IHS Markit datasets available via the Data Lake
The ESG Sovereign Country Scores cover 41 risks at the sovereign level that are relevant to ESG. The scores are backed by rigorous qualitative research and codified definitions for each category of risk, with a statistical approach to maintaining the overall structure of the dataset. The dataset covers 211 countries with 10 years of history updated quarterly. You need to use Spark to read the datasets in this package, as they contain nested structures (list of structs) that cannot be read with the data lake dataframe api call.
REST, Python, S3 Proxy, SQL
- ESG Scores
- Human Rights
- Risk Scores
- Sovereign Risk
- Country Risk
|Dataset Name||Dataset Description||Data Format||Publishing Frequency|
|ESG Human Rights Scores||The human rights scores provide a 12-month forecast for five categories of risk that estimate the extent to which human rights violations pose a reputational risk for a company operating in a country.||Parquet||Quarterly|
|ESG Sovereign Country Scores||A unique country scoring system that enables users to compare and contrast strategic risk in up to 211 countries worldwide. The system comprises six overall standard risk categories (Political, Economic, Legal, Taxation, Operational and Security) that encompass the spectrum of risks encountered by operators. IHS Markit Country Risk numeration reflects qualitative analysis and forecasts. The scores represent our forecast for risks over the coming 12 months. These are determined based on the same largely qualitative indicators and guidance that drive the written analysis and forecasts, alongside specific guidance on score bands. You need to use Spark to read this dataset, as it contains nested structures (list of structs) that cannot be read with the dataframe data lake api call.||Parquet||Quarterly|
|ESG Economic Inputs||The economic input scores provide a 12-month forecast for eight categories of risk that estimate the extent to which economic factors (such as costs, regulations and availability of labour) will impact company operating in a country. You need to use Spark to read this dataset, as it contains nested structures (list of structs) that cannot be read with the dataframe data lake api call.||Parquet||Quarterly|
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