1.1. Contact organisation
Statistics Norway
1.2. Contact organisation unit
Division for R&D, technology and business dynamics statistics
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Statistisk sentralbyrå
PB 2633 St. Hanshaugen
NO-0131 Oslo
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
14 November 2025
2.2. Metadata last posted
14 November 2025
2.3. Metadata last update
14 November 2025
3.1. Data description
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
Definition used in line with Frascati Manual.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
Private and public enterprises with at least 10 employees, except some industries. |
|---|---|
| Hospitals and clinics | University hospitals are included in the Higher education sector. Hospitals outside the university system are included in the government sector. |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | No |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Administration at central level: Expenditure on R&D is included as overhead expenditure in other current costs. Administration at local level: Expenditure on R&D is included in other current costs. Administrative and supportive personnel involved in R&D activities is included in FTE and HC, and the expenditure is included in compensation of employees. |
|---|---|
| External R&D personnel | Costs on external personnel are specified as Costs of contracted personnel, included in Other current costs of intramural R&D. Underlined in the questionnaire that this is different from Acquisition of R&D services (extramural R&D). External personnel are not included in total R&D personnel. |
| Clinical trials: compliance with the recommendations in FM §2.61. | No special effort has been made to deal with clinical trials. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Mostly available, separated in different categories |
|---|---|
| Payments to rest of the world by sector - availability | Mostly available, separated in different categories |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Foreign controlled enterprises are covered. It could be possible to distinguish between foreign-controlled and domestic enterprises. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Separate question about extramural R&D in the survey. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Some enterprises find it difficult to distinguish extramural expenditure from other current costs, especially costs of contracted personnel (other current costs, intramural R&D), and it is often difficult for us to detect wrong reporting. We give information in the survey about difference between extramural R&D and external R&D personnel. |
3.4. Statistical concepts and definitions
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
|---|---|
| Source of funds | In line with FM, but no data on exchange/transfer fund. |
| Type of R&D | In line with FM |
| Type of costs | In line with FM,but no separate data on capitalized computer software or other intellectual property products. |
| Economic activity of the unit | Main economic activity of the conducting unit, enterprise. Before 2021: breakdown by NACE was based on a question in the survey regarding R&D distributed on the establishments (local-kind-of-activity units) in the enterprise. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Only some enterprises in NACE 72 are reclassified to the main industry served. |
| Product field | Not included |
| Defence R&D - method for obtaining data on R&D expenditure | No special method for optaining data on defence R&D |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Average number of persons during the calendar year surveyed |
|---|---|
| Function | In line with FM. Data available for two categories: researchers and other R&D personnel (technical & administrative personnel). Before 2021: Researchers and other R&D personnel were classified by education level |
| Qualification | The survey request the following categories 0-6, 7, 8 (ISCED 2011). |
| Age | Not available |
| Citizenship | Not available |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year |
|---|---|
| Function | In line with FM. Data available for two categories: researchers and other R&D personnel (technical & administrative personnel). Before 2021: Researchers and other R&D personnel were classified by education level |
| Qualification | The survey request the following categories 0-6, 7, 8 (ISCED 2011). |
| Age | Not available |
| Citizenship | Not available |
3.4.2.3. FTE calculation
Information from the R&D survey, ask for number of FTEs on R&D, performed by R&D personnel during the calendar year.
3.5. Statistical unit
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
The reporting unit is legal unit. There are very small differences between enterprise and legal unit regarding R&D.
