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Data

Imports of personal protective equipment per capita

See all data and research on:
PandemicsTrade & Globalization

What you should know about this indicator

  • Products included are: "Breathing appliances and gas masks", "Gloves, mittens and mitts (knitted or crocheted)", "Gloves, mittens and mitts (not knitted or crocheted)", "Vulcanized (other than hard rubber), gloves other than surgical gloves", "Plastics in clothing", "Eyewear", "Surgical gloves", and "Textiles".
  • The currency conversion factors used in Comtrade are domestic currency per US Dollar (period average) from IMF.
Imports of personal protective equipment per capita
Total imports of pandemic-related customs (commodity codes: 392620, 401519, 401511, 611610, 621600, 630790, 902000, 900490).
Source
UN Comtrade (2023); Population based on various sources (2023) – with major processing by Our World in Data
Last updated
August 10, 2023
Date range
1988–2022
Unit
current US$

Sources and processing

This data is based on the following sources

UN Comtrade – Pandemics imports

The United Nations Comtrade database aggregates detailed global annual and monthly trade statistics by product and trading partner for use by governments, academia, research institutes, and enterprises. Data compiled by the United Nations Statistics Division covers approximately 200 countries and represents more than 99% of the world's merchandise trade. Information can be extracted in a variety of formats, including API developer tools for integration into enterprise applications and workflows.

Subscribers receive access to additional functionality to improve efficiency and specificity.

The conversion factors used in Comtrade are domestic currency per US Dollar (period average) from IMF.

Contact subscriptions@un.org for information and licensing.

Retrieved on
August 2, 2023
Retrieved from
https://comtradeplus.un.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.
United Nations, Comtrade Database (accessed online https://comtrade.un.org, 2023).

The United Nations Comtrade database aggregates detailed global annual and monthly trade statistics by product and trading partner for use by governments, academia, research institutes, and enterprises. Data compiled by the United Nations Statistics Division covers approximately 200 countries and represents more than 99% of the world's merchandise trade. Information can be extracted in a variety of formats, including API developer tools for integration into enterprise applications and workflows.

Subscribers receive access to additional functionality to improve efficiency and specificity.

The conversion factors used in Comtrade are domestic currency per US Dollar (period average) from IMF.

Contact subscriptions@un.org for information and licensing.

Retrieved on
August 2, 2023
Retrieved from
https://comtradeplus.un.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.
United Nations, Comtrade Database (accessed online https://comtrade.un.org, 2023).

Various sources – Population

Our World in Data builds and maintains a long-run dataset on population by country, region, and for the world, based on various sources.

You can find more information on these sources and how our time series is constructed on this page: https://ourworldindata.org/population-sources

Retrieved on
March 31, 2023
Retrieved from
https://ourworldindata.org/population-sources
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.
The long-run data on population is based on various sources, described on this page: https://ourworldindata.org/population-sources

Our World in Data builds and maintains a long-run dataset on population by country, region, and for the world, based on various sources.

You can find more information on these sources and how our time series is constructed on this page: https://ourworldindata.org/population-sources

Retrieved on
March 31, 2023
Retrieved from
https://ourworldindata.org/population-sources
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.
The long-run data on population is based on various sources, described on this page: https://ourworldindata.org/population-sources

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

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: Imports of personal protective equipment per capita”, part of the following publication: Saloni Dattani, Lucas Rodés-Guirao, Edouard Mathieu, Hannah Ritchie, and Max Roser (2023) - “Pandemics”. Data adapted from UN Comtrade, Various sources. Retrieved from https://archive.ourworldindata.org/20260304-094028/grapher/imports-of-ppe-related-products-per-capita.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:

UN Comtrade (2023); Population based on various sources (2023) – with major processing by Our World in Data

Full citation

UN Comtrade (2023); Population based on various sources (2023) – with major processing by Our World in Data. “Imports of personal protective equipment per capita” [dataset]. UN Comtrade, “Pandemics imports”; Various sources, “Population” [original data]. Retrieved April 1, 2026 from https://archive.ourworldindata.org/20260304-094028/grapher/imports-of-ppe-related-products-per-capita.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/imports-of-ppe-related-products-per-capita.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/imports-of-ppe-related-products-per-capita.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/imports-of-ppe-related-products-per-capita.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/imports-of-ppe-related-products-per-capita.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/imports-of-ppe-related-products-per-capita.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/imports-of-ppe-related-products-per-capita.csv?v=1&csvType=full&useColumnShortNames=false")

# Fetch the metadata
metadata <- fromJSON("https://ourworldindata.org/grapher/imports-of-ppe-related-products-per-capita.metadata.json?v=1&csvType=full&useColumnShortNames=false")
Stata
import delimited "https://ourworldindata.org/grapher/imports-of-ppe-related-products-per-capita.csv?v=1&csvType=full&useColumnShortNames=false", encoding("utf-8") clear