1.1. Contact organisation
Statistics Denmark
1.2. Contact organisation unit
Science, Technology and Culture
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Sankt Kjelds Plads 11, DK-2100 Copenhagen, Denmark
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
18 March 2026
3.1. Data description
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the business enterprise sector consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics. (EBS Methodological Manual on R&D Statistics).
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 Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education (ISCED 2011).
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | In line with the Frascati-manual. |
|---|---|
| Fields of Research and Development (FORD) | Data is for the NSE+SSH aggregate. Data is not collected according to field of science. Prior to 1996 it wasassumed that all BERD could be categorised within the NSE due to the largepercentage of BERD within the manufacturing industries. As the amount ofR&D conducted within the service sector has been increasing it can no longer be assumed that all BERD should be categorised within the NSEfields of science. |
| Socioeconomic objective (SEO by NABS) | In line with the Frascati-manual |
3.3.2. Sector institutional coverage
| Business enterprise sector | Private and public enterprises in agriculture, mining, manufacturing,services and institutes serving these industries. Hence included in the BEsector is the Authorised Technological Service Institutes (GTS-institutes). |
|---|---|
| Hospitals and clinics | Both Regional and University Hospitals are included in HES, but not in BES |
| Inclusion of units that primarily do not belong to BES | Not included |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Corresponds to the concepts of the Frascati Manual. Administration carriedout by researchers in direct connection with R&D is considered as R&D andincluded in expenditure and personnel data. R&D administration undertakenat central level within the Administration is excluded from the personnelseries but taken into account in Other current costs. |
|---|---|
| External R&D personnel | Available from 2022 |
| Clinical trials | Corresponds to the concepts of the Frascati Manual. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Yes, separated in enterprises, EU and Governments |
|---|---|
| Payments to rest of the world by sector - availability | Yes, separated in enterprises, EU and Governments |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Yes, though some validity problems in the information. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Statistics on extramural R&D is compiled. First, enterprises are asked whether they perform R&D, acquire R&D from other part of the group oracquire form others. Next, the expenditure is asked in separate tables for intramural and extramural R&D, the latter divided in the sources. |
| Difficulties to distinguish intramural from extramural R&D expenditure | The distinguish is easy, but to make sure that the registration is done rigth can be difficult. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
|---|---|
| Source of funds | More sources used |
| Type of R&D | From 1999 the breakdown is based on current R&D expenditure |
| Type of costs | Capital expenditures are divided in buildings and other capital costs |
| Economic activity of the unit | Divergences from the ISIC classification include:
|
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Since 2001 the R&D expenditures are allocated to the main industry served,classified according to the national (ISIC/NACE-based) industrial classification. Prior to 2001 the R&D expenditures were allocated to the principal industrialactivity of the enterprises or institutes, classified according to the national (ISIC/NACE-based) industrial classification. |
| Product field | No problems |
| Defence R&D - method for obtaining data on R&D expenditure | Not collected |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | End of year |
|---|---|
| Function | No problems |
| Qualification | Not included |
| Age | Not included |
| Citizenship | Not included |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Average number of persons employed during the calendar year |
|---|---|
| Function | Personnel is broken down to 'researchers' and 'technicians' |
| Qualification | Not asked |
| Age | Not asked |
| Citizenship | Not asked |
3.4.2.3. FTE calculation
We ask for estimates from each unit. Some institutions still seem to be using ratios according to the employment category. Post-graduate students performing R&D are included.
3.5. Statistical unit
No deviations.
3.6. Statistical population
See below.
3.6.1. National target population
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | All | All |
| Estimation of the target population size | Yes | Yes |
| Size cut-off point | It varies depending of the R&D-propensity (2,6,10,50) | It varies depending of the R&D-propensity (2,6,10,50) |
| Size classes covered (and if different for some industries/services) | 2-5, 6-9,10-49,50-99,100-249,250,999 1000- | 2-5, 6-9,10-49,50-99,100-249,250,999 1000- |
| NACE/ISIC classes covered | 01-45,51-52,60-67,72-74,75.25,90,92.2 | 01-45,51-52,60-67,72-74,75.25,90,92.2 |
3.6.2. Frame population – Description
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
| Method used to define the frame population | Commission Regulation (EC) No 995/2012 of 26 October 2012implementing Decision No 1608/2003/EC of the European Parliament and of the Council as regards statistics on science and technology as regards sizeclasses and industries covered. This is supplemented by national needs to cover industries not mentioned in the decision. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Data from the data collection of R&D and innovation in enterprises 2015and 2016 is the primary source, supplemented with information on theactivity code (NACE rev. 2). All enterprises classified in NACE 72 R&D inthe Business Register are included in the survey. The frame is identified bythe statistical business register owned by Statistics Denmark. Units whichare part of public sector and units with no employment or working owner areexcluded. |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | We have a yearly evaluation of the population. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | We have had yearly increases in the target population since 2019 of around 500-1.000 each year. |
| Systematic exclusion of units from the process of updating the target population | A range of enterprises (with eg. less than 50 employes) in specific activitiesare not supposed to be R&D performers. |
| Estimation of the frame population | Approx 20 000 enterprises. |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See point 3.5.