3.6. Statistical population
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
3.6.1. National target population
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | All active enterprises in the Business Enterprise sector (enterprises with at least 10 employees) and research institutes serving the enterprise sector. | Not applicable |
| Estimation of the target population size | Not available | Not applicable |
| Size cut-off point | Enterprises with 10 or more employees. | Not applicable |
| Size classes covered (and if different for some industries/services) | All size classes except for 0 and 1-9. Enterprises with 10-19 employees in NACE 41-43 and 49-53 are excluded. |
Not applicable |
| NACE/ISIC classes covered | A03, B05-B09, C10-C33, D35, E36-E39, F41-F43, G46, H49-H53, J58-63, K64-K66, M70-72, M74.90, N82.990 | Not applicable |
3.6.2. Frame population – Description
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
| Method used to define the frame population | All units within the NACE- and size classes in the target population, with 10 or more employees at the end of the statistical year. Data source: National business registry. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Known R&D performers from the last R&D survey (above a certain threshold for R&D activity; more than 1 million NOK in intramural R&D expenditure or 3 million NOK in extramural R&D). In addition, enterprises that applied funding from the Norwegian Research Council. We get a list of enterprises from the Norwegian Research Council. All enterprises with 10 or more employees in NACE 72 are included. |
| Inclusion of units that primarily do not belong to the frame population | No such units are included. |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | Not relevant. The frame in the Norwegian survey is all enterprises regardless of R&D activity or not. We have not a register of all R&D performing enterprises. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | Not relevant. The frame in the Norwegian survey is all enterprises regardless of R&D activity or not. We have not a register of all R&D performing enterprises. |
| Systematic exclusion of units from the process of updating the target population | Enterprises with less than 10 employees are excluded. In NACE 41-43 and 49-53 enterprises with less than 20 employees are excluded. Some industries are excluded because of very little R&D, see section 3.6.1 (NACE/ISIC classes covered). |
| Estimation of the frame population | 12856 (brutto population) |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3. (data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
Calendar year 2023.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No, but there is a statistical programme where R&D statistics is mentioned together with all other national statistics. Governed by the general national statistical legislation. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: Yes
- Confidentiality commitments of survey staff: Yes
7.2. Confidentiality - data treatment
Data is never published for less than three units. Also some rules for dominance (for one or two units). Secondary confidentiality is used to avoid disclosure of confidential data.
8.1. Release calendar
Planned release are registered in the release calendar at the SSB Release Calendar Website.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
Data are available for all users at the same time when we release data, no one has access before release.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
National data are disseminated yearly.
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.5 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | Statistics Norway R&D Release |
| Ad-hoc releases | Y | Statistics Norway R&D Release |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | Y | Figures are also made available through thepublication “Report on Science & TechnologyIndicators for Norway”. Variables for all performingsectors as well as time series; Publication on R&D Main Indicators |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y | On an irregular basis shorter articles or reports are produced at Statistics Norway. |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Statistics Norway has an online statdata bank with figures on R&D: Statistics Norway's Online Database
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | Researchers from approved research institutes may have access to unidentified microdata from the R&D statistics (direct identification removed) |
|---|---|
| Access cost policy | Full cost. |
| Micro-data anonymisation rules | Direct identification is removed and the units get a new number/key. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1) | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | Aggregate figures | Main results available at StatisticsNorway´s websites. In addition to the press release, key aggregates, tables and figures are available online. |
| Data prepared for individual ad hoc requests | Y | Micro-data/Aggregate figures | |
| Other |
1) Y – Yes, N - No
10.6. Documentation on methodology
Methodology of R&D survey described at on webpage ( Methodology on R&D Survey )
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Please see the sub-concept 10.7.1 in the full metadata view.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Metadata is published as a parallel to the press release. The press release addresses analyses’ key points and comparisons over time. Availability is further ensured by, including contact persons' names, phone numbers and e-mail addresses. Furthermore, figures accompany key tables for clarity purposes. |
|---|---|
| Requests on further clarification, most problematic issues | Sometimes. Examples: coverage and industry/size classes. |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
Statistics Norway has systems for quality assurance, and production of statistics follows European statistics code of practice. Different kind of quality reviews of statistics. The methodology for the R&D statistics for BES was in 2016 evaluated by methodology experts in Statistics Norway.
11.2. Quality management - assessment
The methodology for the R&D statistics for BES was in 2016 evaluated by methodology experts in Statistics Norway. The conclusion was that the existing methodology is reliable and gives well estimated results. There is always uncertainty, but we have tried to limit this with a large sample of enterprises. As mentioned earlier all enterprises with more than 50 employees (with some exceptions) are census surveys that give reliable results. It is also a strength that the response rate is very high (about 99 per cent) and there is virtually no item nonresponse. Additionally, the quality of the business register data used for sample selection and weights are considered to be of high quality.
