Tarefa crb_papers.q012

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Pergunta feita ao analista

From 2011 to 2012, how did factories that became compliant (per the sourcing agent's annual compliance indicator) change in logged annual order value (natural log of estimated annual order value in constant 2012 USD) relative to factories that remained noncompliant over the same period?

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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

   From 2011 to 2012, how did factories that became compliant (per the sourcing agent's annual compliance indicator) change in logged annual order value (natural log of estimated annual order value in constant 2012 USD) relative to factories that remained noncompliant over the same period?

   ds_0007 (agent)

   | Variable Name | Description | | --- | --- | | (index) | Sequential row number from the CSV export; has no substantive meaning. | | fcode | Anonymous manufacturing establishment identifier. Stable across years within this file but masked to prevent reidentification or linkage to external sources. | | year | Calendar year of record (2009–2012). Reflects the period over which audits, orders, and operational performance were summarized. | | compyr | Annual indicator for whether the establishment was deemed compliant with the sourcing agent’s code of conduct in that year (1 = compliant, 0 = not compliant). Compliance is determined from social and environmental audits; values may be missing in years without an audit. | | gradeyr | Audit grade for the year mapped to a numeric scale (A = 3, B = 2, C = 1, D = 0). Grades summarize findings from factory audits that assess labor, health and safety, environmental, and legal practices. May be blank in years without an audit. | | lestspend_dall | Natural log of the estimated total value of customer orders routed through the sourcing agent to the factory in that calendar year, expressed in thousands of constant 2012 USD. Underlying nominal values were originally reported in annual bins; midpoints were used and deflated to 2012 dollars before logging. | | inspyr | Share of quality inspections passed during the year (0–1), calculated from the agent’s inspection records for orders it handled at the factory. Represents the fraction of checks that met specified quality criteria. | | ontimeyr | On-time delivery rate during the year (0–1), computed from order-tracking data for shipments coordinated by the agent, where “on time” indicates delivery on or before the scheduled date. | | factoryobs_perf | Count of underlying observations used to compute the year’s performance rates (e.g., number of inspections or orders contributing to inspyr and ontimeyr). Larger values imply more stable rate estimates. | | factorytt | Panel time index for convenience in longitudinal analyses (1 = 2009, 2 = 2010, 3 = 2011, 4 = 2012). | Dataset Context: This panel assembles administrative records from a multinational sourcing intermediary that connects buyers—primarily retailers and brand owners in advanced economies—with manufacturing suppliers across 36 countries. The intermediary maintains a supply-chain responsibility program with a written code of conduct covering labor conditions, environmental practices, health and safety, and compliance with local law. Factories are periodically audited by trained assessors, typically during one- to two-day site visits that review documents, conduct walkthroughs, and interview personnel. Audits produce letter grades (A–D), with the intermediary treating A/B as meeting its standard and C/D as not meeting it. For each calendar year, the data retain a factory’s grade and a binary indicator derived from these audits; some factories are visited multiple times in a year, while others may go a year without a recorded audit. The same intermediary aggregates commercial activity by factory and year from its order-management systems. Because customers provided annual purchasing in value bands, the dataset converts those bands to midpoints, deflates them to constant 2012 U.S. dollars using import price indices for consumer goods, and applies a natural log transformation. Operational performance metrics—on-time delivery and quality inspection pass rates—are calculated from order- and shipment-level tracking for business transacted through the agent; these rates are only observed in years when the intermediary coordinated orders at the factory, and their precision varies with the number of underlying events. Coverage spans 2009–2012 and a mix of product categories with a substantial share of apparel. Factory identities and any corporate names were anonymized under a data access agreement. Users should note common features of this type of administrative data: audit results reflect conditions at the time of inspection rather than continuous monitoring; performance rates pertain to the subset of a factory’s portfolio handled by the intermediary; and annual purchasing values are estimates derived from reported bins rather than exact ledgers. Despite these limits, the dataset offers rare, harmonized, multi-country visibility into auditing outcomes, order flows, and supplier performance within global manufacturing supply chains during the early 2010s.

