Population of Each Continent In 10,000 BC

The map above shows a very rough estimate of the population of each continent in 10,000 BC.

The numbers come form Our World In Data, which uses HYDE version 3.3.

Here is list of estimated populations for people living within the borders of modern day states in 10,000 BC.

Entity	Population (historical)
World	4,501,152
Upper-middle-income countries	2,550,997
North America	1,184,755
Asia	1,183,783
South America	1,097,849
High-income countries	1,017,355
Mexico	810,851
Lower-middle-income countries	763,498
USSR	727,181
Europe	481,591
Brazil	415,018
Oceania	324,198
Australia	314,500
Russia	257,671
Peru	242,217
United States	233,969
Africa	228,973
China	217,311
Low-income countries	164,616
Colombia	128,828
Kazakhstan	108,469
Nepal	107,405
Bolivia	104,718
Uzbekistan	102,306
Ukraine	98,793
India	84,810
European Union (27)	80,143
Ecuador	69,685
Venezuela	69,396
Guatemala	52,148
Turkey	39,506
Iran	38,101
Iraq	35,992
Chile	34,799
Sri Lanka	32,212
Nicaragua	30,733
Kyrgyzstan	28,350
Argentina	27,969
Syria	26,928
Georgia	25,145
Tajikistan	24,652
Democratic Republic of Congo	21,905
Vietnam	21,429
Indonesia	20,971
Azerbaijan	20,584
Belarus	20,549
Lebanon	20,511
Myanmar	19,798
Turkmenistan	19,721
Spain	18,221
Morocco	17,352
Saudi Arabia	17,339
Nigeria	17,305
Canada	17,259
France	16,785
Ethiopia	16,296
Philippines	16,146
Mongolia	14,955
Afghanistan	14,737
Panama	14,392
Thailand	14,011
Egypt	13,668
Honduras	13,390
Uganda	13,183
Italy	12,658
Algeria	12,090
Israel	11,936
Sudan	11,863
Jordan	11,376
Moldova	11,216
South Korea	10,786
Laos	10,775
North Korea	10,773
Cambodia	10,453
Kenya	10,114
Papua New Guinea	9,694
Chad	9,682
Armenia	9,614
Bangladesh	9,600
Niger	9,543
El Salvador	8,673
Pakistan	8,500
Tanzania	7,899
Portugal	7,160
Libya	6,936
Germany	6,576
Taiwan	5,579
Japan	5,202
Tunisia	4,837
Cote d'Ivoire	4,806
Serbia and Montenegro	4,363
Ghana	4,013
Mali	3,871
Burkina Faso	3,678
Paraguay	3,500
Somalia	3,470
Togo	3,271
Cameroon	3,224
Eritrea	3,098
Mauritania	2,995
Romania	2,799
Malawi	2,762
Liberia	2,729
Oman	2,644
Benin	2,557
Guinea	2,492
Bosnia and Herzegovina	2,349
Central African Republic	2,265
United Kingdom	2,189
Mozambique	2,155
Costa Rica	2,115
Hungary	2,109
Equatorial Guinea	2,049
Congo	1,997
Uruguay	1,705
Slovakia	1,679
Hong Kong	1,558
Austria	1,465
Liechtenstein	1,363
Slovenia	1,308
Netherlands	1,220
Albania	1,199
Croatia	1,198
Bulgaria	1,165
Yemen	981
Cyprus	922
Kuwait	912
Belgium	888
Luxembourg	791
Dominican Republic	755
Malaysia	754
Zimbabwe	749
Senegal	734
North Macedonia	725
Poland	699
Czechia	697
Zambia	668
Burundi	584
Denmark	540
Rwanda	539
Switzerland	507
Greece	494
Finland	421
United Arab Emirates	409
East Timor	387
Angola	379
Gabon	346
Cuba	241
Norway	237
Sweden	237
Belize	200
Guinea-Bissau	194
Lesotho	146
Djibouti	143
South Africa	90
Eswatini	88
Bahrain	77
Sierra Leone	77
Lithuania	45
Namibia	44
Bhutan	41
Latvia	38
Botswana	28
Estonia	28
Gambia	26
Andorra	25
Guadeloupe	12
Brunei	8
Faroe Islands	8
Iceland	8
Madagascar	6
Qatar	6
Guyana	5
Saint Lucia	5
Grenada	4
New Caledonia	3
Suriname	3
Sao Tome and Principe	2
Antigua and Barbuda	1

What is the HYDE 3.3 dataset?

• HYDE stands for the “History Database of the Global Environment”. The version 3.3 is the latest major release (2023) of this long-term global, gridded dataset of human population, urban/rural splits, population density, built-up area, and land-use (cropland, pasture, rangeland, etc).
• It is maintained by e.g. the PBL Netherlands Environmental Assessment Agency in collaboration with Utrecht University.
• It integrates historical population estimates + land use / built-up area, and represents them spatially (on a grid) as well as temporally (over many millennia).

Key Features & Coverage

Here are main technical / structural details of HYDE 3.3:

• Temporal coverage: From ~10,000 BCE (10 000 years before the Common Era) up to 2023 CE.
• Spatial resolution: 5 arc-minutes (which is about ~85 km² at the equator) grid cells.
• Variables / Layers: Includes total population, urban population, rural population, population density, built-up area (for population side). On the land-use side: cropland (with distinctions such as irrigated vs rain-fed, rice vs non-rice), grazing lands subdivided into intensively-used pasture, converted rangeland, non-converted rangeland, etc.
• Scenario variants: HYDE 3.3 contains a “Baseline” estimate plus “Lower” and “Upper” bound scenarios (to express uncertainty), though some tools currently expose only the Baseline version. (as we do above)
• Data format: The dataset is provided as grid maps (for example ESRI ASCII grid files) and is globally gridded.
• Updates from previous versions: HYDE 3.3 builds on version 3.2, extending the time to 2023 and refining data sources (including remote sensing and archaeological/ radiocarbon data for agriculture onset) and land-use distinctions.

Limitations / Things to watch:

• The dataset is gridded at ~5 arc-minutes resolution, which means for country-level historic data you’ll need to aggregate grid cells to country boundaries. The dataset itself is not a clean “by country” table of population for each year (though tools may provide that).
• The dataset’s focus is broad (population + land use) rather than diplomatic or political events,  so it doesn’t record things like “country X recognised the United States in year Y” but rather gives population context.
• The uncertainty in early years is large. For very early historical periods (especially before reliable census/records) the estimates are modelled. Thus, for older recognition events (19th century, early 20th), the population numbers may be especially uncertain.

Things to Note / Caveats

• Boundaries and political geography: HYDE deals with population and land use, but does not inherently handle changes in country boundaries, new states, colonial status, etc.
• Urban vs rural definitions: HYDE distinguishes urban vs rural population and built-up area, but the definitions vary historically and may have uncertainties especially for older years.
• Uncertainty/Scenarios: The lower/upper scenario variants reflect uncertainty; if you simply use the baseline you’re accepting one estimate but you might want to look at range.
• Data resolution: ~85 km² at equator means smaller countries or fine-scale analysis may be less precise. Also, near the poles resolution in area changes.
• The earliest years (10,000 BCE etc) are highly modelled; for more recent times (post-1900) the data are more reliable.

What do you think?