[
  {
    "id": "10.3390/su162310240",
    "type": "article-journal",
    "title": "Evaluating the Anti-Corruption Factor in Environmental, Social, and Governance Indices by Sampling Large Financial Asset Management Firms",
    "author": [
      {
        "family": "Strang",
        "given": "Kenneth David"
      },
      {
        "family": "Vajjhala",
        "given": "Narasimha Rao"
      }
    ],
    "container-title": "Sustainability",
    "issued": {
      "date-parts": [
        [
          2024,
          11,
          22
        ]
      ]
    },
    "volume": "16",
    "issue": "23",
    "number": "10240",
    "publisher": "MDPI",
    "ISSN": "2071-1050",
    "DOI": "10.3390/su162310240",
    "URL": "https://www.narasimharao.net/research/esg-ratings-anti-corruption-asset-management-firms/",
    "abstract": "Current ESG indices suffer from incomplete and inconsistent data, with some factors irrelevant to specific industries. Regulators emphasize CO2 emissions reporting for finance/insurance firms, yet rented offices and governance issues like money laundering prove more material. This study examined USD 1+ trillion asset management firms using AI to collect undisclosed legal decisions measuring anti-corruption governance (GRI 206-1). Bayesian correlation with bootstrapping revealed ESG ratings failed reflecting legal cases (BF+0 odds ratio: 3005, 99% CI: 0.617–0.965). Misconduct fines correlated significantly with arbitration cases (Vovk-Selke p-ratio: 4411), yet most ESG scores diverged for identical firms. Three corroborating studies identified specific sample firms as unethical. The authors recommend deeper investigation of implications regarding public interest and stakeholder theory.",
    "keyword": "ESG ratings, ESG rating divergence, anti-corruption, corporate governance, GRI 206-1, asset management firms, financial services, money laundering, Bayesian correlation, AI data collection, stakeholder theory",
    "language": "en"
  }
]