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Data

Share of food products in total merchandise exports

What you should know about this indicator

How is this data described by its producer?

Food comprises the commodities in SITC (Rev. 3) sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and division 22 (oil seeds, oil nuts, and oil kernels). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.

Aggregation method:

Weighted average

Statistical concept and methodology:

Methodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics – Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/ Statistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.

Development relevance:

This indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.

Limitations and exceptions:

Previous editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.

Other notes:

Merchandise export shares may not sum to 100 percent because of unclassified trade.

Source
UN Comtrade, WITS, and World Bank staff estimates (2026) – processed by Our World in Data
Last updated
February 27, 2026
Next expected update
February 2027
Date range
1962–2024
Unit
% of merchandise exports

Related research and writing

A dark blue background with a lighter blue world map superimposed over it. Yellow text that says Trade and Globalization by Our World in Data

Trade and Globalization

Esteban Ortiz-Ospina, Bertha Rohenkohl, Veronika Samborska, Simon Van Teutem, Diana Beltekian, and Max Roser

Sources and processing

This data is based on the following sources

UN Comtrade, WITS, and World Bank staff estimates – World Development Indicators

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

Retrieved on
February 27, 2026
Retrieved from
https://data.worldbank.org/indicator/TX.VAL.FOOD.ZS.UN
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.
Comtrade database, United Nations (UN), publisher: UN Statistics Division;
World Integrated Trade Solution system (WITS);
Staff estimates, World Bank (WB). Indicator TX.VAL.FOOD.ZS.UN (https://data.worldbank.org/indicator/TX.VAL.FOOD.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades.WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability.The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization.WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally.Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.

Retrieved on
February 27, 2026
Retrieved from
https://data.worldbank.org/indicator/TX.VAL.FOOD.ZS.UN
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.
Comtrade database, United Nations (UN), publisher: UN Statistics Division;
World Integrated Trade Solution system (WITS);
Staff estimates, World Bank (WB). Indicator TX.VAL.FOOD.ZS.UN (https://data.worldbank.org/indicator/TX.VAL.FOOD.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-02-27.

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

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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: Share of food products in total merchandise exports”. Our World in Data (2026). Data adapted from UN Comtrade, WITS, and World Bank staff estimates. Retrieved from https://archive.ourworldindata.org/20260512-185716/grapher/share-food-exports.html [online resource] (archived on May 12, 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:

UN Comtrade, WITS, and World Bank staff estimates (2026) – processed by Our World in Data

Full citation

UN Comtrade, WITS, and World Bank staff estimates (2026) – processed by Our World in Data. “Share of food products in total merchandise exports” [dataset]. UN Comtrade, WITS, and World Bank staff estimates, “World Development Indicators 125” [original data]. Retrieved May 15, 2026 from https://archive.ourworldindata.org/20260512-185716/grapher/share-food-exports.html (archived on May 12, 2026).

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Data URL (CSV format)
https://ourworldindata.org/grapher/share-food-exports.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/share-food-exports.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/share-food-exports.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/share-food-exports.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/share-food-exports.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/share-food-exports.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/share-food-exports.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/share-food-exports.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear