JRC Data Catalogue
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2023 PREDICT Dataset

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PREDICT includes statistics on ICT industries and their R&D in Europe since 2006. The project covers major world competitors including 40 advanced and emerging countries: the EU27 plus United Kingdom, Norway, Russia and Switzerland in Europe; Canada, the United States and Brazil in the Americas; China, India, Japan, South Korea and Taiwan in Asia; and Australia. The dataset provides indicators in a wide variety of topics, including value added, employment, labour productivity and business R&D expenditure (BERD), distinguishing fine grain economic activities in ICT industries (up to 22 individual activities, 14 of which at the class level, i.e. at 4 digits in the ISIC/NACE classification), media and content industries (15 activities, 11 of them at 4 digit level) and at a higher level of aggregation for all the other industries in the economy. It also produces data on R&D expenditure. Nowcasting of more relevant data in these domains is also performed for the last two years, while time series go back to 1995. ICTs determine competitive power in the knowledge economy. The ICT sector alone accounts for almost one fourth of total Business expenditure in R&D (BERD) for the aggregate of the 40 economies under scrutiny in the project. It also has a huge enabling role for innovation in other technological domains. This is reflected at the EU policy level, where the Digital Agenda for Europe in 2010 was identified as one of the seven pillars of the Europe 2020 Strategy for growth in the Union; the achievement of a Digital Single Market (DSM) is one of the 10 political priorities set by the Commission since 2015; “A Europe fit for the digital age” is identified as one of the six European Commission priorities for the years 2019-2024; and digitalisation made a core part of the post-COVID-19 European recovery plan.

Contributors

How to cite

Cardona, Melisande; Torrecillas, Juan; López-Cobo, Montserrat; De Prato, Giuditta; Righi, Riccardo; Vazquez-Prada Baillet, Miguel; Papazoglou, Michail; Calza, Elisa; Mas, Matilde; Fernández de Guevara, Juan; Benages, Eva; Hernández, Laura; Mínguez, Consuelo; Pascual, Fernando; Pérez, Juan; Robledo, Juan Carlos; Salamanca, Jimena; Solaz, Marta (2026): 2023 PREDICT Dataset. European Commission, Joint Research Centre [Dataset] doi: 10.2905/JRC.N7W2V3R PID: http://data.europa.eu/89h/2682f25a-3d4f-40c7-9938-e2d73a4ff3c1

Keywords

digital economyICTinformation societyR&Dstatistics

Data access

ZIP

ZIP is an archive file format that supports lossless data compression. A ZIP file may contain one or more files or directories that may have been compressed.

Downloadable file

A downloadable file for the dataset.

Use conditions
Creative Commons Attribution 4.0 International

CC BY 4.0 lets others distribute, remix, tweak, and build upon the author’s work, even commercially, as long as they credit the author for the original creation. This is the most accommodating of licences offered. Recommended for maximum dissemination and use of licenced materials.

Access conditions
No limitations

Anybody can directly and anonymously access the data, without being required to register or authenticate.

ZIP

ZIP is an archive file format that supports lossless data compression. A ZIP file may contain one or more files or directories that may have been compressed.

Downloadable file

A downloadable file for the dataset.

Use conditions
Creative Commons Attribution 4.0 International

CC BY 4.0 lets others distribute, remix, tweak, and build upon the author’s work, even commercially, as long as they credit the author for the original creation. This is the most accommodating of licences offered. Recommended for maximum dissemination and use of licenced materials.

Access conditions
No limitations

Anybody can directly and anonymously access the data, without being required to register or authenticate.

  • The compressed zip file contains three Excel files, with the complete PREDICT Dataset

Publications

Publication
Cardona, M., Torrecillas Jodar, J., Lopez Cobo, M., De Prato, G., Righi, R., Vazquez-Prada Baillet, M., Papazoglou, M., Calza, E., Mas, M., Fernández De Guevara Radoselovics, J., Benages, E., Hernández, L., Mínguez, C., Pascual, F., Pérez, J., Robledo, J.C., Salamanca, J. and Solaz, M., Prospective insights on RandD in ICT, European Commission, 2023, JRC133490.
European Commission
  • PREDICT produces statistics and analyses on ICT industries and their R&D in Europe since 2006. The project covers major world competitors including 40 advanced and emerging countries - the EU27 plus Norway, Russia and Switzerland in Europe, Canada, the United States and Brazil in the Americas, China, India, Japan, South Korea and Taiwan in Asia, and Australia - as well as a growing array of indicators related to the ICT content of economic activities.

    PREDICT provides indicators in a wide variety of topics, including value added, employment, labour productivity and BERD, distinguishing fine grain economic activities in ICT industries (up to 22 individual activities, 14 of which at the class level, i.e. at 4 digits in the ISIC/NACE classification), media and content industries (15 activities, 11 of them at 4 digit level) and at a higher level of aggregation for all the other industries in the economy. It also produces data on Government financing of R&D in ICTs, and total R&D expenditure at the country level. Nowcasting of more relevant data in these domains is also performed up to a year before the reference date, while time series go back to 1995.

Publication
PREDICT Dashboard, Joint Research Centre, European Commission

Temporal coverage

From date To date
1995-01-01 2022-12-31

Additional information

Published by
European Commission, Joint Research Centre
Contact email
montserrat.lopez-cobo (at) ec.europa.eu
Update frequency
irregular

The event occurs at uneven intervals.

Data theme(s)
Economy and finance

dataset theme covering the domains of economic activity which involves production, distribution, trade, and consumption of goods and services, and finance activity focusing on the management of money at the individual, company, or government level

Science and technology

dataset theme covering the domains of science and technology, with science being the systematic pursuit of knowledge through testable explanations and predictions across natural, social, and formal disciplines, and technology encompassing the collective techniques, skills, methods, and processes used in producing goods, providing services, or achieving objectives like scientific research

Issued date
2023-05-12
Created date
19 Apr 2023 08:14
Modified date
20 Jul 2023 16:44
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Dataset identifier
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