1.1. Contact organisation
Statistics Denmark
1.2. Contact organisation unit
Science, Technology and Culture
1.3. Contact name
Restricted from publication1.4. Contact person function
Restricted from publication1.5. Contact mail address
Sankt Kjelds Plads 11, DK-2100 Copenhagen, Denmark
1.6. Contact email address
Restricted from publication1.7. Contact phone number
Restricted from publication1.8. Contact fax number
Restricted from publication2.1. Metadata last certified
29 February 20242.2. Metadata last posted
29 February 20242.3. Metadata last update
29 February 20243.1. Data description
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
| None |
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) | All data are available by field of science. R&D data within the Social Sciences are collected without "Psychology" and "Educational Sciences". Both are included in Humanities instead. |
| Socioeconomic objective (SEO by NABS) | The NORDFORSK classification is used. There is a NORDFORSK/OECD correspondence key: Frascati Manual (Version 6)Table 8.2. Also, the R&D of the PNP Sector is broken down in accordance to NORDFORSK. |
3.3.2. Sector institutional coverage
| Government sector | Public institutes, hospitals and health administrations, libraries and archives, museums and collections mainly financed by government, and until 2002, university hospitals were included in the Government sector. |
| Hospitals and clinics | The R&D activities of the university hospitals are included in the Higher education sector beginning 2002. Up through 2001 the Danish university hospitals are included in the Government sector. |
| Inclusion of units that primarily do not belong to GOV |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Corresponds to Frascati Manual. Administration carried out by researchers in direct connection with R&D is considered as R&D and included in expenditure and personnel data. R&D administration undertaken at central level within the Administration is excluded from the personnel series but taken into account in the calculation of R&D shares in overhead costs. |
| External R&D personnel | Corresponds to the concepts of the Frascati Manual |
| 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 |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Y |
| 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 or acquire 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 clear enought, 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 | Based on FTE. |
| Type of costs | Capital expenditures are divided in buildings and other capital costs. |
| Defence R&D - method for obtaining data on R&D expenditure | Institutions performing defence R&D are included in the relevant sectors and receive the same questionnaire as other institutions in the same sector. |
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 | More detailed than needed for international reporting |
| Qualification | Not included |
| Age | Not included |
| Citizenship | Two categories: Danish/foreign |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Average number of persons employed during the calendar year |
| Function | Not included |
| Qualification | Not included |
| Age | Not included |
| Citizenship | Not included |
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.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Not available. |
3.5. Statistical unit
GOV and PNP: The statistical units are the smallest homogeneous unit predominantly involved in only one of the six major fields
of science and technology and for which a complete or almost complete set of factor input data can be obtained.
Neither the HES, the GOV nor the PNP data are combined with data collected from legal business entities.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | GOV and PNP: The statistical units are the smallest homogeneous unit predominantly involved in only one of the six major fields of science and technology and for which a complete or almost complete set of factor input data can be obtained. Neither the HES, the GOV nor the PNP data are combined with data collected from legal business entities |
|
| Estimation of the target population size |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | GOV: The institutions of last year’s R&D statistics are included and new ones are identified primarily through direct contact with administrative bodies (ministries etc.). PNP: The institutions of last year’s R&D statistics are included new ones are identified through available means in the bussiness register |
| Methods and data sources used for identifying a unit as known or supposed R&D performer | GOV: Units of interest are identified by the relevant administrative bodies after consultation with Statistics Denmark. PNP: The institutions of last year’s R&D statistics are included new ones are identified though available means in the bussiness register |
| Inclusion of units that primarily do not belong to the frame population | The R&D data for the HES, GOV and PNP-sectors are collected through the same survey, but all Units in the relevant frame populations are kept seperate in publiced data |
| Systematic exclusion of units from the process of updating the target population | |
| Estimation of the frame population |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
The statistical unit is defined in the Frascati manual as being the smallest homogeneous unit predominantly involved in only one of the six fields of science and for which a complete (or almost complete) set of factor input data can be obtained. In the Danish terminology this means the individual institute in universities, a centre and the individual department in university hospitals.