   file 4bfe0955b959c2258c32c26060e79131a2397e9c8d2e7a2fb405e9d3d14ebb4e · frame —

   ref_panel (reference)

   file 4bfe0955b959c2258c32c26060e79131a2397e9c8d2e7a2fb405e9d3d14ebb4e · frame —

   1. select {"columns":["fcode","year","compyr","lestspend_dall"]}
   2. derive {"args":{"cmp":"gt","column":"compyr","value":0},"name":"compyr","op":"indicator"}
   3. derive {"args":{"column":"compyr","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"compyr","op":"map"}
   4. dropna {"columns":["fcode","year","lestspend_dall"]}
   5. filter {"query":"year in [2011, 2012]"}
   6. merge {"how":"left","on":["fcode"],"with":"ref_years"}
   7. merge {"how":"left","on":["fcode"],"with":"ref_wpre"}
   8. merge {"how":"left","on":["fcode"],"with":"ref_wpost"}
   9. filter {"query":"nyears == 2 and w2011 == 0 and (w2012 == 1 or w2012 == 0)"}
   10. derive {"args":{"cmp":"eq","column":"w2012","value":1},"name":"treat","op":"indicator"}
   11. derive {"args":{"cmp":"eq","column":"year","value":2012},"name":"post","op":"indicator"}
   12. derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}

   ref_wpost (reference)

   file 4bfe0955b959c2258c32c26060e79131a2397e9c8d2e7a2fb405e9d3d14ebb4e · frame —

   1. select {"columns":["fcode","year","compyr","lestspend_dall"]}
   2. derive {"args":{"cmp":"gt","column":"compyr","value":0},"name":"compyr","op":"indicator"}
   3. derive {"args":{"column":"compyr","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"compyr","op":"map"}
   4. dropna {"columns":["fcode","year","lestspend_dall"]}
   5. filter {"query":"year == 2012"}
   6. collapse {"aggs":[{"column":"compyr","fn":"mean","name":"w2012"}],"by":["fcode"]}

   ref_wpre (reference)

   file 4bfe0955b959c2258c32c26060e79131a2397e9c8d2e7a2fb405e9d3d14ebb4e · frame —

   1. select {"columns":["fcode","year","compyr","lestspend_dall"]}
   2. derive {"args":{"cmp":"gt","column":"compyr","value":0},"name":"compyr","op":"indicator"}
   3. derive {"args":{"column":"compyr","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"compyr","op":"map"}
   4. dropna {"columns":["fcode","year","lestspend_dall"]}
   5. filter {"query":"year == 2011"}
   6. collapse {"aggs":[{"column":"compyr","fn":"mean","name":"w2011"}],"by":["fcode"]}

   ref_years (reference)

   file 4bfe0955b959c2258c32c26060e79131a2397e9c8d2e7a2fb405e9d3d14ebb4e · frame —

   1. select {"columns":["fcode","year","compyr","lestspend_dall"]}
   2. derive {"args":{"cmp":"gt","column":"compyr","value":0},"name":"compyr","op":"indicator"}
   3. derive {"args":{"column":"compyr","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"compyr","op":"map"}
   4. dropna {"columns":["fcode","year","lestspend_dall"]}
   5. filter {"query":"year in [2011, 2012]"}
   6. collapse {"aggs":[{"column":"lestspend_dall","fn":"count","name":"n"}],"by":["fcode","year"]}
   7. collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["fcode"]}
2. 1Transformação
   ref_years · select {"columns":["fcode","year","compyr","lestspend_dall"]}

   8281 → 8281 linhas

   Frame antes → depois: 74ebc14fdf1f → e5fc57125cba
3. 2Transformação
   ref_years · derive {"args":{"cmp":"gt","column":"compyr","value":0},"name":"compyr","op":"indicator"}

   8281 → 8281 linhas

   Frame antes → depois: e5fc57125cba → e5fc57125cba
4. 3Transformação
   ref_years · derive {"args":{"column":"compyr","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"compyr","op":"map"}

   8281 → 8281 linhas

   Frame antes → depois: e5fc57125cba → 9efdfc044baf
5. 4Transformação
   ref_years · dropna {"columns":["fcode","year","lestspend_dall"]}

   8281 → 7137 linhas

   Frame antes → depois: 9efdfc044baf → abf76d151b71
6. 5Transformação
   ref_years · filter {"query":"year in [2011, 2012]"}

   7137 → 3875 linhas

   Frame antes → depois: abf76d151b71 → 888c61005d4b
7. 6Transformação
   ref_years · collapse {"aggs":[{"column":"lestspend_dall","fn":"count","name":"n"}],"by":["fcode","year"]}

   3875 → 3875 linhas

   Frame antes → depois: 888c61005d4b → dbad78b6315e
8. 7Transformação
   ref_years · collapse {"aggs":[{"column":"n","fn":"count","name":"nyears"}],"by":["fcode"]}

