Wid_data Csv ((better)) Download
WID contains standardized, reproducible estimates of inequality indicators — such as top income shares, wealth thresholds, public capital, and gender disparities — often spanning more than a century. The data is harmonized to allow cross-country comparisons and is regularly updated with contributions from hundreds of researchers.
# Load the CSV file wid_data = pd.read_csv('wid_data.csv')
For this example, we will select a few variables, including the Gini coefficient and the share of wealth held by the top 10% of the population.
Offering a direct "JSON" or "Parquet" export option would be a huge upgrade for those of us building dashboards or apps on top of this data. But for a CSV workflow, it gets the job done efficiently.
Alternatively, use the page for full datasets (e.g., all countries, all years) – available as large CSV archives.
WID contains standardized, reproducible estimates of inequality indicators — such as top income shares, wealth thresholds, public capital, and gender disparities — often spanning more than a century. The data is harmonized to allow cross-country comparisons and is regularly updated with contributions from hundreds of researchers.
# Load the CSV file wid_data = pd.read_csv('wid_data.csv')
For this example, we will select a few variables, including the Gini coefficient and the share of wealth held by the top 10% of the population.
Offering a direct "JSON" or "Parquet" export option would be a huge upgrade for those of us building dashboards or apps on top of this data. But for a CSV workflow, it gets the job done efficiently.
Alternatively, use the page for full datasets (e.g., all countries, all years) – available as large CSV archives.
