1.1. Contact organisation
Statistics Denmark
1.2. Contact organisation unit
Government Finances
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Sejrøgade 11, 2100 København Ø, Denmark
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
1 October 2025
2.2. Metadata last posted
1 October 2025
2.3. Metadata last update
25 September 2025
3.1. Data description
Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and development (R&D) activities, and thereby provide information about the priority governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content.
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 (FM 2015, Chapter 12), which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
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 (Commission Implementing Regulation (EU) 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 (europa.eu)).
Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
Distribution by socioeconomic objectives (SEO) is based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) at one digit level.
3.2.1. National classification
| National nomenclature of SEO used | NORDFORSK classification |
|---|---|
| Correspondence table with NABS | There is an NORDFORSK/OECD correspondence key: Frascati Manual 2002, Table 8.2 |
3.2.2. NABS classification
| Deviations from NABS | Estimations are used in the distribution. |
|---|---|
| Problems in identifying / separating NABS chapters and sub chapters | Appropriations from local/regional government are included in GBARD: the Health objective includes provincial funding of R&D in hospitals; other provincial and local appropriations are included in NABS categories 9, 10 and 11. |
| Ability to distribute Non-oriented research and General University Funds (GUF) by fields of R&D | Part of GUF can be broken down by FOS (field of science). |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Frascati Manual definition of R&D. |
|---|---|
| Coverage of R&D or S&T in general | GBARD statistics cover R&D |
| Fields of R&D (FORD) covered | All fields of R&D covered |
| Socioeconomic objective (SEO by NABS) | All SEO covered |
3.3.2. Definition and coverage of government
GBARD statistics are assumed to report detailed data on all the government's budget items that may support R&D activities and to measure or estimate their R&D content. For the purposes of GBARD, the Government sector comprises (a) the central (federal) government, (b) regional (state) government and (c) local (municipal) government subsectors (FM2015, Chapter 12).
| Levels of government | Definition | Included / Not included | Comments |
|---|---|---|---|
| Central (federal) government | Appropriations of the parliament + selected governmental foundations | Included | |
| Regional (state) government | Regions (5 in total) | Included | The provincial governments are the 5 regions |
| Local (municipal) government | Municipalities (98 in total) | Included | The Danish local governments are the 98 municipalities |
3.4. Statistical concepts and definitions
Not requested.
3.5. Statistical unit
Million DKK
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.
| Definition of the national target population | The target population is the part of central government budget that is intended for R&D, as well as the budgetted R&D expenses of any other public institution (e.g. hospitals, museums, etc.) for which the survey may be relevant. |
|---|---|
| Estimation of the target population size | Not applicable |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested. See point 5.
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.
Not requested.
a) Calendar year: Calendar year = Fiscal year
b) Fiscal year:
Start month: 1
End month: 12
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Since the beginning of 2021, GBARD statistics are 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. GBARD statistics were based until the end of 2020 on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
6.1.2. National legislation
Paragraph 6 of the Act on Statistics Denmark (link in Danish):
"Public authorities and institutions must, within the framework of the work plan established by the board [of Statistics Denmark], upon request from Statistics Denmark, provide the information in their possession."
The paragraph refers to all information within the framework, including R&D-related information, and as such the respondents are obliged by the national law to provide raw and administrative data.
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development.
- EBS Methodological Manual on R&D Statistics.
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
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:
Not applicable - No information in this survey is confidential.
b) Confidentiality commitments of survey staff:
Not applicable - No information in this survey is confidential.
7.2. Confidentiality - data treatment
Not applicable - No information in this survey is confidential.
8.1. Release calendar
The release calendar for Statistics Denmark, including the data that is the basis for GBARD, is available here: Release calendar. The tables in question are FOUBUD, FOUBUD1, FOUBUD4, and FOUBUD5 under the subject 'Education and research' and subsubject 'Research, development and innovation'.