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. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | None to our knowledge |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No specific statistical legislation |
| Legal acts | No legal act |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | |
| 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.
- European Business Statistics Methodological Manual on R&D Statistics.
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
- Confidentiality protection required by law:
The survey follows Statistics Denmark's official data confidentiality policy, which includes restrictions for access, disclosuure control etc.
- Confidentiality commitments of survey staff:
All personnel in Staticstics Denmark have signed a statement that they will not disclose any information on respondents and their data.
7.2. Confidentiality - data treatment
No need to report unit response level.
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.
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 | Y |
|
|
| 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 results are published with the results for the HES-sector in an annual publication for R&D and Innovation Statistics. In the years 2012-2021 Statistics Denmark published a more extensive publication concerning R&D and innovation. The latest version is "Forskning, udvikling og innovation 2021" (Research, development and innovation 2021).The publication is available at this website. |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Information available at this website (StatBank Denmark).
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 | Restricted to persons working with the statistics and the Research Services team. |
| Access cost policy | not applicable |
| Micro-data anonymisation rules | Micro data are anonymised |
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 | StatBank Denmark includes statistics on R&D. The questionnaires and the methodological notes are available on our website |
|
| Data prepared for individual ad hoc requests | Y | Customer specific tables are produced. | |
| Other | Y | A compendium of tables (EXCEL) are provided on this DST website. |
Y – Yes, N - No
10.6. Documentation on methodology
See more infomation at DST 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. Information and clarity
| 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 Statistics Denmark’s homepage - updated annually. |
| Request on further clarification, most problematic issues | Yes, questions on link between GBARD and HES and GOV |
| Measure to increase clarity | No |
| 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 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
Overall, the quality of the GOV, HES and PNP R&D surveys are high.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs | |
| 1 | The European commission (DG’s and Eurostat) | Data used for the compilation of European statistics and policy analysis | |
| 1 | Nordic providers | Data to compare the Nordic countries | |
| 1 | Denmark, national level (Ministries, Parliament) | Statistics for policy use: development and follow up | |
| 1 | OECD |
|
|
| 2 | National: Managers of R&D institutes, researchers and their associations | Data for policy use: influence policy makers and make comparisons | |
| 3 | National media | Statistics used as a part of the total public R&D. | |
| 4 | National and Nordic researchers and students | Statistics for analysis, including micro data. |
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 so far. Instead, we have a combined user/provider-group for the public R&D statistics (HES+GOV+PNP), where the statistics is discussed and the questionnaire finally agreed. |
| User satisfaction survey specific for R&D statistics | Yes |
| Short description of the feedback received | not available |
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 Frascati Manual. Not applicable for sensus with 100 pct. responses.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | 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 | 2008 | Yearly | 0 | 0 | ||
| Type of R&D | 2008 | Yearly | 0 | 0 | ||
| Type of costs | 2008 | Yearly | 0 | 0 | ||
| Socioeconomic objective | 2008 | Yearly | 0 | 0 | ||
| Region | 2008 | Yearly | 0 | 0 | ||
| FORD | 2008 | Yearly | 0 | 0 | ||
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | 2008 | Yearly | 0 | 0 | ||
| Function | 2008 | Yearly | 0 | 0 | ||
| Qualification | 2008 | Yearly | 0 | 0 | ||
| Age | N | |||||
| Citizenship | N | |||||
| Region | 2008 | Yearly | 0 | 0 | ||
| FORD | 2008 | Yearly | 0 | 0 | ||
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | 2008 | Yearly | 0 | 0 | ||
| Function | 2008 | Yearly | 0 | 0 | ||
| Qualification | 2008 | Yearly | 0 | 0 | ||
| Age | N | |||||
| Citizenship | N | |||||
| Region | 2008 | Yearly | 0 | 0 | ||
| FORD | 2008 | Yearly | 0 | 0 | ||
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2 | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
| Gross Domistic Expenditure on R&D | Source of Funds | By Sector of Performance (also available by field of science) |