3.9. Base period
Not requested.
The statistical unit is the enterprise.
The statistics covers activities for the entire reference year which is 2023 calendar year.
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. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. 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 | Mandatory |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No specific statistical legislation. |
|---|---|
| Legal acts | Paragraph 6 of the Act on Statistics Denmark (link in Danish). |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | None to our knowledge. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | None to our knowledge. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | None to our knowledge. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | None to our knowledge. |
| Planned changes of legislation | None to our knowledge. |
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.
Confidentiality is treated in accordance with Statistics Denmark's policy on confidentiality. This stipulates that the number of units in a cell should be treated as confidential, if there are fewer than three units, and if the variable concerned contains sufficient information to allow for the identification of particular enterprises. Such variables are mainly economic variables. Furthermore, concerning economic variables the policy stipulates that information should be treated as confidential when the two largest enterprises in a cell are dominant. The dominance thresholds for the largest/the two largest are confidential, as the publication of this information in itself may compromise the confidentiality treatment. For a description of Statistics Denmark's policy on confidentiality, see (Datafortrolighed - English version: Data Confidentiality Policy)
7.2. Confidentiality - data treatment
Programs (SAS) as well as manual surveying.
8.1. Release calendar
The publication date appears in the release calendar. The date is confirmed some weeks before.
8.2. Release calendar access
The Release Calender can be accessed on Statistics Denmarks English website: (Scheduled Releases of Statistics Denmark).
8.3. Release policy - user access
Statistics are always published at 8:00 a.m. at the day announced in the release calendar. No one outside of Statistics Denmark can access the statistics before they are published.
Yearly.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Yes | A separate press release is given for R&D-expenditure and –personnel, and for R&D-expenses as share of GDP. The statistics are published in Focus On Statistics Denmark (Nyt fra DanmarksStatistik) and are available fromStatistics Denmark's website and from the database StatBank Denmark. The statistics can also be found at the Eurostat databases (under the STI-domain). |
| Ad-hoc releases | N |
- 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) | Yes | In the years 2012-2021 Statistics Denmark published a more extensive publication concerning R&D and innovation. The latest versionis "Forskning, udvikling og innovation 2021"(Research, development and innovation 2021).The publication is available (Danish only) on R&D Main Indicators |
| Specific paper publication (e.g. sectoral provided to enterprises)(paper, online) | No |
1) Y – Yes, N - No
10.3. Dissemination format - online database
StatBank Denmark, available on this Online Database
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 to the tables. Access to the micro data of the BES is only for researchers through our Safe Centre or through the access for researchers at Statistics Denmark. |
|---|---|
| Access cost policy | The publication can be downloaded from our home page. |
| 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 | Main R&D Indicators | |
| Data prepared for individual ad hoc requests | Y | If someone wants tables different from those published we normally charge the customer for the time spent making thetables. | |
| Other | Y | A compendium of tables(EXCEL) are provided on DST website. |
1) Y – Yes, N - No
10.6. Documentation on methodology
See this website: (Publikationer) where documents on the used methodology can be found.
The OECD's Frascati Manual defines concepts in 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, quality reports, etc.) | A quality handbook is prepared. A declaration of content and quality assessment is available at StatisticsDenmark’s homepage - - updated annually. |
|---|---|
| Request on further clarification, most problematic issues | No requests |
| Measures to increase clarity | The quality handbook is a step in the direction of increasing clarity. The improvement of the questionnaire is an ongoing process also aiming atfurther clarification, and thereby better quality. |
| Impression of users on the clarity of the accompanying information to the data | Our users knows very well our quality documentation. |
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 theprinciples. This involves continuous decentralized and central control of products and processes based ondocumentation following international standards. The central quality assurance function reports to theWorking Group on Quality. Reports include suggestions for improvement that are assessed, decided andsubsequently implemented.