8.2. Release calendar access
The release calendar for Statistics Denmark, including the data that is the basis for GBARD, is available here: Release calendar. The tables in question are FOUBUD, FOUBUD1, FOUBUD4, and FOUBUD5 under the subject 'Education and research' and subsubject 'Research, development and innovation'.
8.3. Release policy - user access
The release calendar for Statistics Denmark, including the data that is the basis for GBARD, is available here: Release calendar. The tables in question are FOUBUD, FOUBUD1, FOUBUD4, and FOUBUD5 under the subject 'Education and research' and subsubject 'Research, development and innovation'.
When data is published, it becomes available in the StatBank at 8 am. Data becomes available to everyone in the public at the same time. The press, government entities, interest organisations and others do not get advance access to unpublished data, and data to Eurostat is transmitted after the national release.
Annually.
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 | Statistics Denmark publishes the National GBARD-figures in the national StatBank.dk. |
| Ad-hoc releases | N |
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 | The Ministry of Finance includes the GBARD-iii for the budget of the central government in their budgetary overview. |
| Specific paper publication (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
The data is published in four tables:
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 | All have access rights. There are no access restrictions on the statbank tables. |
|---|---|
| Access cost policy | None |
| Micro-data anonymisation rules | Not applicable |
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 figure | R&D data has been made available on Statistics Denmarks website since 2008. Before Statistics Denmark took over the responsibility for R&D data, The Danish Centre for Studies in Research and Research Policy published the National GBARD-figures in online publications on their website. |
| Data prepared for individual ad hoc requests | N | Not applicable | Not applicable |
| Other | N | Not applicable | Not applicable |
1) Y – Yes, N - No
10.6. Documentation on methodology
Documentation is available in the documentation section of Statistics Denmark's website. For GBARD, it can be found here: Government budget allocations for research and development
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, etc.) | Information on the statistical quality etc. can be downloaded by all users from the documentation section of Statistics Denmark's website. For GBARD, it can be found here: Government budget allocations for research and development. The publication including the GBARD data also contains general information on R&D and the budget as well as graphs. |
|---|---|
| Request on further clarification | The media, policy makers and researchers do occasionally contact us for further information, but generally not related to problems with clarification. We assist all users, but may ask for a fee if the request is particularly time-consuming. |
| Measure to increase clarity | None at this time. |
| Impression of users on the clarity of the accompanying information to the data | No comments on this by users. |
11.1. Quality assurance
Statistics Denmark follows the principles in the Code of Practice for European Statistics (CoP) and uses the Quality Assurance Framework of the European Statistical System (QAF) for the implementation of the principles. This involves continuous decentralized and central control of products and processes based on documentation following international standards. The central quality assurance function reports to the Working Group on Quality. Reports include suggestions for improvement that are assessed, decided and subsequently implemented.
11.2. Quality management - assessment
Quality is expected to be good as data is reported by the ministries in charge of the relevant accounts and data is validated by the Ministry of Finance. Quality might be improved by a thorough reading of the text in the state budget with additional attention to the difference in research and development and other activities.
A part of the budget under 'General advancement of knowledge' is categorized as the socio-economic objective 14.7: R&D not categorized according to purpose. The is due to research foundations that focus on a broad range of objectives and thus aren't able to know in advance which objectives the budget will be spent on.
The budgets from local and regional authorities depict the development in the reported costs to research and development during previous years including the errors that might be in these statistics. There is some uncertainty in the estimates for regional and municipal funding, as these are based on R&D in the most recent available accounts (which are 1-2 years prior) as well as the total budget for the year.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
|---|---|---|
| 1 - European level | The European commission (DG’s and Eurostat) | Data used for the compilation of European statistics and policy analysis |
| 1 - Member States | Nordic providers of GBOARD statistics | Data used to compare the Nordic countries |
| 1 - National level | Various ministries, Parliament, etc. | Statistics for policy use (from stage 3 (budget proposal) to stage 5 (final budget appropriations)): development and follow up |
| 1 - European level | OECD | Data for international comparisons |
| 2 - National level | Industry, university managers, researchers and their associations | Data for policy use |
| 3 - National level | Media | Statistics used for general information and specific themes, generating further debate |
| 4 - National level | National and Nordic researchers and students | Statistics for analysis, including micro data. |
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 | No user satisfaction survey has been conducted since Statistics Denmark took over the responsibility except from informal enqueries to users in the relevant ministries. A formal user satisfaction survey is not considered relevant as it is the Ministry of Finance that collects the data. |
|---|---|
| User satisfaction survey specific for GBARD statistics | No |
| Short description of the feedback received | No feedback to mention |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Not available.