12.1. Relevance - User Needs
Please see the sub-concept 12.1.1 in the full metadata view.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1-National level | Norwegian Research Council | Data used for research purposes, as well as benchmarking and drawing policy implications |
| 1-National level | Ministry of Education and Research | Data used for policy assessment and policy creation. |
| 1-National level | Ministry of Trade, Industry and Fishery | Data used for policy assessment and policy creation. |
| 1-National level | Ministry of Local Government and Modernisation | Data used for regional benchmarking, policy assessment and policy creation. |
| 2- Social actors | Employer’s associations, trade unions and lobby organisations |
These organizations tend to have very specific needs, depending on their agenda, and in what direction they wish to sway policy makers. E.g. industry employers associations specify the need for more statistics on R&D within new technologies, such as nanotechnology. |
| 3-Media | National media, as well as regional media and trade specific journals |
National media are interested in the benchmarking aspects, specifically comparisons to the other Nordic countries. Regional and trade specific media naturally have a narrower interest, depending on their respective audience. |
| 4- Researchers and students | Researchers from Norwegian universities, colleges, and research institutions |
Micro data used in analytical studies. Researchers’ needs vary with the institutions to which they belong. Researchers from regional colleges often use micro data for regional studies and projects. |
1) Users' class codification
1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes. )
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | Statistics Norway does not undertake a national user satisfaction survey per se. Instead, regular meetings are held with key users. At these meetings the users are encouraged to evaluate previous surveys, as well as suggest changes or amendments to future surveys. |
|---|---|
| User satisfaction survey specific for R&D statistics | Yes, the regular yearly meetings are specific for R&D statistics |
| Short description of the feedback received | Occationally some users want timelier data, but accept the difficulties in reducing the duration of the production process. Occasionally some users want more detailed breakdowns or full coverage of NACE-classes. |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
All R&D breakdowns transmitted.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Not applicable |
| Obligatory data on R&D expenditure | Not applicable |
| Optional data on R&D expenditure | Response burden |
| Obligatory data on R&D personnel | Not applicable |
| Optional data on R&D personnel | Response burden |
| Regional data on R&D expenditure and R&D personnel | Not applicable |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
| Type of R&D | Y-2001 | Biennial | No gap years | No changes | Not applicable | Not applicable |
| Type of costs | Y-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
| Socioeconomic objective | N | Not applicable | Not applicable | No changes | Not applicable | Not applicable |
| Region | Y-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
| FORD | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Type of institution | Y (partly) | Not available | Not available | Not available | Not available | Not available |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-2001 | Annual | No gap years | Female researchers estimated in 2021 | Only 2021 | Response burden |
| Function | Y-2001 | Annual | No gap years | Before 2021: Function defined by education level. From 2021: defined by question in survey | 2021 | Before 2021 education level was used to reduce response burden |
| Qualification | Y-2001 (partly) | Annual | No gap years | No changes | Not applicable | Not applicable |
| Age | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Citizenship | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Region | Y-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
| FORD | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Type of institution | Y (partly) | Not available | Not available | Not available | Not available | Not available |
| Economic activity | Y-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
| Product field | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Employment size class | Y | Annual (from 2001) | No gap years | No changes | Not applicable | Not applicable |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Function | Y-2001 | Annual | Before 2021: Function defined by education level | Only 2001-2020 | Response burden, available information, quality. Good correspondence between occupation and education level on aggregated level. | |
| Qualification | Y (partly)-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
| Age | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Citizenship | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Region | Y-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
| FORD | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Type of institution | Y (partly) | Not available | Not available | Not available | Not available | Not available |
| Economic activity | Y-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
| Product field | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Employment size class | Y-2001 | Annual | No gap years | No changes | Not applicable | Not applicable |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| Extramural R&D expenditure, from type of sector/institution | Y-2001 | Annual | Breakdown by type of institution | Not applicable | Not applicable |
| R&D services sold/delivered to others | Y-2005 | Annual | Breakdown by type of institution | Not applicable | Not applicable |
| Thematic and technology areas | Y-2001 | Annual | 18 areas | Not applicable | Not applicable |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not available | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non- response errors | ||||
| Total intramural R&D expenditure | 2 | 4 | 1 | 5 | 3 | : | +/- |
| Total R&D personnel in FTE | 2 | 4 | 1 | 5 | 3 | : | +/- |
| Researchers in FTE | 2 | 4 | 1 | 5 | 3 | : | +/- |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
A model-based prediction variance was estimated. The sample design and weighting has been taken into account.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | 1,5 | 1,8 | 1,2 |
| R&D personnel (FTE) | 0,8 | 1,7 | 1,1 |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | not applicable | 3,9 | 0,5 | 0 | 1,2 |
| R&D personnel (FTE) | not applicable | 3,4 | 0,5 | 0 | 1,1 |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors:
Frame population is based on the official, up-to-date, business register. There is assumed to be no under-coverage.