11.2. Quality management - assessment
The quality system of Statistics Denmark is based on the 15 principles of the
European Statistics Code ofPractice (CoP) published by Eurostat:
- Professional independence;
- Mandate for data collection;
- Adequacy of resources;
- Commitment to quality;
- Statistical confidentiality;
- Impartiality and objectivity;
- Sound methodology;
- Appropriate statistical procedures;
- Non-excessive burden on respondents;
- Cost effectiveness;
- Relevance;
- Accuracy and reliability;
- Timeliness and punctuality;
- Coherence and comparability;
- Accessibility and clarity.
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 (DGENTR) | The joint OECD/Eurostat international survey of resources devoted to R&D in Member countries. |
| 1- National level | Ministries , Parliament, politicalparties | Data used for policy purposes |
| 1- Regional level | Counties | Data used to compare R&D at theregional level |
| 1- National level | Industrial organisations | Sectoral comparison, comparisons across countries etc. |
| 1- National level | Trusts | Overall assessment of the funds given by trusts. |
| 1- National level | Newspapers, radio, television | Information on R&D expenditures in particular |
| 4- Researchers and students | Students | It varies a lot |
| 4- Researchers and students | Researchers | Researchers who use the data for their own analysis purpose and researches who just want some overall information |
| 5- Enterprises orbusinesses | Enterprises | Interested in the activities in their own industry. |
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 survey is conducted among users. |
|---|---|
| User satisfaction survey specific for R&D statistics | No survey is conducted among users. |
| Short description of the feedback received | Not applicable |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
The statistics is complete according to the Commission Regulation and the guidelines from the FrascatiManual. 100%
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of CommissionImplementing Regulation (EU) No 2020/1197 of 30 July 2020.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | x | |||||
| 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:
- Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.
- 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 | 1996,2000 | ||||
| Type of R&D | Y | 1996,2000 | Based oncurrent costs | |||
| Type of costs | Y | 1996,2000 | ||||
| Socioeconomic objective | N | 1996,2000 | ||||
| Region | Y | 1996,2000 | ||||
| FORD | Y | 1996,2000 | ||||
| Type of institution | Y | 1996,2000 |
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 | Every two years | 1996,2000 | ||||
| Function | Yearly | 1996,2000 | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | Every two years | 1996,2000 | Only researchers(Danish/foreign) | |||
| Region | Yearly | 1996,2000 | estimated | |||
| FORD | N | |||||
| Type of institution | Yearly | 1996,2000 | ||||
| Economic activity | Yearly | 1996,2000 | ||||
| Product field | Every two years | 1996,2000 | estimated | |||
| Employment size class | Yearly | 1996,2000 |
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 | Every two years | 1996,2000 | ||||
| Function | Yearly | 1996,2000 | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | Every two years | 1996,2000 | Only researchers(Danish/foreign) | |||
| Region | Yearly | 1996,2000 | Estimated | |||
| FORD | N | |||||
| Type of institution | Yearly | 1996,2000 | ||||
| Economic activity | Yearly | 1996,2000 | ||||
| Product field | Every two years | 1996,2000 | ||||
| Employment size class | Yearly | 1996,2000 |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2 | Frequency of data collection | Breakdown variables |
Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| Extramural R&Dexpenditure | Y | Yearly | |||
| Funding ofextramural R&D | Y | Every two years | |||
| Employed ph.d.s(HC & FTE) | Y | Every two years | |||
| Strategic R&D fields | Y | Every two years | |||
| Co-operation (typeand geographic area) | Y | Every two years | |||
| Innovation activities(intramural,acquisitions) | Y | Every two years | |||
| All R&D-data (R&D-expenditure, -personnel, co-operation etc.) | Nace | No | into the following: 10-12 13-15, 19,31.0-32.4,32.9-33 16-18 20.0-20.3,20.4-20.9,26.8 21 22 23 24-25 26.0-26.2,27.0-27.4,28.20-28.23 26.3-26.4 26.5, 26.7 26.6, 32.5-32.8 26.5, 26.7 27.5-27.9,28.24, 28.90-28.92, 28.94-28.99 28.0-28.1 28.25-28.29 28.4-28.8 28.3 28.93 29-30 |
||
| All R&D-data (R&D-expenditure, -personnel, co-operation etc.) | Nace | No | Breakdown of Knowledge-based services into: 58.2-58.9,62.01-62.02=Software publishing etc. 62.03-63.1=2=Comp. facility management, information services 71.12-71.12.39=Engineering 71.11,71.12.40-71.12.99=Geological examinations etc. 71.20-71.29=Technical testing and analyses 72= ScientificR&D 60-61,69,70.20-70.29,73-74,78,82,02.4,59.11-59.12,59.20-59.9= Other knowledge-based services |
- This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.
- Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not available | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
- 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 | ||||
| Total intramural R&D expenditure | 3 | 1 | 2 | 4 | - | + | |
| Total R&D personnel in FTE | 3 | 1 | 2 | 4 | - | - | |
| Researchers in FTE | 3 | 1 | 2 | 4 | - | - | |
- 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 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 |
- 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
- 'Good' = In the event that at least one out of the three criteria above described would not be fully met.
- 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
- '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.
- '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
CLAN.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1 | Services sector2 | TOTAL | |
|---|---|---|---|
| R&D expenditure | 1,3 | ||
| R&D personnel (FTE) | 1,4 |
- Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43).
- Services sector (NACE Rev 2.: 45-47,49-53,55-56,58-63,64-66 68,69-75,77-82,84,85,86-88,90-93,94-96,97-98,99).
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
| R&D expenditure | 1,3 | ||||
| R&D personnel (FTE) | 1,4 |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
Description/assessment of coverage errors: We usually have a few non-response enterprises, and some enterprises that are inactice at the time of data collection.
Measures taken to reduce their effect: Non-response are contacted repeatedly, if that doesn't work we imputate (usually between 0 and 20 imputations are done). Inactive enterprises are assumed to perform 0 RD.
13.3.1.1. Over-coverage - rate
Magnitude of error (%) = (Observed Value-True Value)/ True Value (%).
13.3.1.2. Common units - proportion
There are no common units.
13.3.1.3. Frame misclassification rate
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
| By size class for the Industry Sector | |||||
|---|---|---|---|---|---|
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
| Number or surveyed enterprises in the stratum (according to frame) | 0 | 0 | 0 | 0 | 0 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 0 | 0 | 0 | 0 | 0 |
| Misclassification rate | 0 | 0 | 0 | 0 | 0 |
| By size class for the Services Sector | |||||
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
| Number or surveyed enterprises in the stratum (according to frame) | 0 | 0 | 0 | 0 | 0 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 0 | 0 | 0 | 0 | 0 |
| Misclassification rate | 0 | 0 | 0 | 0 | 0 |
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
Description/assessment of measurement errors: Alot of respondents struggle to understand the definitions and destictions of RD and Innovation. Furthermore we have alot of respondents struggle to understand alot of the follow up questions.
Measures taken to reduce their effect: We look over incoming data and compare to previous years and to other enterprises of similiar NACE, to determine whether the data is consistent and credible. If data is deemed not credible we contact the enterprise and ask them to confirm or resubmit.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response, which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response, which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate.
Definition:
Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Definition:
- Un-weighted Unit Non- Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey).
- Weighted Unit Non- Response Rate = 1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample).
13.3.3.1.1. Unit non-response rates by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
| Number of units with a response in the realised sample | 3451 | ||||
| Total number of units in the sample | 3500 | ||||
| Unit Non-response rate (un-weighted) | |||||
| Unit Non-response rate (weighted) | Not applicable |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 3451 | ||
| Total number of units in the sample | 3500 | ||
| Unit Non-response rate (un-weighted) | |||
| Unit Non-response rate (weighted) | Not applicable |
- Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43).
- Services (NACE Rev 2.: 45-47,49-53,55-56,58-63,64-66 68,69-75,77-82,84,85,86-88,90-93,94-96,97-98,99).
13.3.3.1.3. Recalls/Reminders description
4 reminders were sent out to non-responding enterprises, followed by a telephone reminder.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No, since response rate is minimum 97 per cent. |
|---|---|
| Selection of the sample of non-respondents | Not applicable |
| Data collection method employed | Not applicable |
| Response rate of this type of survey | Not applicable |
| The main reasons of non-response identified | Not applicable since response rate is minimum 97 per cent. |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item non-Response Rate (%) = 1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample) * 100.