*Danish National Research Foundation *Advanced Technology Foundation *The Danish Council for independent Research *The Dansih Council for Strategic Research *The Dansih Council for Technology and Innovation *Other Gov. sources *Greenlands Self Government *Faroe Islands Self Government *Regional and muncipality funds *Other Public funds *Danish Business Enterprises *Other Danish Private Sources *European Union *Foreign Business enterprises *Other Foreign Sources |
||
| Gross Domistic Expenditure on R&D | Type of Costs | By Sector of Performance (also available by field of science (of unit)) |
Current Costs *Labour Costs *Labour Costs for services for R&D *Other Current Costs Parent units budget *Current costs Capital Costs Units internal budget *Land & Buildings *Equipment & Instruments Parent units budget *Capital Costs |
||
| Gross Domistic Expenditure on R&D TOTAL R&D PERSONNEL |
Total Domistic Expenditure TOTAL R&D PERSONNEL (HC) TOTAL R&D PERSONNEL (FTE) |
By Field Of Science | *by subfield | ||
| TOTAL R&D PERSONNEL | Occupation | By Sector of employment (also available by field of science(of unit)) |
*Researcher with Completed PhD *Researcher with completed masters *Other Resserchers *Scholarships *Technical Staff *Administrative Staff |
||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year.
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
- 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 | 5 | 4 | 2 | 3 | 1 | +/- | |
| Total R&D personnel in FTE | 5 | 4 | 1 | 3 | 2 | +/- | |
| Researchers in FTE | 5 | 4 | 1 | 3 | 2 | +/- | |
- 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 | 4 | ||||
| Total R&D personnel in FTE | 5 | ||||
| Researchers in FTE | 5 |
- 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
- 'Good' = In the event that at least one out of the three criteria described above 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
No variance in a census.
13.2.1.2. Coefficient of variation for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | None (full target population in sample) |
| Government | None (full target population in sample) |
| Higher education | None (full target population in sample) |
| Private non-profit | None (full target population in sample) |
| Rest of the world | None (full target population in sample) |
| Total | None (full target population in sample) |
13.2.1.3. Coefficient of variation for R&D expenditure by function and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | None (full target population in sample) |
| Technicians | None (full target population in sample) | |
| other support staff | None (full target population in sample) | |
| Qualification | ISCED 8 | Not in survey (Qualification by national standard) |
| ISCED 5-7 | Not in survey (Qualification by national standard) | |
| ISCED 4 and below | Not in survey (Qualification by national standard) |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors : It is the assessment of Statistics Denmark that there are no coverage errors concerning R&D in the Government sector.
- Measures taken to reduce their effect: no measures taken.
- Share of PNP (if PNP is included in GOV): PNP is not included in the Government sector.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
- Description/assessment of measurement errors:
Measurement errors are estimated to be very limited. A main reason being that the population is very much the same from year to year, with relativelys few new respondents.
- Measures taken to reduce their effect:
Contact with respondents e.g. if figures are very different from previous year, if data is not filled in etc.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey).
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
| 88 | 88 | 0 |
13.3.3.2. Item non-response - rate
Definition: Un-weighted Item Non-Response Rate (%) = 1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample) * 100.
13.3.3.2.1. Un-weighted item non-response rate
| R&D variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| N/A | There is no study available showing item response rate. The method of data collection, the current resorce situation and for soome variables the questionnaire itself, makes such a study unfeassible for the immediate future. | |
13.3.3.3. Measures to increase response rate
We have contacts at every institution and public institutions have to report to Statistics Denmark according to Act on Statistics Denmark.
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 | The data is keyed in using Genstat, collected through electronic online questionaires or receieved from other electronic sources. |
| Estimates of data entry errors | Estimated to be low. When an unlikely value is entered Genstat will imediately mark the data as suspicious, so errors can be corrected during the entry process. |
| Variables for which coding was performed | No manual coding is performed. |
| Estimates of coding errors | 0 |
| Editing process and method | Validation in excel files |
| Procedure used to correct errors | The institutions may be contacted during the validation work. In a few cases data from last year has to be used (cold-decking). |
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: Ult. 2021.