Some groups of enterprises are not included because of response burden and very small amounts of R&D: enterprises with less than 10 employees, some industries and enterprises with 10-19 employees in the NACE 41-43, 46 and 49-53.
b) Measures taken to reduce their effect:
Not available
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Frame misclassification rate
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
| By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
|---|---|---|---|---|---|
| Number or surveyed enterprises in the stratum (according to frame) | not applicable | 1343 | 1259 | 253 | 2855 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | not applicable | 1 | 0 | 0 | 1 |
| Misclassification rate | 0,07% | 0,04% | |||
| By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
| Number or surveyed enterprises in the stratum (according to frame) | not applicable | 1604 | 934 | 230 | 2768 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | not applicable | 1 | 1 | 0 | 2 |
| Misclassification rate | not applicable | 0,06% | 0,1% | 0,07% |
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors:
Some respondents probably report based on their own definition of R&D, and it may not correspond to the definition in the questionnaire. Some respondents may have difficulties to distinguish between external R&D personnel and extramural R&D. Within ICT related development there are risk of overreporting R&D. It is a risk of measurement errors regarding R&D integrated in deliveries for customers.
b) Measures taken to reduce their effect:
Information in the questionnaire and guidelines. Routines to detect errors during editing, recontact with respondents.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response, which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response, which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate.
Definition:
Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100
13.3.3.1.1. Unit non-response rates by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| Number of units with a response in the realised sample | not applicable | 2921 | 2160 | 477 | 5558 |
| Total number of units in the sample | not applicable | 2947 | 2193 | 483 | 5623 |
| Unit Non-response rate (un-weighted) | not applicable | 1% | 2% |
1% | 1% |
| Unit Non-response rate (weighted) | not applicable | not available | not available | not available | not available |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 2820 | 2738 | 5558 |
| Total number of units in the sample | 2855 | 2768 | 5623 |
| Unit Non-response rate (un-weighted) | 1% |
1% | 1% |
| Unit Non-response rate (weighted) | not available | not available | not available |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.3.3.1.3. Recalls/Reminders description
The survey is mandatory for the respondents. There were sent one reminder as a formal decision of sanction (a fine/fee). The respondents are informed that the fee is waived if the surveys is completed within a second/final due date. Before this formal reminder, they get an informal reminder.
If enterprises don’t respond after the second due date, the fine will be enforced by a national authority responsible for collecting debt owed to the government. The respondents will be informed that paying the fine does not release them from the legal obligation to answer the survey.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | Non-response analysis survey is not carried out |
|---|---|
| Selection of the sample of non-respondents | Not applicable |
| Data collection method employed | Not applicable |
| Response rate of this type of survey | Not applicable |
| The main reasons of non-response identified | Not applicable |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 0 | 0 | 0 |
| Imputation (Y/N) | Y | Y | Y |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used | No item non-response. But enterprises not responding to the survey are imputated if they reported R&D last survey. Method: mainly information from last year survey. | No item non-response. But enterprises not responding to the survey are imputated if they reported R&D last survey. Method: mainly information from last year survey. | No item non-response. But enterprises not responding to the survey are imputated if they reported R&D last survey. Method: mainly information from last year survey. |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | not available |
| Total R&D personnel in FTE | not available |
| Researchers in FTE | not available |
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | Data are collected on a web-based questionnaire. Report on paper questionnaire is no longer accepted. In the web-based questionnaire automatic controls are incorporated to minimize inconsistent reporting. Wrong values being reported have been reduced significantly compared to paper questionnaire. A common electronic platform is used for transferring data from the web survey to an in-house editing program. SAS is also used in data processing. |
|---|---|
| Estimates of data entry errors | No numbers available on data entry errors. |
| Variables for which coding was performed | No variables |
| Estimates of coding errors | Not available |
| Editing process and method | After receiving the questionnaires from the enterprises, the data is revised on a micro level. With the web-based questionnaire, the number of errors is reduced considerably. During the data revision, answers from previous surveys are used as a reference. Numerous consistency checks are undertaken. Where discrepancies are found, the auditor either corrects obvious mistakes, or contacts the enterprise to rule out mistakes and misconceptions. In addition, the auditors use information from the CIS survey, annual reports, Internet etc. The data revision is also done on the macro level. Enterprises that contribute significantly to their aggregate/group are prioritized in the data revision. Tables on macro level are being checked to find possible errors. While the editing process is meticulous and precise, it is possible for some errors to slip by without being identified and corrected. |
| Procedure used to correct errors | Mostly recontacting enterprises or correction based on information from previous surveys, annual reports etc. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: 31 October 2023
- Lag (days): 304.