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
| Item non-response rate (un-weighted) (%) | <1% | <1% | <1% |
| Imputation (Y/N) | Y | Y | Y |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used | Item non-response is imputed automatically for central variables, e.g. 'labour costs', 'other current costs', number of R&D personnel (head counts and f.t.e.). This imputation will use information from the previous survey if these are available for the repondent, or from the survey two years before. Otherwise it will be calculated as a mean for the specific type of industry and size class. Therefore Statistics Denmark has no information on the item non-response rate. | Item non-response is imputed automatically for central variables, e.g. 'labour costs', 'other current costs', number of R&D personnel (head counts and f.t.e.). This imputation will use information from the previous survey if these are available for the repondent, or from the survey two years before. Otherwise it will be calculated as a mean for the specific type of industry and size class. Therefore Statistics Denmark has no information on the item non-response rate. | Item non-response is imputed automatically for central variables, e.g. 'labour costs', 'other current costs', number of R&D personnel (head counts and f.t.e.). This imputation will use information from the previous survey if these are available for the repondent, or from the survey two years before. Otherwise it will be calculated as a mean for the specific type of industry and size class. Therefore Statistics Denmark has no information on the item non-response rate. |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
| Total intramural R&D expenditure | <1% |
| Total R&D personnel in FTE | <1% |
| Researchers in FTE | <1% |
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 | 99,5 percent of the responses are gathered by electronic onlinequestionnaires. The 0,5 percent remaining are filling a paper questionnaire. Respective error estimates are not available. |
|---|---|
| Estimates of data entry errors | It is estimated that app. 5 percent of the reporting units make one or more errors in their data entry, typically not noticing that all costs should be calculated in 1,000 DKK. |
| Variables for which coding was performed | In principle all variables, main efforts is on the economic variables and personal number and FTE. |
| Estimates of coding errors | We some times have a few processing errors, but its on very few respondents. |
| Editing process and method | We have SAS-code that tests for coding/processing errors. For example we test if all respondents data still sum correctly across sub-values. |
| Procedure used to correct errors | Recontact and imputations. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: T + 11
- Lag (days): 330
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: T + 23
- Lag (days): 695
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 | 16 |
| Actual date of transmission of the data (T+x months) | 10 | 16 |
| Delay (days) | 0 | 0 |
| Reasoning for delay | No delay | No 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
No general issues.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual 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 deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly paragraph 4.2). | No deviation | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | Statistical units in some casesincludes more than one legalunit |
| Target population | FM2015 Chapter 7 (mainly paragraph 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | The national survey covers abroader range of activities andsize classes than necessary tosatisfy the needs for EU-data |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly paragraph 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015 Chapter 3 (mainly § 3.51-3.59) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | The national survey covers abroader range of activities andsize classes than necessary tosatisfy the needs for EU-data |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | The national survey covers abroader range of activities andsize classes than necessary tosatisfy the needs for EU-data |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, 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 preparation activities | No deviation | |
| Data collection method | No deviation | |
| Cooperation with respondents | No deviation | |
| Follow-up of non-respondents | No deviation | Close follow-up, resulting inhigh response-rate. |
| Data processing methods | No deviation | |
| Treatment of non-response | No deviation | |
| Data weighting | No deviation | |
| Variance estimation | No deviation | |
| Data compilation of final and preliminary data | No deviation | |
| Survey type | No deviation | Combination of census and sampling |
| Sample design | No deviation | The national survey covers abroader range of activities andsize classes than neccessary tosatisfy the needs for EU-data. |
| Survey questionnaire | The 2021 survey included more variables than neccesary for delivering the EU-data. E.g. |
Electronical and physicalquestionnaire. |
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) | From 2007 | 2016 | |
| Function | From 2007 | 2016 | |
| Qualification | From 2007 | 2016 | |
| R&D personnel (FTE) | From 2007 | 2016 | |
| Function | From 2007 | 2016 | |
| Qualification | From 2007 | 2016 | |
| R&D expenditure | From 2007 | 2016 | |
| Source of funds | From 2007 | 2016 | |
| Type of costs | From 2007 | 2016 | |
| Type of R&D | From 2007 | 2016 | |
| 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
The statistics is based on a survey sample. The statistics is compiled in one joined questionnaire which covers both the R&D domain and the innovation statistics.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey 2020 (CIS2020) (inn_cis12) (europa.eu) also collects the R&D expenditure of enterprises that form the coverage of the CIS2020 survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Business enterprise R&D expenditures is a primary source to the National Accounts.