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.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells | |
|---|---|---|---|---|---|---|
| Provisional budget statistics1 | X | |||||
| Obligatory final budget statistics1 | X | |||||
| Optional final budget statistics2 | Not relevant |
1) Criteria: Obligatory data (provisional budget and final budget). Only 'Very Good' = 100% and 'Very Poor' <100% apply.
2) Criteria: Optional data (final budget). '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 – Provisional data
| Availability1 | Frequency of data collection | Gap years – years with missing data | Time of compilation (T+x)2 | Comments | |
|---|---|---|---|---|---|
| Total GBARD | Y - 2001 | Annual | T-6 | Data is available from 2001 at Statistics Denmark's database (statbank.dk). Earlier data is also available in older publications but not in any central database. | |
| NABS Chapter level | Y - 2004 | Annual | T+6 | Data is available in Eurostat's database from 2004 | |
| NABS Sub-chapter level | N | ||||
| Special categories - Biotech | N | ||||
| Special categories - Nanotech | N | ||||
| Special categories - Security | N |
1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.
2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled
12.3.3.2. Data availability – Final data
| Availability1 | Frequency of data collection | Gap years – years with missing data | Time of compilation (T+x)2 | Comments | |
|---|---|---|---|---|---|
| Total GBARD | Y - 2001 | Annual | T-6 | ||
| NABS Chapter level | Y - 2004 | Annual | T+6 | ||
| NABS Sub-chapter level | N | ||||
| Special categories - Biotech | N | ||||
| Special categories - Nanotech | N | ||||
| Special categories - Security | N |
1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.
2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled
12.3.3.3. Data availability – Other special categories
| Special categories | Stage1 | Availability1 | Frequency of data colletion | Gap years – years with missing data | Time of compilation (T+x)3 | Comments |
|---|---|---|---|---|---|---|
| No special categories exist |
Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | No special categories exist |
1) Stage: P - provisional, F - final.
2) Availability of the data: No, data are not available, Y: Yes, data are available + start year.
3) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled
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 errors | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | |||
|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | |||
| Non applicable | 5 | - | - | - | 5 | +/- |
- 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 ‘-‘.
- 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 GBARD.
13.1.2. Assessment of the accuracy
| Indicators | 5 (Very Good)1 | 4 (Good)2 | 3 (Satisfactory)3 | 2 (Poor)4 | 1 (Very poor)5 |
|---|---|---|---|---|---|
| GBARD | X | ||||
| National public funding to transnationally coordinated R & D | X |
- High level of coverage (At least all national or federal ministries and the ministries and agencies responsible for R&D funding at state or regional level). High rate of response (>90%) in data collection. All figures broken down by NABS.
- If at least one out of the three criteria described above would not be fully met.
- In the event that the rate of response would be lower than 80% even by meeting the two remaining criteria.
- In the event that the average rate of response would be lower than 70% and at least one of the two remaining criteria would not be met.
- If all the three criteria described above are not met.
13.2. Sampling error
Not requested.
13.2.1. Sampling error - indicators
Not requested.
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:
There are no coverage errors concerning the central government appropriations. There will problably always remain small coverage errors when it comes to data concerning local data used for estimates of the regional and municipal budgets.