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 24 February 2025
- Lag (days): 420.
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | T+10 | T+18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay | Not applicable | Not applicable |
15.1. Comparability - geographical
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No general issues.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | Before 2021: definition of researcher based on education level |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | YES | Total R&D personnel only include internal R&D personnel. |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation | Most enterprises in NACE 72 are classified in NACE 72, only some enterprises are classified according to industry served. |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | Statistical unit is statistical unit enterprise, and in most cases equal to legal unit. Therefor, the legal unit is reporting unit. |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation |
|
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation |
|
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation |
15.2. Comparability - over time
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | From 1970 | 1995, 1987, 1985, 1984, 1981 | 1995:The Business Enterprise survey was expanded considerably to include a sample of units with a minimum of 10 employees (previously 50 employees minimum) in branches which have been traditionally covered ; several service units have also been included. Total figures including the “new” enterprises (expansion) are therefore not comparable with those for previous years. 1987:The BE survey was expanded to include Bank and Insurance Services and more Engineering Services.1985:A significant number of new enterprises within computing and technical services was included. 1984:The coverage of the survey was expanded to include more private firms, notably within the branches of Transport, Commercial and Engineering Services and Other activities (ISIC 71, 8323/24). 1981:The coverage of the survey was expanded to include more private firms, notably within the branches of Transport, Commercial and Engineering Services and Other activities (ISIC 71, 8323/24). Prior to 1981 each institutes R&D was included under the industry it principally served. |
| Function | From 1974 | 2021 | Before 2021: Education level was used as a proxy for function. From 2021 onwards, function is reported directly. Only 2021: Female researchers are estimated |
| Qualification | Not applicable | Not applicable | Not applicable |
| R&D personnel (FTE) | From 1970 | 1995, 1987, 1985, 1984, 1981 | 1995:The Business Enterprise survey was expanded considerably to include a sample of units with a minimum of 10 employees (previously 50 employees minimum) in branches which have been traditionally covered ; several service units have also been included. Total figures including the “new” enterprises (expansion) are therefore not comparable with those for previous years. 1987:The BE survey was expanded to include Bank and Insurance Services and more Engineering Services.1985:A significant number of new enterprises within computing and technical services was included. 1984:The coverage of the survey was expanded to include more private firms, notably within the branches of Transport, Commercial and Engineering Services and Other activities (ISIC 71, 8323/24). 1981:The coverage of the survey was expanded to include more private firms, notably within the branches of Transport, Commercial and Engineering Services and Other activities (ISIC 71, 8323/24). Prior to 1981 each institutes R&D was included under the industry it principally served. |
| Function | From 1970 | 2021 | Before 2021: Education level was used as a proxy for function. From 2021 onwards, function is reported directly. |
| Qualification | Not applicable | Not applicable | Not applicable |
| R&D expenditure | From 1970 | 1995, 1987, 1985, 1984, 1981 | 1995:The Business Enterprise survey was expanded considerably to include a sample of units with a minimum of 10 employees (previously 50 employees minimum) in branches which have been traditionally covered ; several service units have also been included. Total figures including the “new” enterprises (expansion) are therefore not comparable with those for previous years. 1987:The BE survey was expanded to include Bank and Insurance Services and more Engineering Services.1985:A significant number of new enterprises within computing and technical services was included. 1984:The coverage of the survey was expanded to include more private firms, notably within the branches of Transport, Commercial and Engineering Services and Other activities (ISIC 71, 8323/24). 1981:The coverage of the survey was expanded to include more private firms, notably within the branches of Transport, Commercial and Engineering Services and Other activities (ISIC 71, 8323/24). Prior to 1981 each institutes R&D was included under the industry it principally served. |
| Source of funds | From 1970 | 1987, 1980 | 1987: The “own funds” of commercial State enterprises (included as executive units in the business enterprise sector), previously included in government funds, were reclassified as own funds of business enterprises. - The BE survey was expanded to include Bank and Insurance Services and more Engineering Services. 1980: the "own funds" of the mining sector (i.e. oil producers) include funds from their foreign parent companies |
| Type of costs | From 1970 | See above | See above |
| Type of R&D | From 1970 | See above | See above |
| Other | 2021 | Before 2021: Industry breakdowns were based on distribution of R&D on the establishments in the enterprise (a question in the survey). From 2021 enterprise was used for industry breakdowns. Affects all variables reported by industry. |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Data is produced the same way in odd and even years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
R&D survey is used as input in National Accounts.
