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etl
etl
 
 
.gitattributes
.gitattributes
 
 
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.gitignore
 
 
README.md
README.md
 
 
datapackage.json
datapackage.json
 
 
ddf--concepts.csv
ddf--concepts.csv
 
 
ddf--datapoints--annual_income--by--person--time.csv
ddf--datapoints--annual_income--by--person--time.csv
 
 
ddf--datapoints--average_age_of_dollar_billionaires_years--by--country--time.csv
ddf--datapoints--average_age_of_dollar_billionaires_years--by--country--time.csv
 
 
ddf--datapoints--daily_income--by--person--time.csv
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ddf--datapoints--dollar_billionaires_per_million_people--by--country--time.csv
ddf--datapoints--dollar_billionaires_per_million_people--by--country--time.csv
 
 
ddf--datapoints--total_number_of_dollar_billionaires--by--country--time.csv
ddf--datapoints--total_number_of_dollar_billionaires--by--country--time.csv
 
 
ddf--datapoints--worth--by--person--time.csv
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ddf--entities--geo--country.csv
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ddf--entities--geo--g77_and_oecd_countries.csv
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ddf--entities--geo--global.csv
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ddf--entities--geo--income_3groups.csv
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ddf--entities--geo--income_groups.csv
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ddf--entities--geo--landlocked.csv
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ddf--entities--geo--main_religion_2008.csv
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ddf--entities--geo--un_sdg_ldc.csv
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ddf--entities--geo--un_sdg_region.csv
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ddf--entities--geo--unhcr_region.csv
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ddf--entities--geo--unicef_region.csv
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ddf--entities--geo--west_and_rest.csv
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ddf--entities--geo.csv
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ddf--entities--person.csv
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methodology.md
methodology.md
 
 

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Gapminder Billionaires Dataset

This repository contains a DDF dataset on dollar billionaires, built by combining and harmonizing data from:

If you are new to DDF:

Key documentation

What this dataset includes

The dataset contains:

  • Person-level entity data (billionaires)
  • Person-level time series (e.g. worth, annual_income, daily_income)
  • Country-level derived indicators (e.g. billionaire counts, billionaires per million, average age)

You can inspect available files directly in the repository root (for datapoints/entities) and in ddf--concepts.csv for definitions and metadata.

Data processing summary

At a high level, updates follow this process:

  1. Download source data (Hurun + Forbes)
  2. Transform each source to clean intermediate tables
  3. Generate embeddings for matching
  4. Run MCP/LLM-assisted matching to create mappings
  5. Human review of mappings
  6. Build final DDF outputs

See full instructions in etl/README.md.

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