Income per person since 1800: two centuries of prosperity

Commercial use OKFreshLicense: CC-BY-4.0

The full snapshot isn't published yet — check back after the next refresh.Download sample CSV Download sample JSONSample rows only (up to 20) — not the complete dataset.

Value-added panel of Gapminder's Systema Globalis long income series (keyless GitHub raw CSVs, CC BY 4.0): GDP per capita in PPP-adjusted constant international dollars for 203 countries, 1800-2019 (some reach back centuries further), with derived signals — trailing 10-year and 50-year growth and the within-year income rank. The long-run prosperity layer for growth, convergence and development models; joins on country_code with the catalog's other country-year panels.

Source: Gapminder Systema Globalis income intelligence
Rows: 50,215
Columns: 10
Source cadence: Yearly
Last refreshed: Oct 3, 2026
Theme: economics

Column	Type	Description
country_code	string	ISO 3166-1 alpha-3 country code, uppercase (Gapminder entities file field iso3166_1_alpha3; Kosovo keeps its geo id "KOS").
country_name	string	Country name as published in Gapminder's entities file.
region	string	Gapminder world_4region: americas, europe, asia or africa.
income_group	string	Gapminder income_3groups: high_income, middle_income or low_income.
year	integer	Reference year. The source publishes 1800-2019 for most countries; a few reach back centuries further (sparse historic estimates).
income_per_person	float	Income per person: GDP per capita in PPP-adjusted constant international dollars (Gapminder 'Income per person, long series' — 1990+ anchored to World Bank WDI, earlier years from the Maddison Project Database, Penn World Table and Gapminder historic estimates). (unit: PPP$)
income_growth_10y_pct	float	Trailing 10-year growth of income_per_person, in percent; null when the base year is absent from the source. (unit: %)
income_growth_50y_pct	float	Trailing 50-year growth of income_per_person, in percent; null when the base year is absent from the source. (unit: %)
income_rank	integer	Rank of income_per_person within the year (1 = highest). (unit: rank)
row_hash	string	Deterministic 12-hex row identity hash (country_code\|year).

First 10 sample rows — a preview, not the complete dataset.

country_code	country_name	region	income_group	year	income_per_person	income_growth_10y_pct	income_growth_50y_pct	income_rank	row_hash
ABW	Aruba	americas	high_income	1,800	876.5	—	—	101	a24f7ce7b0f1
ABW	Aruba	americas	high_income	1,801	876.5	—	—	101	3e4ea58d4e34
ABW	Aruba	americas	high_income	1,802	876.5	—	—	101	fd8a5dc66a68
ABW	Aruba	americas	high_income	1,803	876.5	—	—	101	89454277fcdd
ABW	Aruba	americas	high_income	1,804	876.5	—	—	101	51606e2a3508
ABW	Aruba	americas	high_income	1,805	876.5	—	—	101	ff7c3c91135d
ABW	Aruba	americas	high_income	1,806	876.5	—	—	101	293c0aa0aeb5
ABW	Aruba	americas	high_income	1,807	876.5	—	—	102	9d5560aec00e
ABW	Aruba	americas	high_income	1,808	876.5	—	—	101	426be1ef7291
ABW	Aruba	americas	high_income	1,809	876.5	—	—	100	d6da9c72ed2a

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/gapminder_income_intel/income_per_person_long_run" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/gapminder_income_intel/income_per_person_long_run").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/gapminder_income_intel/income_per_person_long_run

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261003T122238Z-b5544f178a82 and its content hash, so readers get exactly the data you used.

APA

Gapminder Systema Globalis income intelligence. (2026). Income per person since 1800: two centuries of prosperity [Data set, snapshot 20261003T122238Z-b5544f178a82, sha256 b5544f178a82]. Datazimuts. Retrieved 2026-10-05, from https://datazimuts.com/en/datasets/gapminder_income_intel/income_per_person_long_run?snapshot=20261003T122238Z-b5544f178a82

BibTeX

@misc{dz_gapminder_income_intel_income_per_person_b5544f17,
  title        = {{Income per person since 1800: two centuries of prosperity}},
  author       = {{Gapminder Systema Globalis income intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/gapminder_income_intel/income_per_person_long_run?snapshot=20261003T122238Z-b5544f178a82}},
  note         = {Snapshot 20261003T122238Z-b5544f178a82, sha256 b5544f178a8213e6389b97734b455e289e38b09adbc8d5c90158ae1c02cb901b; accessed 2026-10-05}
}

Embed a table or a chart

Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.

Type

X axis

Value

Aggregate

Theme

HTML

<iframe src="https://datazimuts.com/embed/chart?dataset=gapminder_income_intel%2Fincome_per_person_long_run&lang=en&theme=auto&snapshot=20261003T122238Z-b5544f178a82&x=year&y=year&agg=avg" title="Income per person since 1800: two centuries of prosperity" width="100%" height="380" style="border:0" loading="lazy"></iframe>

Income per person since 1800: two centuries of prosperity

Follow this dataset

Cite this snapshot

Embed a table or a chart

Use your own AI key