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
|---|---|---|---|---|---|
| Total intramural R&D | Not available | Not available | Community Innovation Survey - CIS | Not available | Differences in population, sample, data collection, survey design etc.. Research institutes serving enterprise sector are not included in CIS. Enterprises with 5-9 employees included in CIS and not in R&D. |
| Total extramural R&D | Not available | Not available | Community Innovation Survey - CIS | Not available | Differences in population, sample, data collection, survey design, etc. Research institutes serving enterprise sector are not included in CIS. Enterprises with 5-9 employees included in CIS and not in R&D. |
15.4. Coherence - internal
Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 53730337.000 | 30230.3 | 22319.2 |
| Final data (delivered T+18) | 53650042.963 | 30102 | 22209 |
| Difference (of final data) | -80294.037 | -128.3 | -110.2 |
Comments : Number of total R&D personnel and researchers are internal personnel
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 1,1 NOK million | Not available. |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 1,4 NOK million | Not available |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use / person / day | |
|---|---|---|
| Staff costs | Not available | Not available |
| Data collection costs | Not available | Not available |
| Other costs | Not available | Not available |
| Total costs | Not available | Not available |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs : It is difficult to calculate detailed figures for costs.
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 5589 respondents | Not available |
| Average Time required to complete the questionnaire in hours (T)1 | Not available | Not available |
| Average hourly cost (in national currency) of a respondent (C) | Not available | Not available |
| Total cost | Not available | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
R&D survey for business enterprise sector. Survey with combination of sample and census.
Data are collected by means of one survey specially designed to measure R&D in the business enterprises and one designed for research institutes serving enterprise sector. Web-based questionnary. Se details below.
For enterprises with 50 employees or more there is a census survey with the following exceptions: a sample of 30 per cent were drawn for enterprises with 50-99 employees in NACE 41-43, 46, 49-53, due to the large number of enterprises in these NACE-classes.
For enterprises with 10-49 employees:
Combination of sample and census:
- Census: all enterprises with large R&D expenditures reported in the previous survey (more than 1 million NOK in intramural R&D or 3 million NOK in extramural R&D) all enterprises with at least 10 employees in NACE 72, enterprises with at least 10 employees that applied for funding from the Norwegian Research Council (list from the Norwegian Research Council).
- Sample: among the other enterprises a random sample is drawn. In NACE 41-43, 49-53 enterprises with 10-19 employees were excluded.
Research institutes serving enterprise sector: census.
18.1.2. Sample/census survey information
| Sampling unit | Enterprise. Legal unit is reporting unit. In most cases enterprise is equal to legal unit. | |
|---|---|---|
| Stratification variables (if any - for sample surveys only) | NACE (ISIC) and size class (number of employees) | |
| Stratification variable classes | Classes of employees (size class): 10-19, 20-49, 50+. In nace 41-43, 46, 49-53 the size class 50+ is divided in 50-99 and 100+. NACE is classified to two-digit level. |
|
| Population size | 12847 | |
| Planned sample size | 5623 | |
| Sample selection mechanism (for sample surveys only) | Stratified random selection. Census for enterprises with 50+ employees and larger. See 18.1.1. | |
| Survey frame | Central register of Enterprises and Establishments | |
| Sample design | The sample was stratified by 2-digit NACE and size classes (10-19, 20-49). The sampling rate is in general 30 per cent for size class 20-49, 15 per cent for 10-19. In NACE 41-43, 46 and 49-53 there is also strata with 50-99 employees. Larger enterprises are always included (census).