Accounts on the state budget might include activities that a deeper investigation would reveal as innovation activities instead of research and development. Balancing this, there might also be research and development activities that aren't included due to their small amounts.
b) Measures taken to reduce their effect:
The units that make up the local data are regularly checked to ensure the list remains accurate, which will improve the estimates of the regional and municipal budgets for R&D.
Activities, where it is unclear if they relate to innovation or R&D, are examined further as time and ressources permit.
13.3.1.1. Over-coverage - rate
Data is only collected from units that belong to the target population.
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. The survey questionnaire used for data collection may have led to the recording of wrong values.
a) Description/assessment of measurement errors:
No measurement errors are expected. The majority of the data comes from one source, which avoids the risk of double-counting R&D funding. The remaining come from a handful of other sources, none of which overlap.
b) Measures taken to reduce their effect:
Not applicable
13.3.3. Non response error
Non response errors: occur 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.
a) Problems in obtaining data from targeted information providers:
None - all data is obtained from the information providers.
b) Measures taken to reduce their effect:
Not applicable
c) Effect of non-response errors on the produced statistics:
Not applicable
13.3.3.1. Unit non-response - rate
Not requested.
13.3.3.2. Item non-response - rate
Not requested.
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.
a) Data processing and editing processes:
No processing errors are expected. The sources outside the budget of the central government which are calculated using coefficients only comprise about 15 percent of the total budget, so any processing errors within this area have limited effect.
b) Description of errors:
Not applicable
c) Measures taken to reduce their effect:
Not applicable
13.3.5. Model assumption error
Model assumption errors occur when the assumptions made for the estimation of parameters, models, the testing of statistical hypotheses, etc., are violated. As a result, the quality of the resulting statistics is affected (e.g. degrees of confidence might be inflated).
Description/assessment:
Not applicable
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
Date of first release of national data:
T-6
14.1.2. Time lag - final result
Date of first release of national data:
T+6
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) | 6 | 12 |
| Actual date of transmission of the data (T+x months) | 6 | 6 |
| Delay (days) | 0 | 0 |
| Reasoning for delay | Not applicable | Not applicable |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Regulation No 2020/1197, Frascati manual and the EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issue | Reference to recommendations | Deviation from recommendations | National definition / Treatment / Deviations from recommendations |
|---|---|---|---|
| Research and development | FM2015 Chapter 2 (mainly paragraphs 2.3 and 2.4). | No deviations. | The Frascati Manual definition is presented for the reporters |
| Coverage of levels of government | FM2015, §12.5 to 12.9 | No deviations. | OK |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197: Annex 1, Table 20 | No deviations. | OK at chapter-level |
| Reference period | Reg. 2020/1197: Annex 1, Table 20 | No deviations. | OK |
15.1.3. Deviations from recommendations
GBARD encompass all spending allocations met from sources of government revenue foreseen within the budget, such as taxation. Spending allocations by extra-budgetary government entities are within the scope only to the extent that their funds are allocated through the budgetary process (FM2015 §12.9). The following table lists a number of key methodological issues, which may affect the international comparability of national GBARD statistics.
