Tarefa crb_papers.q040

Veredito

Pergunta feita ao analista

By how much did the proportion of report text devoted to gender-equality topics (the overall attention measure) increase in Italian company-years after the 2011 quota versus Greek company-years, relative to 2010?

Valores esperados e de onde vêm

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

   By how much did the proportion of report text devoted to gender-equality topics (the overall attention measure) increase in Italian company-years after the 2011 quota versus Greek company-years, relative to 2010?

   ds_0021 (agent)

   | Variable Name | Description | | --- | --- | | year_f | Four-digit calendar year covered by the company report (e.g., 2007–2017). | | company | Numeric company identifier originating from external listings used to build the sampling frame (e.g., Orbis or stock exchange records). Stable within firm across years but not necessarily consecutive. | | quota | Policy-period indicator: equals 1 for Italian firm-years from 2011 onward (the year a national law regulating the gender composition of boards was enacted); equals 0 for Italian firm-years before 2011 and for all Greek firm-years in 2007–2017. Reflects the adoption date, not firm-specific compliance dates. | | sustain | Report type flag: 1 if the document is a sustainability/social responsibility report; 0 if it is an annual/financial report. When both report types existed for a firm-year, the sustainability report was retained. | | pct_rev_change | Year-over-year percentage change in the firm’s year-end revenue, computed as 100 × [(Revenue_t − Revenue_{t−1}) / Revenue_{t−1}]. May be missing where consecutive-year revenue figures were unavailable. | | prop_attn | Proportion of a report’s text devoted to gender-equality themes, measured as the share of tokens matching a curated dictionary (e.g., leadership representation, pay gaps, family-care policies, discrimination/harassment) divided by total tokens. Values range from 0 to 1. Texts were standardized to English (machine translation used when needed) before tokenization. | | companyid | Study-assigned numeric identifier for each company, providing a consistent panel unit across years (distinct from the source “company” code). | | year | Integer time index for analysis (1 = 2007, …, 11 = 2017), aligned to the study period and uniform across firms. | Dataset Context: This firm-year panel assembles corporate reporting from large publicly listed companies in Italy and Greece during 2007–2017, a period that spans a major corporate governance reform in Italy affecting the gender composition of boards (enacted in 2011 with a phased compliance schedule and sanctions). Greece did not introduce an equivalent requirement for private listed firms within this window. The two-country setting provides multiple years of corporate disclosures before and after the reform’s introduction in Italy. Sampling began with high-revenue listed firms on the Italian and Greek exchanges, identified through commercial listings. For each firm-year, publicly posted annual reports and sustainability/social responsibility reports were downloaded from company websites. When both report types were available, the sustainability report was retained because it typically includes more detail on workforce practices and social initiatives. This process yielded 962 reports for 96 firms (52 in Italy and 44 in Greece), with most companies observed repeatedly over the 11-year span. PDFs were converted to text and, where necessary, machine-translated to English to maintain a consistent analysis language. A dictionary-based approach quantified how much of each report focused on workplace gender-equality topics by counting dictionary token matches and dividing by the total number of tokens in the document. The dictionary was built from relevant policy and workplace terminology and includes terms related to leadership representation, pay equity, family-care and work–life balance provisions, and discrimination/harassment. Because report length and formatting vary, the measure is constructed as a proportion to facilitate comparisons across documents and years. Financial information extracted from reports or public filings was used to compute year-over-year revenue changes. Identifiers enable linkage across years: companyid provides a stable panel key, year is an internal time index, and year_f records the calendar year. Users should be aware that measurement can be influenced by factors such as document length, translation quality, and differences between annual and sustainability reporting formats; the sustain flag indicates report type to help account for such variation. Overall, the dataset offers a structured view of how major listed firms in two Southern European economies communicated about workplace gender-equality topics across a decade that included a salient corporate governance reform in one country.