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
|---|---|---|---|---|---|
| Not applicable. | Not applicable. | Not applicable. | Not applicable. | Not applicable. | Not applicable. |
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 (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 51288429 | 42456 | 31378 |
| Final data (delivered T+18) | 53198226 | 43014.11 | 30878.58 |
| Difference (of final data) | 1909797 | 558.11 | -499.42 |
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) | 1236762 |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Questions regarding External R&D personnel wasn't introduced in the danish questionnaire untill 2022. So N/A for 2021. |
- 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.).
- 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 | Not available | Not available |
| Data collection costs | Not available | Not available |
| Other costs | Not available | Not available |
| Total costs | Not available | Not available |
| Comments on costs | ||
- 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) | approx 3500 | |
| Average Time required to complete the questionnaire in hours (T)1 | approx 0,333 h. | |
| Hourly cost (in national currency) of a respondent (C) | Not available | |
| Total cost | The estimated respons burden for the reference year 2023 is 3,9 billion DKK. | Method for messurament of administrative burden on business. |
- T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’).
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. 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 | R&D survey for BES/CIS |
|---|---|
| Type of survey | The statistics are compiled on the basis of questionnaires. Questionnairesare web-based (since 2011) and close to 100 per cent of the responses comesfrom this media. The sample is of 3.000 enterprises from most size classesand all NACE-industries in the Danish enterprise sector. The sample is basedon a frame of 18.000 units. |
| Combination of sample survey and census data | The enterprises in the census are characterized by at least one of thefollowing criterias. • Reported R&D expenditures of at least 5 mill D.kr in atleast one of the two past years |
| Combination of dedicated R&D and other survey(s) | R&D survey for BES/CIS |
| Sub-population A (covered by sampling) | R&D survey for BES/CIS |
| Sub-population B (covered by census) | R&D survey for BES/CIS |
| Variables the survey contributes to | R&D survey: R&D expenditure by type of cost, funding, type of R&D,regional, main NACE, product, strategic topic, product/process/other |
| Survey timetable-most recent implementation | Start:T+3; Now casting: T+10; National publication: T+12; Reporting to EU: T+18 |
18.1.2. Sample/census survey information
| Stage 1 | |
| Sampling unit | Legal unit |
| Stratification variables (if any - for sample surveys only) | NACE Size Region (from 2006) |
| Stratification variable classes | 54 NACE-classes 6 size-classes 5 regions |
| Population size | 22 805 |
| Planned sample size | 3 500 |
| Sample selection mechanism (for sample surveys only) | Modified pps (5-100 percent depending on size class, each stratumis sorted by size andsample is drawn systematically through the stratum). |
| Survey frame | The respondents are selected from the Business Register. |
| Sample design | There are 54 strata. The selection probabilities are determined by strata and size group. The selection probability is generally increasing with size, measured by number of employees The table attached under 2.6.4 (Other documentation) shows sample-probabilities for each industry and size-class. |
| Sample size | 3 500 |
| Survey frame quality | Good after athorough validation |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Business Statistics Register (Danish: ESR = Erhvervsstatistisks register). Internal register at Statistics Denmark based on the official business register. |
|---|---|
