1.1. Contact organisation
Statistics Norway
1.2. Contact organisation unit
Statistics Norway
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
P.O. Box 2633 St. Hanshaugen, NO-0131 Oslo, Norway
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
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
3.1. Data description
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the 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 the 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) Handbook 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 local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is 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
See below.
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 the Frascati Manual.
3.3.2. Sector institutional coverage
| Government sector | Central, provincial and local government institutes. Hospitals outside the university system are included in Government sector. |
|---|---|
| Hospitals and clinics | Hospitals outside the university system are included in the Government sector. |
| Inclusion of units that primarily do not belong to GOV and the borderline cases. |
Since the 1989 survey, the Private non-Profit sector is included in the Government sector |
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. Persons are not included in data on personnel (FTE and HC). Administration at local level: Expenditure on R&D is included in other current costs. Personnel is included in FTE and HC. |
|---|---|
| External R&D personnel | Not applicable |
| 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 |
|---|---|
| Payments to rest of the world by sector - availability | Not applicable |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) 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 | Not applicable |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not applicable |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year, annual |
|---|---|
| Source of funds | In line with FM. Data on internal/external available, but no data on transfer/exchange funds yet. |
| Type of R&D | In line with FM |
| Type of costs | In line with FM, but no information on capitalized computer software or other intellectual property products. |
| Defence R&D - method for obtaining data on R&D expenditure | Expenditure financed by the Ministry of Defence is used as an approximation |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year, annual |
|---|---|
| Function | Researchers and support staff. |
| Qualification | Available for researchers, not for support staff. |
| Age | Available for researchers. |
| Citizenship | Available for researchers on ad hoc basis (some survey years) |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year, annual |
|---|---|
| Function | Researchers and other support staff |
| Qualification | not available |
| Age | not available |
| Citizenship | not available |
3.4.2.3. FTE calculation
Information from R&D survey
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
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 of institutional units.
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 Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| 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 units known or believed to perform R&D activities are surveyed. | Not applicable |
| Estimation of the target population size | The target population is all units known to perform R&D to some extent. 137 units in 2023 | Not applicable |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | The frame population is equal to the target population, except for museums. Estimates are provided for these museums, as they are not covered by the survey |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | The most used sources are registers for R&D financing in The Research Council of Norway and information in the budget propositions from the various ministries. |
| Inclusion of units that primarily do not belong to the frame population | Since the 1989 survey, the Private non-Profit sector is included in the Government sector |
| Systematic exclusion of units from the process of updating the target population | Not relevant |
| Estimation of the frame population | Not relevant |
3.7. Reference area
Not requested.
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 2015. 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.
Expenditure: NOK 1000
R&D personnel: number of persons
Type of R&D: per cent
The metadata refers to reference 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 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. 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. 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. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | No |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics 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, according to legislation.
- Confidentiality commitments of survey staff: Staff takes a oath for service confidentiality.
7.2. Confidentiality - data treatment
Confidential data is not published for GOV. Data is never published for less than three units.
8.1. Release calendar
Release calendar is available on website. Usually October 31st.
8.2. Release calendar access
At our website under New statistics: Online Free Database
For Eurostat this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
Data available at the same time for all users.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | R&D Main Indicator for GOV |
| Ad-hoc releases | Y | R&D Main Indicator in the Institute Sector |
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 the publication “Report on Science & Technology Indicators for Norway”. Variables for all performing sectors as well as time series; Published Report on Main R&D Indicators. |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y | On irregular basis there are published shorter articles or reports. |
1) Y – Yes, N - No
10.3. Dissemination format - online database
See below
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 the micro-data |
No, only aggregated |
|---|---|
| Access cost policy | Full cost |
| Micro-data anonymisation rules | Microdata from the R&D statistics is available if rules and regulations according to confidentiality are met. |
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 Statistics Norway's website (Main R&D Indicators in the Institute Sector) |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | |
| Other | Figures are also made available through articles and tables in the online publication “Report on Science & Technology Indicators for Norway” with variables for all performing sectors as well as time series |
1) Y – Yes, N - No
10.6. Documentation on methodology
Metodology of R&D survey described on website (Main R&D Indicators in the Institute Sector)
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
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 key points and comparisons over time. Information 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 by email or at user meetings. |
See below
11.1. Quality assurance
Statistics Norway's requirements for official statistics is based on the Statistics Act, which lays down the formal framework for all Norwegian official statistics, and those requirements developed in international collaborations.
11.2. Quality management - assessment
The quality is considered to be of high quality as nearly all R&D performing units are covered by the survey.
The compilation also includes extensive quality control and comparisons with previous surveys.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | The Research Council of Norway | Data used for benchmarking, research policy issues, evaluations |
| 1 | Ministry of Education and Research | Data used for policy assessment and policy creation, white papers |
| 1 | Other ministries | Data used for policy assessment and policy creation |
| 2 | Media | Data to inform public |
| 3 | National media, as well as regional media and trade specific journals | National media are interested in the benchmarking aspects, often to comparisons to other countries. |
| 4 | Researchers and/or administrators at Norwegian research institutions | Data used for specific research purposes, for developing institutional research strategies etc. |
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 has not undertaken a national user satisfaction survey per se. Instead, 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 | No |
| Short description of the feedback received | Not relevant |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Close to 100 %
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 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Not applicable |
| Obligatory data on R&D expenditure | Not applicable |
| Optional data on R&D expenditure | Not applicable |
| Obligatory data on R&D personnel | Not applicable |
| Optional data on R&D personnel | Not applicable |
| 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-1970 | Biennial before 2007, annual from 2007 | None | |||
| Type of R&D | Y-1970 | Biennial before 2007, annual from 2007 | None | |||
| Type of costs | Y-1970 | Biennial before 2007, annual from 2007 | None | |||
| Socioeconomic objective | N | Biennial until 2005 | ||||
| Region | Y-1970 | Biennial before 2007, annual from 2007 | None | |||
| FORD | Y-1970 | Biennial before 2007, annual from 2007 | None | |||
| Type of institution | N |
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-1961 | Biennial before 2007, annual from 2007 | ||||
| Function | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| Qualification | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| Age | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| Citizenship | Y-2023 | Annual from 2023 | ||||
| Region | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| FORD | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| Type of institution | N |
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 available | ||||
| Function | Y-1961 | Biennial before 2007, annual from 2007. | ||||
| Qualification | N | Not available | ||||
| Age | N | Not available | ||||
| Citizenship | N | Not available | ||||
| Region | Y-1961 | Biennial before 2007, annual from 2007. | ||||
| FORD | Y-1961 | Biennial before 2007, annual from 2007. | ||||
| Type of institution | N |
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 |
|---|---|---|---|---|---|
| Thematic and technology areas | Y-1995 | Every second year. | 18 policy priorities | R&D expenditures | |
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').
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 |
|---|---|---|
| Educational level (i.e. PhD and Master degree) | Head Count | Annual |
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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- 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 | : | 3 | 1 | 4 | 2 | : | : |
| Total R&D personnel in FTE | : | 3 | 1 | 4 | 2 | : | : |
| Researchers in FTE | : | 3 | 1 | 4 | 2 | : | : |
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. 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
Not applicable
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not available |
| Government | Not available |
| Higher education | Not available |
| Private non-profit | Not available |
| Rest of the world | Not available |
| Total | Not available |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not available |
| Technicians | Not available | |
| other support staff | Not available | |
| Qualification | ISCED 8 | Not available |
| ISCED 5-7 | Not available | |
| ISCED 4 and below | Not available |
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 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.
- Description/assessment of coverage errors : Respondents may fail to understand instructions, there might be a new contact person and strategic answers may also occour.
- Measures taken to reduce their effect: Not apllicable
- Share of PNP (if PNP is included in GOV): Not applicable
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
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.
- Description/assessment of measurement errors: Measurements errors are overall few.
- Measures taken to reduce their effect: None
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 satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
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
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 98 | 102 | 4% |
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) (%) | 4% | 4% | 4% |
| Comments | Census survey | Census survey | Census survey |
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 | Some of the data is entered manually from questionnaire and directly into the R&D database. Punching errors may occur, but the errors are minimized through control and testing and cannot be estimated. |
|---|---|
| Estimates of data entry errors | Automatic controls have reduced errors to a minimum, not possible to estimate. |
| Variables for which coding was performed | FTE |
| Estimates of coding errors | Punching errors may occur, but the errors are minimized through control and testing and cannot be estimated. |
| Editing process and method | Editing is performed manually. |
| Procedure used to correct errors | After receiving the questionnaires from the units, figures are checked thoroughly. This reduces the number of errors to a minimum. During the data revision, answers from previous surveys are used as a reference. Consistency checks are undertaken. Where discrepancies are found, the auditor either corrects obvious mistakes, or contacts the units to rule out mistakes and misconceptions. |
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: 29 October 2024
- Lag (days): 300 days
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 29 October 2024
- Lag (days): 300 days
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) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
See below
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
In line with FM
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, 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 | |
| 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 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 | ||
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No deviation | |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | Yes | From the 2005-survey socio-economic objectives are not covered by the surveys. |
| Reference period | 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 method | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | |
| Survey questionnaire / data collection form | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | The survey questionnaire is only available in Norwegian. |
| Cooperation with respondents | FM2015 Chapter (mainly sub-chapter 8.5). | No deviation | |
| Data processing methods | FM2015 Chapter 8 (mainly sub-chapters 8.5-8.6). | No deviation | |
| Treatment of non-response | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.2. Comparability - over time
See below.