| Methodological issues | Reference to recommendations | Deviation from recommendations | National definition / Treatment / Deviations from recommendations |
|---|---|---|---|
| Definition of GBARD | FM § 12.9 | Outlays are to be met only from taxation or other government revenue within the budget. | |
| Stages of data collection | FM2015 §12.41 | No deviations | |
| Gross / net approach, net principle | FM2015 §12.20 and 12.21 | Yes, corresponding revenue excluded from appropriations according to the net principle. | |
| EU/other funds | Eurostat's EBS Methodological Manual on R&D Statistics | EU and Nordic funds are not included in the GBARD data (although they are included in the National GBARD statistics) | |
| Types of expenditure | FM2015 §12.15 to 12.18 | No deviations | |
| Current and capital expenditure | FM §12.15 | Both current and capital expenditure are included in GBARD | |
| Extra budgetary funds | FM §12.8, 12.20, 12.38 | Other government funds are included. No distinction is made between different types of funds, but we may distinguish between basic funds and other government funds. |
|
| Loans | FM §12.31, 12.32, 12.34 | A distinction is made between loans that are to be repaid and other loans, only including in GBAORD those loans that are not expected to be repaid. Loans have, however, not been relevant since 2001. | |
| Indirect funding, tax rebates, etc. | FM §12.31 - 12.38 | Indirect funding (tax rebates etc.) is excluded. | |
| Treatment of multi-annual projects | FM2015 §12.44 | No deviations | |
| Treatment of GBARD going to R&D abroad | FM2015 §12.19 | No deviations | GBARD includes government-financed R&D performed abroad by a number of international organisations and national institutions. These appropriations are separated in the National statistics (as a separate sector) |
| Criterion for distribution by socioeconomic objective | FM2015 §12.50 to 12.71 | No deviations | Expected to be OK, but the reporters might make mistakes |
| Method of identification of primary objective | Eurostat's EBS Methodological Manual on R&D Statistics, topic 2, statement B.6 | Not mentioned in the guidelines |
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 | |
|---|---|---|---|
| Provisional data | 2004 | 2009, 2001, 1999, 1994, 1993, 1992, 1988, 1983 | 2009: improvement of methods leads to higher estimates for provincial funding 2001: A new principle concerning budgeting of commitments was introduced: from 2001 commitments of grants are carried to the debit side at the time of entering the commitment, where previously commitment of grants was carried to the debit side at maturity. 1999:Provincial and local government funding is included in the GBAORD data (in particular funding in provincial hospitals), as well as funding from the Danish National Research Foundation (included in Non-Oriented Research) and funding from the Danish Investment Fund (included in Industrial production and technology). 1994:The Ministry of education changed its methodology for the R&D funding estimation. 1992: Before 1992, GBAORD data included non-government sources. 1993, 1988, 1983:Changes in the method of assessing GBAORD led to breaks in series. |
| Final data | 2004 | 2009, 2001, 1999, 1994, 1993, 1992, 1988, 1983 | 2009: improvement of methods leads to higher estimates for provincial funding 2001: A new principle concerning budgeting of commitments was introduced: from 2001 commitments of grants are carried to the debit side at the time of entering the commitment, where previously commitment of grants was carried to the debit side at maturity. 1999:Provincial and local government funding is included in the GBAORD data (in particular funding in provincial hospitals), as well as funding from the Danish National Research Foundation (included in Non-Oriented Research) and funding from the Danish Investment Fund (included in Industrial production and technology). 1994:The Ministry of education changed its methodology for the R&D funding estimation. 1992: Before 1992, GBAORD data included non-government sources. 1993, 1988, 1983:Changes in the method of assessing GBAORD led to breaks in series. |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.3. Coherence - cross domain
GBAORD was compared with the total government-financed GERD in 2003 and 2005. Prior to 2003 no systematic comparisons between the GERD and the GBAORD series were made.
In 2003 and 2005 there was less than 1% in difference, when international appropriations are excluded from GBAORD.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not requested.
15.4. Coherence - internal
This part compares GBARD statistics from the provisional and final budget for the reference year.
