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Deaths from famines by region and year

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Famines

What you should know about this indicator

  • WPF defines a famine as mass mortality due to mass starvation, with mass starvation being the "destruction, deprivation or loss of objects and activities required for survival".
  • WPF coded the most credible estimate of the number of deaths across sources. If there were several equally credible estimates, WPF used their median.
  • The 1910–1919 famine in British Somaliland and the African Red Sea Region (Sudan, Northern Ethiopia, Eritrea, and Djibouti) is treated as a single event because the 100,000+ mortality estimate applies to the entire region, not individual areas.
  • For the Ottoman Empire (1894–1896), East Africa (1896–1900), and the combined Somaliland–African Red Sea Region famine (1910–1919), the 100,000 death estimate is a minimum, meaning the actual death toll was likely higher.

How is this data described by its producer?

Famines are assessed based on severity, magnitude, and duration. Magnitude, measured as the total number of excess deaths, was used to determine inclusion in the catalogue. A threshold of 100,000 deaths was applied due to limited demographic research on proportional death rate increases.

Deaths from famines by region and year
Deaths in famines that are estimated to have killed 100,000 people or more.
Source
World Peace Foundation (2025) – processed by Our World in Data
Last updated
January 17, 2025
Next expected update
May 2026
Date range
1870–2023
Unit
deaths

Sources and processing

This data is based on the following sources

World Peace Foundation – The WPF Famine Dataset

The World Peace Foundation has compiled a comprehensive dataset cataloging famines and mass starvation events since 1870. Their main dataset focuses on events that caused 100,000 or more deaths.

The dataset faces several methodological challenges that require careful consideration. Historical data quality varies significantly across different periods and regions, making direct comparisons challenging. Different measurement methods and inconsistent data collection practices further complicate the analysis. A particularly notable observation is that the worse a humanitarian emergency becomes, the more difficult it becomes to gather reliable data about it. These challenges are compounded by the complexity of defining famine boundaries and categorizing different types of mass starvation events.

The framework for defining famines in this dataset encompasses three main categories: conventional famines driven by food crises, mass starvation caused by war or genocide, and massive humanitarian emergencies. These events are distinguished from chronic poverty by being distinct episodes rather than ongoing conditions. The methodology uses a threshold of 100,000 deaths for practical purposes, considering both direct starvation deaths and related health crisis mortality. The dataset has evolved from using "lowest credible estimate" to "most credible estimate" for death tolls, and employs placeholder estimates of "100,000+" when exact figures are unavailable.

The classification of famine causes follows a structured approach, identifying immediate triggers, contributory factors, and structural causes. The dataset recognizes four main triggers: adverse climate, government policies, armed conflict, and genocide. Importantly, the authors note that famine causes are often complex and interconnected, rarely attributable to a single factor.

Given these methodological considerations, the authors emphasize that this compilation should be viewed more as a catalogue than a strict dataset, suitable for drawing general conclusions rather than precise statistical analyses. The dataset remains open for expert review and input, functioning as a living document that can be updated as new information becomes available.

Retrieved on
October 3, 2025
Retrieved from
https://worldpeacefoundation.org/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Historic Famines dataset. World Peace Foundation (2025).

The World Peace Foundation has compiled a comprehensive dataset cataloging famines and mass starvation events since 1870. Their main dataset focuses on events that caused 100,000 or more deaths.

The dataset faces several methodological challenges that require careful consideration. Historical data quality varies significantly across different periods and regions, making direct comparisons challenging. Different measurement methods and inconsistent data collection practices further complicate the analysis. A particularly notable observation is that the worse a humanitarian emergency becomes, the more difficult it becomes to gather reliable data about it. These challenges are compounded by the complexity of defining famine boundaries and categorizing different types of mass starvation events.

The framework for defining famines in this dataset encompasses three main categories: conventional famines driven by food crises, mass starvation caused by war or genocide, and massive humanitarian emergencies. These events are distinguished from chronic poverty by being distinct episodes rather than ongoing conditions. The methodology uses a threshold of 100,000 deaths for practical purposes, considering both direct starvation deaths and related health crisis mortality. The dataset has evolved from using "lowest credible estimate" to "most credible estimate" for death tolls, and employs placeholder estimates of "100,000+" when exact figures are unavailable.