   file e7a25901cae1d1a00a86567bbc1c285ed2ab23166c1bfdc4423b3134e05ea474 · frame —

   ref_ever (reference)

   file e7a25901cae1d1a00a86567bbc1c285ed2ab23166c1bfdc4423b3134e05ea474 · frame —

   1. select {"columns":["quota","prop_attn","companyid","sustain","pct_rev_change","year_f"]}
   2. rename {"map":{"year_f":"year"}}
   3. dropna {"columns":["quota","prop_attn","companyid","sustain","pct_rev_change","year"]}
   4. collapse {"aggs":[{"column":"quota","fn":"max","name":"ever_treated"}],"by":["companyid"]}

   ref_panel (reference)

   file e7a25901cae1d1a00a86567bbc1c285ed2ab23166c1bfdc4423b3134e05ea474 · frame —

   1. select {"columns":["quota","prop_attn","companyid","sustain","pct_rev_change","year_f"]}
   2. rename {"map":{"year_f":"year"}}
   3. dropna {"columns":["quota","prop_attn","companyid","sustain","pct_rev_change","year"]}
   4. merge {"how":"inner","on":["companyid"],"with":"ref_ever"}
   5. filter {"query":"year in [2010, 2011]"}
   6. derive {"args":{"cmp":"eq","column":"year","value":2011},"name":"post","op":"indicator"}
   7. derive {"args":{"columns":["ever_treated","post"]},"name":"post_ever","op":"interaction"}
2. 1Transformação
   ref_ever · select {"columns":["quota","prop_attn","companyid","sustain","pct_rev_change","year_f"]}

   962 → 962 linhas

   Frame antes → depois: 7b70b12a64c7 → 75fb3c24a6a7
3. 2Transformação
   ref_ever · rename {"map":{"year_f":"year"}}

   962 → 962 linhas

   Frame antes → depois: 75fb3c24a6a7 → 0a7613513c61
4. 3Transformação
   ref_ever · dropna {"columns":["quota","prop_attn","companyid","sustain","pct_rev_change","year"]}

   962 → 761 linhas

   Frame antes → depois: 0a7613513c61 → 0f8049ca2440
5. 4Transformação
   ref_ever · collapse {"aggs":[{"column":"quota","fn":"max","name":"ever_treated"}],"by":["companyid"]}

   761 → 90 linhas

   Frame antes → depois: 0f8049ca2440 → d7a807572b0e
6. 5Transformação
   ref_panel · select {"columns":["quota","prop_attn","companyid","sustain","pct_rev_change","year_f"]}

   962 → 962 linhas

   Frame antes → depois: 7b70b12a64c7 → 75fb3c24a6a7
7. 6Transformação
   ref_panel · rename {"map":{"year_f":"year"}}

   962 → 962 linhas

   Frame antes → depois: 75fb3c24a6a7 → 0a7613513c61
8. 7Transformação
   ref_panel · dropna {"columns":["quota","prop_attn","companyid","sustain","pct_rev_change","year"]}

   962 → 761 linhas

   Frame antes → depois: 0a7613513c61 → 0f8049ca2440
9. 8Transformação
   ref_panel · merge {"how":"inner","on":["companyid"],"with":"ref_ever"}

   761 → 761 linhas

   Frame antes → depois: 0f8049ca2440 → a23521f6e054
10. 9Transformação
    ref_panel · filter {"query":"year in [2010, 2011]"}

    761 → 161 linhas

    Frame antes → depois: a23521f6e054 → 6e4a5f70675a
11. 10Transformação
    ref_panel · derive {"args":{"cmp":"eq","column":"year","value":2011},"name":"post","op":"indicator"}

    161 → 161 linhas

    Frame antes → depois: 6e4a5f70675a → 68ce610fbcb5
12. 11Transformação
    ref_panel · derive {"args":{"columns":["ever_treated","post"]},"name":"post_ever","op":"interaction"}

    161 → 161 linhas

    Frame antes → depois: 68ce610fbcb5 → 989d1ac32923
13. 12Turno do modelo
    call_fd012c5freferenceFim: tool_use0 ms0 de entrada · 0 de saída · 0 de raciocínio · 0 em cache

    Texto visível

    smf.ols('prop_attn ~ post*ever_treated + sustain + pct_rev_change').fit(cov_type='cluster', groups=companyid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Chamadas pedidas

    • infer.ols

      {
        "cluster": "companyid",
        "controls": [
          "post",
          "ever_treated",
          "sustain",
          "pct_rev_change"
        ],
        "dataset": "ref_panel",
        "x": [
          "post_ever"
        ],
        "y": "prop_attn"
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
14. 13Ferramenta
    infer.olsv0.1.0call_d2120f49decidido pelo modeloreexecutável

    Motivo: smf.ols('prop_attn ~ post*ever_treated + sustain + pct_rev_change').fit(cov_type='cluster', groups=companyid) (CR1 errors with statsmodels' default G/(G-1)·(N-1)/(N-K) correction)