15.4.1. Comparison between provisional and final data according to NABS 2007
| R&D allocations in the provisional budget delivered at T+6 | R&D allocations in the final budget delivered at T+12 | Difference (of final data) | |
|---|---|---|---|
| Exploration and exploitation of the Earth | 271,3 | 271,3 | 0,0 |
| Environment | 297,4 | 332,4 | 35,0 |
| Exploration and exploitation of space | 340,9 | 340,9 | 0,0 |
| Transport, telecommunication and other infrastructures | 34,0 | 33,9 | -0,1 |
| Energy | 1091,5 | 1093,5 | 2,0 |
| Industrial production and technology | 1031,7 | 1025,9 | -5,8 |
| Health | 4137,7 | 4404,3 | 266,6 |
| Agriculture | 750,3 | 1090,6 | 340,3 |
| Education | 1086,9 | 737,7 | -349,2 |
| Culture, recreation, religion and mass media | 384,3 | 400,4 | 16,1 |
| Political and social systems, structures and processes | 474,8 | 491,1 | 16,3 |
| General advancement of knowledge: R&D financed from General University Funds (GUF) | 10328,4 | 10413,0 | 84,6 |
| General advancement of knowledge: R&D financed from other sources than GUF | 4132,4 | 3905,9 | -226,5 |
| Defence | 102,6 | 103,1 | 0,5 |
| TOTAL GBARD | 24464,2 | 24644,0 | 179,8 |
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 | Not applicable | Not applicable |
| Data collection costs | Not applicable | Not applicable |
| Other costs | Not applicable | Not applicable |
| Total costs | Not applicable | Not applicable |
| Comments on costs | ||
| Most of the data are provided by the Ministry of Finance. We have no information on the cost of their work. In Statistics Denmark one head of section is spending roughly a fifth of the working hours with this statistical area |
||
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) | Not relevant | Not relevant |
| Average Time required to complete the questionnaire in hours (T)1 | Not relevant | Not relevant |
| Average hourly cost (in national currency) of a respondent (C) | Not relevant | Not relevant |
| Total cost | Not relevant | Not relevant |
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
Statistics Denmark revises published figures in accordance with the Revision Policy for Statistics Denmark.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
a) Provisional data:
The sources consist of:
- government budget data from the Ministry of Finance
- the Danish National Research Foundation
- the Nordic Council of Ministers
- EU funds
- municipal and regional funds (from Statistics Denmark's survey)
b) Final data:
The sources are the same as for the provisional data
c) General University Funds (GUF):
The sources are the same as for the provisional data
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Provisional data | Final data | Comments | |
|---|---|---|---|
| Data collection method | The funding of R&D from the budget of the Central Government has since 2004 been collected by the Ministry of Finance. A contact person in each ministry collects data from the reporting units of their ministry and reports these data to the Ministry of Finance. These data are then sent from the Ministry of Finance to Statistics Denmark. Statistics Denmark collects data from the governmental fund – The Danish National Research Foundation (“Danmarks Grundforskningsfond”) and from the Nordic Council of Ministers ("Nordisk Ministerråd") – through contact persons in the foundations. Statistics Denmark also estimates the regional and municipal funding of R&D and the EU funding of Danish R&D, the latter only to be used in the national estimate. |
Budget survey and text analysis: Like the data collection method for the provisional data. |
- Budget survey for Central government; - Text analysis on EU. - Estimates from R&D statistics for local/municipal government. The data compilation to GBARD is not integrated with the survey to government R&D performers. |
| Stage of data collection | Pre-provisional (3): Budget proposals (figures presented to the parliament for the coming year). Provisional data (4): Initial budget appropriations (figures as voted by the parliament for the coming year, including changes introduced in the parliamentary debate). The pre-provisional and provisional data categories are noted p for provisional. When they are replaced with final data, the p is removed |
Final data (5): Final budget appropriations (figures as voted by the parliament for the coming year, including additional votes during the year) | • For GBARD pre-provisional (3): Budget proposals (figures presented to the parliament for the coming year). • For GBARD provisional (4): Initial budget appropriations (figures as voted by the parliament for the coming year, including changes introduced in the parliamentary debate). • For GBARD final (5). Final budget appropriations (figures as voted by the parliament for the coming year, including additional votes during the year). |
| Reporting units | The institution funding/administrating is the reporting unit. | The institution funding/administrating is the reporting unit. | The reporting unit has remained consistent over the years. |
| Basic variable | Appropriations are the basic variable. The Danish name of the basic variable is “tilsagnsbevillinger”, which includes the term “commitment”. | Appropriations are the basic variable. The Danish name of the basic variable is “tilsagnsbevillinger”, which includes the term “commitment”. | Not applicable |
| Time of data collection (T+x)1) | T-6 months | T+6 months | Not applicable |
| Problems in the translation of budget items | Some problems. | ||
1) Time of data collection (T+x): T is assumed to represent the end of reference period. x expresses the number of months after (positive) or before (negative) T when data is collected.