   3875 → 2245 linhas

   Frame antes → depois: dbad78b6315e → c58d3179c6da
9. 8Transformação
   ref_wpre · select {"columns":["fcode","year","compyr","lestspend_dall"]}

   8281 → 8281 linhas

   Frame antes → depois: 74ebc14fdf1f → e5fc57125cba
10. 9Transformação
    ref_wpre · derive {"args":{"cmp":"gt","column":"compyr","value":0},"name":"compyr","op":"indicator"}

    8281 → 8281 linhas

    Frame antes → depois: e5fc57125cba → e5fc57125cba
11. 10Transformação
    ref_wpre · derive {"args":{"column":"compyr","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"compyr","op":"map"}

    8281 → 8281 linhas

    Frame antes → depois: e5fc57125cba → 9efdfc044baf
12. 11Transformação
    ref_wpre · dropna {"columns":["fcode","year","lestspend_dall"]}

    8281 → 7137 linhas

    Frame antes → depois: 9efdfc044baf → abf76d151b71
13. 12Transformação
    ref_wpre · filter {"query":"year == 2011"}

    7137 → 2119 linhas

    Frame antes → depois: abf76d151b71 → 5c4aecc4392e
14. 13Transformação
    ref_wpre · collapse {"aggs":[{"column":"compyr","fn":"mean","name":"w2011"}],"by":["fcode"]}

    2119 → 2119 linhas

    Frame antes → depois: 5c4aecc4392e → 24c2cd4021f2
15. 14Transformação
    ref_wpost · select {"columns":["fcode","year","compyr","lestspend_dall"]}

    8281 → 8281 linhas

    Frame antes → depois: 74ebc14fdf1f → e5fc57125cba
16. 15Transformação
    ref_wpost · derive {"args":{"cmp":"gt","column":"compyr","value":0},"name":"compyr","op":"indicator"}

    8281 → 8281 linhas

    Frame antes → depois: e5fc57125cba → e5fc57125cba
17. 16Transformação
    ref_wpost · derive {"args":{"column":"compyr","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"compyr","op":"map"}

    8281 → 8281 linhas

    Frame antes → depois: e5fc57125cba → 9efdfc044baf
18. 17Transformação
    ref_wpost · dropna {"columns":["fcode","year","lestspend_dall"]}

    8281 → 7137 linhas

    Frame antes → depois: 9efdfc044baf → abf76d151b71
19. 18Transformação
    ref_wpost · filter {"query":"year == 2012"}

    7137 → 1756 linhas

    Frame antes → depois: abf76d151b71 → 33e54e33cf58
20. 19Transformação
    ref_wpost · collapse {"aggs":[{"column":"compyr","fn":"mean","name":"w2012"}],"by":["fcode"]}

    1756 → 1756 linhas

    Frame antes → depois: 33e54e33cf58 → 7a6b47359e19
21. 20Transformação
    ref_panel · select {"columns":["fcode","year","compyr","lestspend_dall"]}

    8281 → 8281 linhas

    Frame antes → depois: 74ebc14fdf1f → e5fc57125cba
22. 21Transformação
    ref_panel · derive {"args":{"cmp":"gt","column":"compyr","value":0},"name":"compyr","op":"indicator"}

    8281 → 8281 linhas

    Frame antes → depois: e5fc57125cba → e5fc57125cba
23. 22Transformação
    ref_panel · derive {"args":{"column":"compyr","default":0,"keep_unmapped":false,"mapping":[{"source":1,"target":1}]},"name":"compyr","op":"map"}

    8281 → 8281 linhas

    Frame antes → depois: e5fc57125cba → 9efdfc044baf
24. 23Transformação
    ref_panel · dropna {"columns":["fcode","year","lestspend_dall"]}

    8281 → 7137 linhas

    Frame antes → depois: 9efdfc044baf → abf76d151b71
25. 24Transformação
    ref_panel · filter {"query":"year in [2011, 2012]"}

    7137 → 3875 linhas

    Frame antes → depois: abf76d151b71 → 888c61005d4b
26. 25Transformação
    ref_panel · merge {"how":"left","on":["fcode"],"with":"ref_years"}

    3875 → 3875 linhas

    Frame antes → depois: 888c61005d4b → 4fd84346b530
27. 26Transformação
    ref_panel · merge {"how":"left","on":["fcode"],"with":"ref_wpre"}

    3875 → 3875 linhas

    Frame antes → depois: 4fd84346b530 → 57eea2af67e9
28. 27Transformação
    ref_panel · merge {"how":"left","on":["fcode"],"with":"ref_wpost"}