    Argumentos

    {
      "cluster": "companyid",
      "controls": [
        "post",
        "ever_treated",
        "sustain",
        "pct_rev_change"
      ],
      "dataset": "ref_panel",
      "fe": [],
      "intercept": true,
      "level": 0.95,
      "se": "HC1",
      "subset": null,
      "weights": null,
      "x": [
        "post_ever"
      ],
      "y": "prop_attn"
    }

    Figuras call_d2120f49

    Chave	Valor	Valor bruto	Erro-padrão	IC
    coef.post_ever Coefficient on post_ever in the regression of prop_attn	0,0158066	0.015806564832344536	0,009934090.009934089881688054	-0,00395194 – 0,0355651

    Diagnósticos

    • n_obs = 161161
    • k = 66
    • r2 = 0,634930.6349304523908477
    • df_resid = 155155
    • se_type = "cluster"
    • n_clusters = 8484

    Tabelas (1)

    {
      "coefficients": [
        {
          "ci_high": 0.015480831110948137,
          "ci_low": -0.008914338869894334,
          "coef": 0.003283246120526902,
          "p": 0.5938243126501479,
          "se": 0.006132645371716379,
          "t": 0.5353719188898739,
          "term": "Intercept"
        },
        {
          "ci_high": 0.03556507005966711,
          "ci_low": -0.003951940394978041,
          "coef": 0.015806564832344536,
          "p": 0.11537903080899661,
          "se": 0.009934089881688054,
          "t": 1.5911437303865625,
          "term": "post_ever"
        },
        {
          "ci_high": 0.02293239822557311,
          "ci_low": -0.003816326954633128,
          "coef": 0.00955803563546999,
          "p": 0.15894206768761424,
          "se": 0.006724300171080017,
          "t": 1.4214171575173502,
          "term": "post"
        },
        {
          "ci_high": 0.011066799507012181,
          "ci_low": -0.025736980833463766,
          "coef": -0.007335090663225794,
          "p": 0.4301489974372446,
          "se": 0.009252017237179814,
          "t": -0.7928098786661648,
          "term": "ever_treated"
        },
        {
          "ci_high": 0.16129050921864613,
          "ci_low": 0.10704104846256378,
          "coef": 0.13416577884060496,
          "p": 1.3830436484851995e-15,
          "se": 0.013637646496628695,
          "t": 9.837898267400575,
          "term": "sustain"
        },
        {
          "ci_high": 0.00004079585210277754,
          "ci_low": -0.00004691572452348151,
          "coef": -0.000003059936210351986,
          "p": 0.8899640528329869,
          "se": 0.000022049610429662515,
          "t": -0.13877506907040718,
          "term": "pct_rev_change"
        }
      ]
    }

    Digests dos dados

    ref_panel 989d1ac3292383f244bb409f0450bfbb2e1ce53f13b12a97d1d2eef26b3b980e

    sha256 do resultado

    982c8ae77ecb35f99e23b40dc8fafcd1ac978073e6ce3d7396c5c0a79df6d4f3

    Resultado mostrado ao modelo
15. 14Turno do modelo
    call_534b3f33referenceFim: 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.post_ever = 0,01581. Reference path transcribed from the item's own estimation script.",
        "caveats": [],
        "figure_refs": [
          "call_d2120f49:coef.post_ever"
        ]
      }

    Mensagens (formato neutro)Instruções do sistemaFerramentas oferecidas
16. 15Guarda
    resposta aceita

    Números encontrados

    0,01581

    Sem lastro

    —

    Não citados

    —

    1 nova(s) tentativa(s) restante(s)
17. 16Resposta

    Estimativa (infer.ols): coef.post_ever = 0,01581. Reference path transcribed from the item's own estimation script.

    Figuras citadas: call_d2120f49:coef.post_ever · Parada: answered

    Figura da resposta: call_d2120f49:coef.post_ever = 0,01580660.015806564832344536 (se 0,009934090.009934089881688054)
18. 17Pontuação
    #0resposta com lastropassouobrigatóriaevery number backed
19. 18Pontuação
    #1figura da resposta próximapassouobrigatória0.01580656483 in [-0.003664251336, 0.035277381]?
20. 19Pontuação
    #2figura da resposta próximapassouregistrada0.01580656483 vs 0.015806564832344584
21. 20Pontuação
    #3figura da resposta próximapassouregistrada0.009934089882 vs 0.00993408988168804

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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