| Description of collected data / statistics | Employed; turnover; other economic indicators; secondary NACE-classes;address; tel; email; web; contacts; establishments. |
| Reference period, in relation to the variables the survey contributes to | Reference year |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Realised sample size (per stratum) | See table below |
|---|---|
| Mode of data collection | Web-based questionnaire |
| Incentives used for increasing response | Mandatory |
| Follow-up of non-respondents | 4 written reminders, followed up by phone |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Imputation or assumed 0-values across the board. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | Over 97% |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | We have almost 0 non-response due to the questionnaire being mandatory. |
|
|
000-009 |
010-049 |
050-099 |
100-249 |
500+ |
Total |
||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
Pop |
Sample |
Pop |
Sample |
Pop |
Sample |
Pop |
Sample |
Pop |
Sample |
Pop |
Sample |
| 01-03 |
0 |
0 |
653 |
12 |
26 |
8 |
11 |
11 |
1 |
1 |
1 |
1 |
| 05-09 |
0 |
0 |
31 |
9 |
5 |
5 |
3 |
3 |
3 |
3 |
1 |
1 |
| 10-11 |
0 |
0 |
280 |
10 |
52 |
17 |
46 |
46 |
13 |
13 |
11 |
11 |
| 12 |
0 |
0 |
2 |
2 |
1 |
1 |
2 |
2 |
0 |
0 |
0 |
0 |
| 13 |
0 |
0 |
37 |
8 |
9 |
9 |
2 |
2 |
1 |
1 |
0 |
0 |
| 14 |
0 |
0 |
18 |
8 |
2 |
2 |
0 |
0 |
1 |
1 |
0 |
0 |
| 15 |
0 |
0 |
2 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 16 |
0 |
0 |
60 |
8 |
15 |
8 |
16 |
16 |
4 |
4 |
0 |
0 |
| 17 |
0 |
0 |
29 |
8 |
4 |
4 |
5 |
5 |
3 |
3 |
2 |
2 |
| 18 |
0 |
0 |
50 |
9 |
10 |
10 |
1 |
1 |
2 |
2 |
0 |
0 |
| 19 |
0 |
0 |
2 |
2 |
1 |
1 |
0 |
0 |
2 |
2 |
0 |
0 |
| 20 |
0 |
0 |
50 |
10 |
13 |
10 |
17 |
17 |
3 |
3 |
4 |
4 |
| 21 |
0 |
0 |
25 |
17 |
5 |
5 |
2 |
2 |
1 |
1 |
9 |
9 |
| 22 |
0 |
0 |
108 |
8 |
34 |
8 |
15 |
15 |
5 |
5 |
1 |
1 |
| 23 |
0 |
0 |
58 |
8 |
18 |
8 |
19 |
19 |
11 |
11 |
3 |
3 |
| 24 |
0 |
0 |
27 |
8 |
6 |
6 |
5 |
5 |
6 |
6 |
1 |
1 |
| 25 |
0 |
0 |
495 |
8 |
79 |
8 |
26 |
26 |
7 |
7 |
3 |
3 |
| 261 |
0 |
0 |
15 |
9 |
5 |
5 |
2 |
2 |
0 |
0 |
0 |
0 |
| 262 |
0 |
0 |
10 |
10 |
4 |
4 |
0 |
0 |
0 |
0 |
0 |
0 |
| 263 |
0 |
0 |
14 |
14 |
2 |
2 |
1 |
1 |
0 |
0 |
0 |
0 |
| 264 |
0 |
0 |
6 |
6 |
1 |
1 |
2 |
2 |
1 |
1 |
0 |
0 |
| 265 |
0 |
0 |
44 |
16 |
8 |
8 |
7 |
7 |
4 |
4 |
3 |
3 |
| 266 |
0 |
0 |
9 |
9 |
1 |
1 |
1 |
1 |
0 |
0 |
3 |
3 |
| 267 |
0 |
0 |
6 |
6 |
2 |
2 |
0 |
0 |
1 |
1 |
1 |
1 |
| 27 |
1 |
1 |
90 |
12 |
24 |
10 |
20 |
20 |
1 |
1 |
3 |
3 |
| 28 |
0 |
0 |
347 |
10 |
103 |
14 |
75 |
75 |
20 |
20 |
12 |
12 |
| 29 |
0 |
0 |
25 |
9 |
8 |
8 |
7 |
7 |
2 |
2 |
0 |
0 |
| 30 |
0 |
0 |
12 |
8 |
3 |
3 |
1 |
1 |
0 |
0 |
0 |
0 |
| 301 |
0 |
0 |
10 |
10 |
1 |
1 |
1 |
1 |
2 |
2 |
0 |
0 |
| 302 |
0 |
0 |
4 |
4 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 303 |
1 |
1 |
5 |
5 |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
1 |
| 304 |
0 |
0 |
2 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 31 |
0 |
0 |
102 |
8 |
13 |
9 |
10 |
10 |
4 |
4 |
1 |
1 |
| 32 |
0 |
0 |
50 |
10 |
6 |
6 |
9 |
9 |
2 |
2 |
3 |
3 |
| 33 |
0 |
0 |
176 |
8 |
20 |
9 |
8 |
8 |
3 |
3 |
1 |
1 |
| 35-36 |
3 |
3 |
82 |
23 |
17 |
17 |
8 |
8 |
10 |
10 |
6 |
6 |
| 37-39 |
0 |
0 |
74 |
9 |
16 |
8 |
13 |
13 |
4 |
4 |
3 |
3 |
| 41-43 |
0 |
0 |
2570 |
46 |
241 |
16 |
120 |
120 |
20 |
20 |
15 |
15 |
| 45-47 |
10 |
10 |
4084 |
116 |
488 |
61 |
242 |
242 |
61 |
61 |
58 |
58 |
| 49-53 |
7 |
7 |
976 |
28 |
143 |
12 |
87 |
87 |
41 |
41 |
33 |
33 |
| 55-56 |
0 |
0 |
1369 |
22 |
74 |
8 |
27 |
27 |
16 |
16 |
4 |
4 |
| 58-63 |
1 |
1 |
1223 |
32 |
197 |
31 |
111 |
111 |
24 |
24 |
24 |
24 |
| 64-65 |
2 |
2 |
180 |
12 |
40 |
13 |
42 |
42 |
11 |
11 |
25 |
25 |
| 66 |
0 |
0 |
138 |
8 |
14 |
10 |
10 |
10 |
3 |
3 |
3 |
3 |
| 68 |
0 |
0 |
433 |
8 |
52 |
8 |
34 |
34 |
9 |
9 |
3 |
3 |
| 69-70+73-75 |
2 |
2 |
1042 |
19 |
140 |
15 |
70 |
70 |
31 |
31 |
14 |
14 |
| 71 |
0 |
0 |
424 |
9 |
68 |
10 |
34 |