The classification of famine causes follows a structured approach, identifying immediate triggers, contributory factors, and structural causes. The dataset recognizes four main triggers: adverse climate, government policies, armed conflict, and genocide. Importantly, the authors note that famine causes are often complex and interconnected, rarely attributable to a single factor.

Given these methodological considerations, the authors emphasize that this compilation should be viewed more as a catalogue than a strict dataset, suitable for drawing general conclusions rather than precise statistical analyses. The dataset remains open for expert review and input, functioning as a living document that can be updated as new information becomes available.

Retrieved on
October 3, 2025
Retrieved from
https://worldpeacefoundation.org/
Citation
This is the citation of the original data obtained from the source, prior to any processing or adaptation by Our World in Data. To cite data downloaded from this page, please use the suggested citation given in Reuse This Work below.
Historic Famines dataset. World Peace Foundation (2025).

How we process data at Our World in Data

All data and visualizations on Our World in Data rely on data sourced from one or several original data providers. Preparing this original data involves several processing steps. Depending on the data, this can include standardizing country names and world region definitions, converting units, calculating derived indicators such as per capita measures, as well as adding or adapting metadata such as the name or the description given to an indicator.

At the link below you can find a detailed description of the structure of our data pipeline, including links to all the code used to prepare data across Our World in Data.

Read about our data pipeline
Notes on our processing step for this indicator

The deaths were assumed to be evenly distributed over the duration of each famine, except for the famine in China between 1958 and 1962, where the source provides a year-by-year breakdown of mortality.

Reuse this work

  • All data produced by third-party providers and made available by Our World in Data are subject to the license terms from the original providers. Our work would not be possible without the data providers we rely on, so we ask you to always cite them appropriately (see below). This is crucial to allow data providers to continue doing their work, enhancing, maintaining and updating valuable data.
  • All data, visualizations, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

Citations

How to cite this page

To cite this page overall, including any descriptions, FAQs or explanations of the data authored by Our World in Data, please use the following citation:

“Data Page: Deaths from famines by region and year”, part of the following publication: Bastian Herre, Veronika Samborska, Joe Hasell, and Max Roser (2017) - “Famines”. Data adapted from World Peace Foundation. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/deaths-from-famines-by-region-and-year.html [online resource] (archived on March 4, 2026).

How to cite this data

In-line citationIf you have limited space (e.g. in data visualizations), you can use this abbreviated in-line citation:

World Peace Foundation (2025) – processed by Our World in Data

Full citation

World Peace Foundation (2025) – processed by Our World in Data. “Deaths from famines by region and year” [dataset]. World Peace Foundation, “The WPF Famine Dataset” [original data]. Retrieved April 2, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/deaths-from-famines-by-region-and-year.html (archived on March 4, 2026).

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Data API

Use these URLs to programmatically access this chart's data and configure your requests with the options below. Our documentation provides more information on how to use the API, and you can find a few code examples below.

Data URL (CSV format)
https://ourworldindata.org/grapher/deaths-from-famines-by-region-and-year.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/deaths-from-famines-by-region-and-year.metadata.json?v=1&csvType=full&useColumnShortNames=false

Code examples

Examples of how to load this data into different data analysis tools.

Excel / Google Sheets
=IMPORTDATA("https://ourworldindata.org/grapher/deaths-from-famines-by-region-and-year.csv?v=1&csvType=full&useColumnShortNames=false")
Python with Pandas
import pandas as pd
import requests

# Fetch the data.
df = pd.read_csv("https://ourworldindata.org/grapher/deaths-from-famines-by-region-and-year.csv?v=1&csvType=full&useColumnShortNames=false", storage_options = {'User-Agent': 'Our World In Data data fetch/1.0'})

# Fetch the metadata
metadata = requests.get("https://ourworldindata.org/grapher/deaths-from-famines-by-region-and-year.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/deaths-from-famines-by-region-and-year.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/deaths-from-famines-by-region-and-year.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/deaths-from-famines-by-region-and-year.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear