1.1. Contact organisation
Statistics Norway
1.2. Contact organisation unit
Statistics Norway
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
P.O. Box 2633 St. Hanshaugen, NO-0131 Oslo, Norway
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
28 June 2023
2.2. Metadata last posted
30 January 2024
2.3. Metadata last update
30 January 2024
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).
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. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
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).
3.2.1. Additional classifications
| Additional classification used | Description |
| N/A | |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Coverage of GERD and total R&D personnel resources is in accordance with Frascati Manual definitions. |
| Fields of Research and Development (FORD) | Data cover all major fields of research and development in accordance with FM. From 2007 onwards surveys (and FORD) are annual. |
| Socioeconomic objective (SEO by NABS) | In the surveys until 2003 data covered socio-economic objectives in accordance to NABS at chapter level. From the 2005-survey socio-economic objectives are not covered by the surveys. |
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 | 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 | N/A |
| Clinical trials | 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 | N/A |
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) 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 questions |
| Difficulties to distinguish intramural from extramural R&D expenditure |
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 capitalised 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 | N/A |
| Age | N/A |
| Citizenship | N/A |
3.4.2.3. FTE calculation
Information from R&D survey
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| N/A | ||
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. | |
| Estimation of the target population size | The target population is all units known to perform R&D to some extent |
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 | |
| Estimation of the frame population |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. 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.
Expenditure: NOK 1000
R&D personnel: number of persons
Type of R&D: per cent
Calendar year, annual
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. 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. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | Statistics Norway produces statistics in line with European Statistics Code of Practice |
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. |
| Legal acts | The statistics are developed, produced and disseminated pursuant to Act no. 32 of 21 June 2019 relating to official statistics and Statistics Norway (the Statistics Act). |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Statistics Norway produce R&D statistic for all R&D performing sectors in Norway. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | N/A |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | N/A |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | N/A |
| Planned changes of legislation | N/A |
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
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
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.
a) Confidentiality protection required by law: Yes
b) Confidentiality commitments of survey staff: Yes
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
8.2. Release calendar access
https://www.ssb.no/en/kommende-publiseringer
8.3. Release policy - user access
Data available at the same time for all users.
Annual
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Y | The release of the data is made public through tables and an article on https://www.ssb.no |
| Ad-hoc releases |
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 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | Figures are also made available through the online publication “Report on Science & Technology Indicators for Norway” with variables for all performing sectors as well as time series (https://www.forskningsradet.no/indikatorrapporten/). |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
Y | On irregular basis there are published shorter articles or reports. |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Statistics Norway has an online data bank with figures on R&D (https://www.ssb.no/en/teknologi-og-innovasjon/forskning-og-innovasjon-i-naeringslivet)
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | Micro-data from the R&D statistics are available to researchers, if rules and regulations are met. |
| Access cost policy | Full cost |
| Micro-data anonymisation rules |
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 | Statistics Norway has an online data bank with figures on R&D (https://www.ssb.no/en/teknologi-og-innovasjon/forskning-og-innovasjon-i-naeringslivet) | |
| Data prepared for individual ad hoc requests | Y | ||
| Other | Y | 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 (https://www.forskningsradet.no/indikatorrapporten/) |
1) Y – Yes, N - No
10.6. Documentation on methodology
Metodology of R&D survey described on website (https://www.ssb.no/en/teknologi-og-innovasjon/forskning-og-innovasjon-i-naeringslivet/statistikk/forskning-og-utvikling-i-instituttsektoren)
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 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. |
| Request on further clarification, most problematic issues | Sometimes. Most problematic issues are questions on comparability between countries. |
| Measure to increase clarity | We generally face clarity questions on request, no plans for further actions besides continuously updating the websites. |
| Impression of users on the clarity of the accompanying information to the data | Good |
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, other ministries | Data used for policy assessment and policy creation |
| 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. Regional and trade specific media naturally have a narrower interest, depending on their respective audience. |
| 4 | Researchers and/or administrators at Norwegian universities, colleges and 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 to suggest changes or amendments to future surveys. |
| User satisfaction survey specific for R&D statistics | |
| Short description of the feedback received |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
N/A
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.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | ||||||
| Obligatory data on R&D expenditure | X | |||||
| Optional data on R&D expenditure | X | |||||
| Obligatory data on R&D personnel | X | |||||
| Optional data on R&D personnel | X | |||||
| Regional data on R&D expenditure and R&D personnel | X |
Criteria:
A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.
B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Y-1970 |
Biennial before 2007, annual from 2007 | ||||
| Type of R&D | Y-1970 | Biennial before 2007, annual from 2007 | ||||
| Type of costs | Y-1970 | Biennial before 2007, annual from 2007 | ||||
| Socioeconomic objective | N | Biennial until 2005 | ||||
| Region | Y-1970 | Biennial before 2007, annual from 2007 | ||||
| FORD | Y-1970 | Biennial before 2007, annual from 2007 | ||||
| Type of institutionY | Y-1970 | Biennial before 2007, annual from 2007 |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| Function | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| Qualification | Y-1961 (researchers) | Biennial before 2007, annual from 2007 | ||||
| Age | Y-1961 (researchers) | Biennial before 2007, annual from 2007 | ||||
| Citizenship | N (ad hoc, plans to include annually for researchers) | Ad hoc, plans to include annually for researchers | ||||
| Region | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| FORD | Y-1961 | Biennial before 2007, annual from 2007 | ||||
| Type of institution | Y-1961 | Biennial before 2007, annual from 2007 |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | N (can be estimated) | |||||
| Function | Y-1970 | Biennial before 2007, annual from 2007 | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-1970 | Biennial before 2007, annual from 2007 | ||||
| FORD | Y-1970 | Biennial before 2007, annual from 2007 | ||||
| Type of institution | Y-1970 | Biennial before 2007, annual from 2007 |
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 areae | Y-1995 | Biennial before 2007, annual from 2007 | A total of 18 areas | ||
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
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 errors | 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 (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.