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) | 1970-2023 | 2007, 2009, 2019 |
2007: New survey method for hospitals. 2009: one large entity was moved from HES to GOV. 2019: several entities were moved from GOV til BES due to a merger. |
| Function | 1970-2023 | 2007, 2009, 2019 | See above |
| Qualification | 1970-2023 | Not applicable | |
| R&D personnel (FTE) | 1970-2023 | 2007, 2009, 2019 | 2007: New survey method for hospitals. 2009: one large entity was moved from HES to GOV. 2019: several entities were moved from GOV til BES due to a merger. |
| Function | 1970-2023 | See above | |
| Qualification | 1970-2023 | Not applicable | |
| R&D expenditure | 1970-2023 | 2007, 2009, 2019 | 2007: New survey method for hospitals. 2009: one large entity was moved from HES to GOV. 2019: several entities were moved from GOV til BES due to a merger |
| Source of funds | 1970-2023 | 2007, 2009, 2019 | See above |
| Type of costs | 1970-2023 | 2007, 2009, 2019 | See above |
| Type of R&D | 1970-2023 | 2007, 2009, 2019 | See above |
| Other | 1970-2023 |
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
No difference 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.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not available
15.4. Coherence - internal
See below.
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 – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers(in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 10 833 000 | 6613 | 4691 |
| Final data (delivered T+18) | 10 833 000 | 6613 | 4691 |
| Difference (of final data) | 0 | 0 | 0 |
Comments:
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanations of consistency issues, if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 7 352 000 000/ 6613 = 1 111 750 NOK | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 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 |
1) 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 not possible to calculate the costs for producing the R&D statistics in t
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | Not available | 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 the research institutes
18.1.2. Sample/census survey information
| Sampling unit | All units with R&D in the government sector |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | 137 units |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | Not applicable |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Not applicable |
| Variables the survey contributes to | R&D expenditure, FTE by qualification, field of R&D, funding sources, type of R&D, R&D coefficient of external funding, National variables: information on foreign doctorate holders, share of R&D activity that involved international cooperation, share of R&D activity relevant for business, share of R&D devoted to 18 policy priority ares |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Research institutes provide informastion in the R&D module of the Key figure survey, by questionnaire. Other entitiers provide data in a dedicated R&D survey. |
|---|---|
| Description of collected data / statistics | Expenditures and funding data. |
| Reference period, in relation to the variables the administrative source contributes to | Calender year. |
| Variables the administrative source contributes to | Prefill to the R&D module. |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Data is collected directly from the R&D performing units. Web questionnaires are sent to all units known or believed to perform R&D. In addition estimates are made for non-university museums. |
|---|---|
| Description of collected information | Type of R&D, Fields of science and technology, Thematic priorities and Technology areas (from 2005), R&D expenditure by source of funds and type of costs, R&D personnel. |
| Data collection method | WEB-questionnaires sent to all R&D performing units. |
| Time-use surveys for the calculation of R&D coefficients | Not applicable |
| Realised sample size (per stratum) | Not applicable |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Link to survey by email |
| Incentives used for increasing response | Email reminder |
| Follow-up of non-respondents | Email reminder & phone calls |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 97% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Not applicable |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available
|
| R&D national questionnaire and explanatory notes in the national language: | |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | Not availble |
18.4. Data validation
Many procedures for checking the source and output data, like comparing with data from previous years (both micro annd macro level), controlling inconsistencies between different variables, micro and macro editing. Special focus on large units (checking with annual reports, internet), contact with respondents.
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)
18.5.2. Data compilation methods
| Data compilation method - Final data | Starting 2007 full surveys are undertaken every year for the Government sector |
|---|---|
| Data compilation method - Preliminary data | Preliminary results are based on the received questionnaires. |
18.5.3. Measurement issues
| Method of derivation of regional data | The entites are asked to distribute their R&D activity by county |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Excluded |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not applicable |
|---|---|
| Description of the estimation method | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No further comments
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the 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 the 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) Handbook 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.
31 October 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested.
The metadata refers to reference 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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- 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.
Expenditure: NOK 1000
R&D personnel: number of persons
Type of R&D: per cent
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.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
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.
See below.
See below.