34 |
14 |
14 |
12 |
12 |
| 721 |
426 |
198 |
122 |
122 |
22 |
22 |
10 |
10 |
5 |
5 |
4 |
4 |
| 722 |
23 |
23 |
2 |
2 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
| 77-82 |
2 |
2 |
1140 |
23 |
152 |
13 |
90 |
90 |
25 |
25 |
22 |
22 |
| 84-85 |
0 |
0 |
21 |
8 |
11 |
11 |
14 |
14 |
1 |
1 |
1 |
1 |
| 86 |
0 |
0 |
438 |
8 |
13 |
8 |
5 |
5 |
3 |
3 |
2 |
2 |
| 90-93 |
0 |
0 |
419 |
8 |
50 |
8 |
22 |
22 |
1 |
1 |
3 |
3 |
| 94-99 |
0 |
0 |
484 |
8 |
59 |
8 |
23 |
23 |
14 |
14 |
5 |
5 |
| Total |
478 |
250 |
18075 |
804 |
2278 |
472 |
1277 |
1277 |
396 |
396 |
301 |
301 |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | Explanatory Notes on the R&D Survey |
| Other relevant documentation of national methodology in English: | Under development |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
An extensive validation process of the data is carried out. One part of the validations is integrated in the datacollection in the dynamic web-questionnaire; another part is carried out after the data collection usingmicro- and macro validation techniques. The individual reports from the enterprises are compared to formeryears reports and the registered information on number of employees and turnover. Outlier detection is alsoused as a validation process.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition: Imputation rate (for the variable x) % = (Number of imputed records for the variable x) / (Total number of possible records for x)*100.
18.5.1.1. Imputation rate by Size class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | 0 | 0 | 0 | 0 | 0 |
| R&D personnel (FTE) | 0 | 0 | 0 | 0 | 0 |
18.5.1.2. Imputation rate by NACE
| Industry1 | Services2 | TOTAL | |
|---|---|---|---|
| R&D expenditure | 0 | 0 | 0 |
| R&D personnel (FTE) | 0 | 0 | 0 |
- Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43).
- Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99).
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | Data on R&D are collected annually. |
|---|---|
| Data compilation method - Preliminary data | Linear projection of former years R&D-variables with growth of GDP |
18.5.3. Measurement issues
| Method of derivation of regional data | Larger enterprises are asked to estimate the share of R&D being performedin establishments outside the headquarters. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not relevant |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT is not included. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | The socio-economic classification is the NORDFORSK-classification, but astandard key to NABS exists, see Table 8.2, Frascati Manual. |
18.5.4. Weighting and estimation methods
| Weight calculation method | Weight = Nstrata - nstrata |
|---|---|
| Data source used for deriving population totals (universe description) | The Business Register |
| Variables used for weighting | As calibration variables are used Number of employees, turnover and region(NUTS2-level). |
| Calibration method and the software used | CLAN-procedures are used |
| Estimation | The calibrated weights are used in all estimations. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No further comments.
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the business enterprise sector consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics. (EBS Methodological Manual on R&D Statistics).
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 Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
18 March 2026
See below.
No deviations.
See below.
Not requested. R&D statistics cover national and regional data.
The statistics covers activities for the entire reference year which is 2023 calendar year.
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:
- 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.
The statistical unit is the enterprise.
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.
Yearly.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