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' = In the event that at least one out of the three criteria described above would not be fully met.
3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be 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
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
13.2.1.1. Variance Estimation Method
N/A
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | N/A |
| Government | N/A |
| Higher education | N/A |
| Private non-profit | N/A |
| Rest of the world | N/A |
| Total | N/A |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | N/A |
| Technicians | N/A | |
| other support staff | N/A | |
| Qualification | ISCED 8 | N/A |
| ISCED 5-7 | N/A | |
| ISCED 4 and below | N/A |
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.
a) Description/assessment of coverage errors :
We conduct a full survey of all known institutions performing R&D in the Government sector.
b) Measures taken to reduce their effect:
N/A
c) Share of PNP (if PNP is included in GOV):
Less than 1 per cent.
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.
a) Description/assessment of measurement errors:
Measurement errors are overall few.
b) Measures taken to reduce their effect:
More clear and educational information in the questionnaire and guidelines.
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)
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 variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| R&D expenditure | 4% | Census survey |
| R&D personnel in FTE | 4% | Census survey |
| Researchers in FTE | 4% | Census survey |
13.3.3.3. Measures to increase response rate
In advance of every data collection we work on improvement of clarity in the questionnaire and guidelines. We support the respondents, contact them for missing/strange answers, and send out several reminders.
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 | Source of funds |
| 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)
a) End of reference period: 31.12.2021
b) Date of first release of national data: 26.10.2022
c) Lag (days): 300
14.1.2. Time lag - final result
a) End of reference period: 31.12.2021
b) Date of first release of national data: 26.10.2022
c) Lag (days): 300
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 |
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 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 paragraph 5.2). | No | |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No | |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| 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 |
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, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | No | |
| Survey questionnaire / data collection form | No | The survey questionnaire is only available in Norwegian. |
| Cooperation with respondents | No | |
| Data processing methods | No | |
| Treatment of non-response | No | |
| Variance estimation | No | |
| Data compilation of final and preliminary data | No |
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) | 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 | 2007, 2009, 2019 | See above | |
| Qualification | N/A | ||
| R&D personnel (FTE) | 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 | |
2007, 2009, 2019 | See above |
| Qualification | N/A | ||
| R&D expenditure | |
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 | 2007, 2009, 2019 | See above | |
| Type of costs | |
2007, 2009, 2019 | See above |
| Type of R&D | 2007, 2009, 2019 | See above | |
| Other |
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
15.3.3. 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 |
| N/A | N/A | N/A | N/A | N/A | N/A |
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) | 10753058 | 6880 | 4614 |
| Final data (delivered T+18) | 10753058 | 6880 | 4614 |
| Difference (of final data) | 0 | 0 | 0 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 975 170 NOK |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | N/A All personnel are employed by the government institutions. |
(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) | % sub-contracted1) | |
| Staff costs | N/A | 0 |
| Data collection costs | N/A | 0 |
| Other costs | N/A | 0 |
| Total costs | ||
| Comments on costs | ||
| It is not possible to calculate the costs for producing R&D statistics in the Government sector. This activity is integrated in other tasks, and the persons involved in compiling the statistics work on other projects as well. | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | N/A | N/A |
| Average Time required to complete the questionnaire in hours (T)1 | N/A | N/A |
| Average hourly cost (in national currency) of a respondent (C) | N/A | N/A |
| Total cost |
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
Normally we don't revise published figures. But in case we need to revise figures, we would do so.
17.2. Data revision - practice
If needed, we would mark revised figures in the databank and other tables and make it public
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. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | Key figure survey to research institutes with an integrated module on R&D. Dedicated R&D survey to other entities. Both annual. |
| Type of survey | Census. Questionnaire on web. |
| Combination of sample survey and census data | |
| Combination of dedicated R&D and other survey(s) | |
| Sub-population A (covered by sampling) | |
| Sub-population B (covered by census) | |
| Variables the survey contributes to | All variables |
| Survey timetable-most recent implementation |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | N/A | ||
| Stratification variables (if any - for sample surveys only) | N/A | ||
| Stratification variable classes | N/A | ||
| Population size | N/A | ||
| Planned sample size | N/A | ||
| Sample selection mechanism (for sample surveys only) | N/A | ||
| Survey frame | N/A | ||
| Sample design | N/A | ||
| Sample size | N/A | ||
| Survey frame quality | N/A |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Research institutes provide the information in the R&D module of the Key figure survey, by questionnaire. Other entities provide data in a dedicated R&D survey. |
| Description of collected data / statistics | N/A |
| Reference period, in relation to the variables the survey contributes to | Calendar year |
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 | N/A |
| Realised sample size (per stratum) | N/A |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | N/A |
| Incentives used for increasing response | N/A |
| Follow-up of non-respondents | At least 2 reminders |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | N/A |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | N/A |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | N/A |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | |
| R&D national questionnaire and explanatory notes in the national language: | https://www.ssb.no/innrapportering/nokkeltall-forskningsinstitutter |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
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
N/A
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | 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 entities are asked to distribute their R&D activity by geographic location |
| Coefficients used for estimation of the R&D share of more general expenditure items | |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Excluded |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics |
18.5.4. Weighting and estimation methods
| Description of weighting method | |
| Description of the estimation method |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
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).
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. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
30 January 2024
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.
Calendar year, annual
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.
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. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
Annual
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.
