Our World
in Data
Oxford Martin School logoUniversity of Oxford logoGlobal Change Data Lab logo
Data

Number of new book titles published per million people

About this data

Number of new book titles published per million people
The number of new book titles publisher per year, per million inhabitants in a given country for the period 1500 - 2010.
Source
Various sources (2017) – processed by Our World in Data
Last updated
September 9, 2017
Date range
1500–2009
Unit
Unique booktitles per million inhabitants

Sources and processing

This data is based on the following sources

Various sources – Clio-Infra - Human Capital

Clio-Infra is a collaborative project that provides historical data on global economic development. The Human Capital theme brings together datasets covering education, numeracy, and the founding of universities, compiled by individual researchers from central statistical agencies, historical reconstructions, and scholarly research.

This snapshot is an Our World in Data compilation of six Clio-Infra datasets on human capital:

Per-component notes:

  • Average years of education (van Leeuwen, van Leeuwen-Li 2015): Publisher source: Central statistical agencies, historical reconstructions.
  • Book titles per capita (Fink-Jensen 2015): Publisher source: Combination of datasets and publications that contain data on book production for different regions and periods. Includes CLIO Infra data.
  • Composite wellbeing index (Rijpma 2017): Publisher source: Composite measure constructed from 9 variables from the CLIO-Infra project.
  • Educational inequality gini coefficient (van Leeuwen and van Leeuwen-Li 2015): Publisher source: Central statistical agencies, historical reconstructions.
  • Numeracy (total) (Baten 2015): Publisher source: Reconstruction of data using a variety of sources, incl. scholarly research.
  • Universities founded (Foldvari 2015): Additional information: Author's note: The quantity and quality of available information on universities vary by period and geographical area. Even the definition of university is not universal: in some countries even colleges offering undergradute programmes can be named universities (Latin America, Japan, Near East). Please see source for further information. Publisher source: Central statistical agencies, historical reconstructions.
Retrieved on
September 9, 2017
Retrieved from
https://clio-infra.eu/
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.
Clio-Infra. (2015–2017). Human Capital datasets: van Leeuwen and van Leeuwen-Li (2015), Fink-Jensen (2015), Rijpma (2017), Baten (2015), and Foldvari (2015). International Institute of Social History.

Clio-Infra is a collaborative project that provides historical data on global economic development. The Human Capital theme brings together datasets covering education, numeracy, and the founding of universities, compiled by individual researchers from central statistical agencies, historical reconstructions, and scholarly research.

This snapshot is an Our World in Data compilation of six Clio-Infra datasets on human capital:

Per-component notes:

  • Average years of education (van Leeuwen, van Leeuwen-Li 2015): Publisher source: Central statistical agencies, historical reconstructions.
  • Book titles per capita (Fink-Jensen 2015): Publisher source: Combination of datasets and publications that contain data on book production for different regions and periods. Includes CLIO Infra data.
  • Composite wellbeing index (Rijpma 2017): Publisher source: Composite measure constructed from 9 variables from the CLIO-Infra project.
  • Educational inequality gini coefficient (van Leeuwen and van Leeuwen-Li 2015): Publisher source: Central statistical agencies, historical reconstructions.
  • Numeracy (total) (Baten 2015): Publisher source: Reconstruction of data using a variety of sources, incl. scholarly research.
  • Universities founded (Foldvari 2015): Additional information: Author's note: The quantity and quality of available information on universities vary by period and geographical area. Even the definition of university is not universal: in some countries even colleges offering undergradute programmes can be named universities (Latin America, Japan, Near East). Please see source for further information. Publisher source: Central statistical agencies, historical reconstructions.
Retrieved on
September 9, 2017
Retrieved from
https://clio-infra.eu/
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.
Clio-Infra. (2015–2017). Human Capital datasets: van Leeuwen and van Leeuwen-Li (2015), Fink-Jensen (2015), Rijpma (2017), Baten (2015), and Foldvari (2015). International Institute of Social History.

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: Number of new book titles published per million people”. Our World in Data (2026). Data adapted from Various sources. Retrieved from https://archive.ourworldindata.org/20260512-000143/grapher/new-books-per-million.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:

Various sources (2017) – processed by Our World in Data

Full citation

Various sources (2017) – processed by Our World in Data. “Number of new book titles published per million people” [dataset]. Various sources, “Clio-Infra - Human Capital” [original data]. Retrieved May 14, 2026 from https://archive.ourworldindata.org/20260512-000143/grapher/new-books-per-million.html (archived on May 12, 2026).

Download

Quick download

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.

Download full data

Includes all entities and time points

Download displayed data

Includes only the entities and time points currently visible in the chart

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/new-books-per-million.csv?v=1&csvType=full&useColumnShortNames=false
Metadata URL (JSON format)
https://ourworldindata.org/grapher/new-books-per-million.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/new-books-per-million.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/new-books-per-million.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/new-books-per-million.metadata.json?v=1&csvType=full&useColumnShortNames=false").json()
R
library(jsonlite)

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

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