18.3.2. General University Funds (GUF)
General University Funds are collected through the same method as other appropriations.
18.3.3. Distribution by socioeconomic objectives (SEO)
| Level of distribution of budgetary items – institution or programme/project | Objectives are distributed at the account level, which may then be split into more SEOs. |
|---|---|
| Criterion of distribution – purpose or content | An evaluation by the reporters. |
| Method of identification of primary objectives | An evaluation by the reporters. |
| Difficulties of distribution | No known difficulties. |
18.3.4. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| GBARD national questionnaire and explanatory notes in English: | Not applicable |
| GBARD national questionnaire and explanatory notes in the national language: | Not applicable |
| Other relevant documentation of national methodology in English: | Statistical documentation on Statistics Denmark's website |
| Other relevant documentation of national methodology in the national language: | Statistikdokumentation på Danmarks Statistiks hjemmeside |
18.4. Data validation
The government budget data from the Ministry of Finance is validated primarily by the Ministry of Finance themselves as they gather the relevant budget data. Once the data set is received by Statistics Denmark, it is compared to previous years to spot unusual developments and other things that might indicate incorrect data that should be investigated further.
Data from the Danish National Research Foundation, the Nordic Council of Ministers, and EU funds is collected and compared to previous years for validation.
Data regarding municipalities and regions is an estimate based on a survey and is received already validated, but is, like the other data sources, compared with data from previous years.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
0
18.5.2. Data compilation methods
See below.
18.5.2.1. Identifying R&D
| Method(s) of separating R&D from non-R&D | The reporters estimate the share of R&D in each part of each appropriation. The estimate is based on their knowledge or the use of each and every account, as well as communication with institutes and other receivers of government funding. The technique is harmonised across reporters. |
|---|---|
| Description of the use of the coefficient (if applicable) | Coefficients are used for regional and municipal government. The coefficients are derived from the R&D statistics: In the Provisional (4) budget it is derived from the R&D statistics for the year T-2, and in the Final (5) budget it is derived from the R&D statistics for the year T-1. |
| Coefficient estimation method | Coefficients are used for regional and municipal government. The coefficients are derived from the R&D statistics: In the Provisional (4) budget it is derived from the R&D statistics for the year T-2, and in the Final (5) budget it is derived from the R&D statistics for the year T-1. |
| Frequency of updating of coefficients | Annually |
18.5.2.2. General University Funds (GUF)
| Method(s) of separating R&D from non-R&D | Same method as other appropriations. |
|---|---|
| Description of the use of the coefficient (if applicable) | Not applicable |
| Coefficient estimation method | Not applicable |
| Frequency of updating of coefficients | Not applicable |
18.5.2.3. Other issues
| Treatment of multi-annual programmes | Multi-annual programmes are reported in a single year - the year of the commitment. |
|---|---|
| Possibility to classify budgetary items by COFOG functions | No |
| Possibility to classify budgetary items by other nomenclatures e.g. NACE | No |
| Method of estimation of future budgets | Data at current prices are used. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and development (R&D) activities, and thereby provide information about the priority governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content.
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 (FM 2015, Chapter 12), which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
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 (Commission Implementing Regulation (EU) 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 (europa.eu)).
Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
25 September 2025
Not requested.
Million DKK
See below.
Not requested.
a) Calendar year: Calendar year = Fiscal year
b) Fiscal year:
Start month: 1
End month: 12
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.
Not requested.
See below.
a) Provisional data:
The sources consist of:
- government budget data from the Ministry of Finance
- the Danish National Research Foundation
- the Nordic Council of Ministers
- EU funds
- municipal and regional funds (from Statistics Denmark's survey)
b) Final data:
The sources are the same as for the provisional data
c) General University Funds (GUF):
The sources are the same as for the provisional data
Annually.
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.