    3875 → 3875 linhas

    Frame antes → depois: 57eea2af67e9 → e42afb7aac0b
29. 28Transformação
    ref_panel · filter {"query":"nyears == 2 and w2011 == 0 and (w2012 == 1 or w2012 == 0)"}

    3875 → 2436 linhas

    Frame antes → depois: e42afb7aac0b → 515c5436d7f3
30. 29Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"w2012","value":1},"name":"treat","op":"indicator"}

    2436 → 2436 linhas

    Frame antes → depois: 515c5436d7f3 → bcdd85f4377b
31. 30Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2012},"name":"post","op":"indicator"}

    2436 → 2436 linhas

    Frame antes → depois: bcdd85f4377b → 78253a72f392
32. 31Transformação
    ref_panel · derive {"args":{"columns":["treat","post"]},"name":"treat_post","op":"interaction"}

    2436 → 2436 linhas

    Frame antes → depois: 78253a72f392 → def0a7b276a0
33. 32Turno do modelo
    call_d3574f89referenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('lestspend_dall ~ treat*post'), cluster by fcode

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "fcode",
        "controls": [
          "treat",
          "post"
        ],
        "dataset": "ref_panel",
        "x": [
          "treat_post"
        ],
        "y": "lestspend_dall"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
34. 33Ferramenta
    infer.olsv0.1.0call_1a65e4b2decidido pelo modeloreexecutável

    Motivo: smf.ols('lestspend_dall ~ treat*post'), cluster by fcode

    Argumentos

    {
      "cluster": "fcode",
      "controls": [
        "treat",
        "post"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "treat_post"
      ],
      "y": "lestspend_dall"
    }

    Figuras call_1a65e4b2

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.treat_post Coefficient on treat_post in the regression of lestspend_dall	0,336392	0.3363917116965359	0,1259180.12591763131474598	0,089352 – 0,583431

    Diagnósticos

    • n_obs = 2.4362436
    • k = 44
    • r2 = 0,008562340.00856233808221385
    • df_resid = 2.4322432
    • se_type = "cluster"
    • n_clusters = 1.2181218

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 6.696414017247825,
          "ci_low": 6.444342820676901,
          "coef": 6.570378418962363,
          "p": 0,
          "se": 0.06424110493675794,
          "t": 102.2768588029512,
          "term": "Intercept"
        },
        {
          "ci_high": 0.5834314226894514,
          "ci_low": 0.08935200070362051,
          "coef": 0.3363917116965359,
          "p": 0.00765157573059826,
          "se": 0.12591763131474598,
          "t": 2.6715219162254185,
          "term": "treat_post"
        },
        {
          "ci_high": 0.5819821316711885,
          "ci_low": -0.014659599600032602,
          "coef": 0.28366126603557795,
          "p": 0.06235098632277709,
          "se": 0.152055945263302,
          "t": 1.8655059198401387,
          "term": "treat"
        },
        {
          "ci_high": -0.0863543934511987,
          "ci_low": -0.31156228737283936,
          "coef": -0.19895834041201904,
          "p": 0.000545699322724241,
          "se": 0.05739491121087171,
          "t": -3.4664804982629276,
          "term": "post"
        }
      ]
    }

    Digests dos dados

    ref_panel def0a7b276a0f72b8bf91eb522179c4044e8b030216b86de4ec9de2e3961570b

    sha256 do resultado

    e01c461d80a222517c06ee522c52d4629600d1dccae7690977a7e92129c655ae

    Resultado mostrado ao modelo
35. 34Turno do modelo
    call_b58ce11dreferenceFim: 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.treat_post = 0,3364. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_1a65e4b2:coef.treat_post"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
36. 35Guarda
    resposta aceita

    Números encontrados

    0,3364

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
37. 36Resposta

    Estimativa (infer.ols): coef.treat_post = 0,3364. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_1a65e4b2:coef.treat_post · Parada: answered

    Figura da resposta: call_1a65e4b2:coef.treat_post = 0,3363920.3363917116965359 (se 0,1259180.12591763131474598)
38. 37Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
39. 38Pontuação
    #1figura da resposta próximapassouobrigatória0.3363917117 in [0.08959315432, 0.5831902691]?
40. 39Pontuação
    #2figura da resposta próximapassouregistrada0.3363917117 vs 0.33639171169653814
41. 40Pontuação
    #3figura da resposta próximapassouregistrada0.1259176313 vs 0.12591763131474554

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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