The following enterprises are included in the census part of the sample:
|
|
| Sample size | 5589 (included imputed non-respondents with R&D from previous year’s survey) | |
| Survey frame quality | High quality | |
| Variables the survey contributes to | All R&D variables |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | No collection of administrative data on R&D |
|---|---|
| Description of collected data / statistics | Not applicable |
| Reference period, in relation to the variables the administrative source contributes to | Not applicable |
| Variables the administrative source contributes to | Not applicable |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
| Realised sample size (per stratum) | Realized sample included non-respondents that are imputed with R&D information from last survey. Total: 5390 10-19 employees: 1128 20-49 employees: 1805 50-99 employees: 1228 100+: 1428 |
|---|---|
| Mode of data collection | Web-based questionnaire |
| Incentives used for increasing response | Mandatory survey |
| Follow-up of non-respondents | Reminder, use of fee |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Imputation based on information from last survey or other information, only for enterprises with R&D in the last survey. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | About 99% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Non-response analysis is not carried out |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | "R&D_BES_questionnaire_2023_English" |
| R&D national questionnaire and explanatory notes in the national language: | "R&D_BES_questionnaire_2023_Norwegian" |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | Not available |
Annexes:
Questionnaire for R&D in Business enterprise sector, 2023, in English
Questionnaire for R&D in Business enterprise sector 2023, in Norwegian
18.4. Data validation
Many procedures for checking the source and output data. Examples: calculating response rate, comparing with data from previous years (both at micro and macro level), controlling inconsistencies between different variables (both at micro and macro level). Special focus on important units (checking with annual reports, internet), detection of outliers. Prioritizing data validation of enterprises that affect macro level.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)
18.5.1.1. Imputation rate by Size class
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | not applicable | not applicable | not applicable | not applicable |
| 10-49 employees and self-employed persons | 0,4 % | not available | 0,4 % | not available |
| 50-249 employees and self-employed persons | 0,7 % | not available | 0,7 % | not available |
| 250-and more employees and self-employed persons | 0,8 % | not available | 0,8 % | not available |
| TOTAL | 0,6 % | not available | 0,6 % | not available |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | 0,7 % | not available | 0,7 % | not available |
| Services2) | 0,4 % | not available | 0,4 % | not available |
| TOTAL | 0,6 % | not available | 0,6 % | not available |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
| Data compilation method - Final data | Not relevant; annual survey. |
|---|---|
| Data compilation method - Preliminary data | Same method as final data, but fewer variables/dimensions. Some data validation is executed after publishing preliminary data. |
18.5.3. Measurement issues
| Method of derivation of regional data | Enterprises are asked to distribute total R&D persons and total intramural R&D expenditure on establishments (local kind-of-activity units). This is used to make regional distribution.
|
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not relevant |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Excluded. Mentioned in the questionnaire. |
18.5.4. Weighting and estimation methods
| Weight calculation method | For all the numerical variables such as R&D expenditure, R&D personnel etc. number of employees was used to calculate weights. For each stratum the number of employees in the population is divided by the number of employees in the achieved sample. The population and sample are stratified by NACE 2-digit level and size. Enterprises that ceased operation during data collection were removed from the sampling frame before weighting. Statistics Norway uses the inverse of the sampling fraction i.e. using the number of enterprises, to calculate how many enterprises that have R&D activity (all variables that are number of units, yes or no questions etc.). |
|---|---|
| Data source used for deriving population totals (universe description) | Statistics Norway's business register for all enterprises in Norway, this database inconstantly being updated and developed. |
| Variables used for weighting | Employees in each stratum. Number of enterprises int each stratum. |
| Calibration method and the software used | Statistics Norway used SAS-macro commands developed by its own staff. |
| Estimation | Coefficient of variation calculated |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No further comments.
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
14 November 2025
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
The reporting unit is legal unit. There are very small differences between enterprise and legal unit regarding R&D.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
Not requested. R&D statistics cover national and regional data.
Calendar year 2023.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
National data are disseminated yearly.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
