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Data

Unadjusted gender wage gap including unemployed

About this data

Source
Sandefur (2018) – processed by Our World in Data
Last updated
June 2, 2018
Date range
2014–2014
Unit
%

Sources and processing

This data is based on the following sources

Sandefur – Chart of the week: Gender pay gaps around the world are bigger than you think, and have almost nothing to do with girls schooling

Retrieved on
June 2, 2018
Retrieved from
https://www.cgdev.org/blog/chart-week-gender-pay-gaps-around-world-are-bigger-you-think-and-have-almost-nothing-do-girls
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.
Sandefur, J. (2018). Chart of the Week: Gender Pay Gaps around the World Are Bigger Than You Think, and Have Almost Nothing to Do with Girls Schooling. Center for Global Development.
Retrieved on
June 2, 2018
Retrieved from
https://www.cgdev.org/blog/chart-week-gender-pay-gaps-around-world-are-bigger-you-think-and-have-almost-nothing-do-girls
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.
Sandefur, J. (2018). Chart of the Week: Gender Pay Gaps around the World Are Bigger Than You Think, and Have Almost Nothing to Do with Girls Schooling. Center for Global Development.

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: Unadjusted gender wage gap including unemployed”. Our World in Data (2026). Data adapted from Sandefur. Retrieved from https://archive.ourworldindata.org/20260511-092124/grapher/unadjusted-gender-wage-gap-including-unemployed.html [online resource] (archived on May 11, 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:

Sandefur (2018) – processed by Our World in Data

Full citation

Sandefur (2018) – processed by Our World in Data. “Unadjusted gender wage gap including unemployed” [dataset]. Sandefur, “Chart of the week: Gender pay gaps around the world are bigger than you think, and have almost nothing to do with girls schooling” [original data]. Retrieved May 14, 2026 from https://archive.ourworldindata.org/20260511-092124/grapher/unadjusted-gender-wage-gap-including-unemployed.html (archived on May 11, 2026).

Download

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Download the data shown in this chart as a ZIP file containing a CSV file, metadata in JSON format, and a README. The CSV file can be opened in Excel, Google Sheets, and other data analysis tools.

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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/unadjusted-gender-wage-gap-including-unemployed.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/unadjusted-gender-wage-gap-including-unemployed.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/unadjusted-gender-wage-gap-including-unemployed.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/unadjusted-gender-wage-gap-including-unemployed.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/unadjusted-gender-wage-gap-including-unemployed.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

# Fetch the data
df <- read.csv("https://ourworldindata.org/grapher/unadjusted-gender-wage-gap-including-unemployed.csv?v=1&csvType=full&useColumnShortNames=false")

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