21cd32f448bab96e162
- 9Turno do modelo
call_9ec407c1referenceFim:tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cacheChamadas pedidassubmit_answer(meta-ferramenta){ "answer": "Estimativa (infer.ols): coef.nj = 0,06156. Caminho de referência inferido à mão.", "caveats": [], "figure_refs": [ "call_d0b3daab:coef.nj" ] }
- 10Guardaresposta aceita
- Números encontrados
- 0,06156
- Sem lastro
- —
- Não citados
- —
1 nova(s) tentativa(s) restante(s) - 11Resposta
Estimativa (infer.ols): coef.nj = 0,06156. Caminho de referência inferido à mão.
Figuras citadas:call_d0b3daab:coef.nj· Parada:answeredFigura da resposta:call_d0b3daab:coef.nj= 0,06155830.061558312312247114(se 0,04533810.04533812955723252) - 12Pontuação#0resposta com lastropassouobrigatória
every number backed - 13Pontuação#1figura da resposta próximafalhouobrigatória
0.062 in (-0.06386, -0.06014)?
Épocas × verificações
| # | 0 | 1 |
|---|---|---|
| Época 1 | passou | falhou |
Tentativas
Cada tentativa é seu próprio arquivo e é publicada; só a última de cada época é pontuada.
- época 1, tentativa 1falhoupontuada(exibindo)
Downloads desta tarefa
142570a2c316d9ac426776363793a95dbb901cab113024b7d8ae2c8af9ffe561O 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.
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| sources/qrdata.gold.382.json | 1,7 KB | a0312f32ad5df4467756c91f8ff7b1652d4c567877ec7d5b1b7909f5f7ea1947 |
| specs/qrdata_socsci.382.json | 7,2 KB | 7c80fd402063edf0bd0de2176253f083504a65f43f78ce10b85eccabb42a262e |
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