- Date of first release of national data: 21st December 2022.
- Lag (days): 342.
14.1.2. Time lag - final result
- End of reference period: Ult. 2021.
- Date of first release of national data: 14th December 2023.
- Lag (days): 687.
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | ||
| Delay (days) | ||
| Reasoning for delay | Shift in personnel. Three out of four persons working with these statistics are completely new to the area | Shift in personnel. Three out of four persons working with these statistics are completely new to the area |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
University hospitals were included up to 2001.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197, Frascati manual and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
| R&D personnel | FM2015 Chapter 5 (mainly paragraph 5.2). | No | OK, description presented for the reporters |
| Researcher | FM2015, § 5.35-5.39. | No | OK, description presented for the reporters |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | OK, persons engaged in R&D at a given date |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | OK, data based on FTE during the calendar year |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | ||
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No | OK |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | OK |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | OK |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | OK |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No | OK, since 2002 |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | OK, no borderline institutions |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | OK |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | OK |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No | OK |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | No | OK |
| Survey questionnaire / data collection form | No | |
| Cooperation with respondents | No | OK, see former sections |
| Data processing methods | No | OK |
| Treatment of non-response | No | No relevant |
| Variance estimation | No | |
| Data compilation of final and preliminary data | No |
15.2. Comparability - over time
Comparability over time: is very high as the questionnaire has remained overall unchanged, and a large part of the respondents have been the same.
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) | 2008-2021 | ||
| Function | 2008-2021 | ||
| Qualification | 2008-2021 | ||
| R&D personnel (FTE) | 2008-2021 | ||
| Function | 2008-2021 | ||
| Qualification | 2008-2021 | ||
| R&D expenditure | 2008-2021 | ||
| Source of funds | 2008-2021 | ||
| Type of costs | 2008-2021 | ||
| Type of R&D | 2008-2021 | ||
| Other | 2008-2021 |
- 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 full target population in sample. The statistics is compiled by questionnaire.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Besides the statistics concerning the public sector there are also statistics regarding the business sector and in National Accounts. The Research and Development in the public sector statistics covers research and development performed in the public sector. I.e. it also includes some research and development financed by private sources. Research and development in the public sector in National Accounts covers research and development financed by the public sector. I.e. it also includes some research and development performed by the private sector.
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 |
There are no comparable statistics available. TheR&D budget appropriations statistics is not comparable, as budgets may strech over a period of several years, and the reference period will therefore not be comparable.
15.4. Coherence - internal
The internal coherence of the data is high. Many checks of the internal coherence are carried out, and data is - if neccesary - corrected.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | |||
| Final data (delivered T+18) | 1.998.399.000 | 2.196 | 1.659 |
| Difference (of final data) |
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) | 592.107 |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Note: Statistics Denmark has not collected data on internal and external R&D personnel in 2021. F.t.e. and labour costs thus includes both internal and external R&D personnel. |
- 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 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 | ||
- 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) | ||
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
- 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 | Research and development in the public sector |
| Type of survey | Census |
| Combination of sample survey and census data | |
| Combination of dedicated R&D and other survey(s) | GOV and PNP: Target population covered by full census |
| Sub-population A (covered by sampling) | GOV and PNP: Full target population |
| Sub-population B (covered by census) | None |
| Variables the survey contributes to | R&D expenditure by type of cost, funding, type of R&D, regional, Field of Science (2-digit), strategic topic, socio-economic objectives (NORDFORSK); |
| Survey timetable-most recent implementation | Start:t+3; Nowcasting:t+10; National publication:t+13;Reporting to EU:t+18 |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Institutes, departments, centres | ||
| Stratification variables (if any - for sample surveys only) | |||
| Stratification variable classes | |||
| Population size | |||
| Planned sample size | Census | ||
| Sample selection mechanism (for sample surveys only) | |||
| Survey frame | R&D performers last year; information from ministries and other | ||
| Sample design | Full Target population included in survey | ||
| Sample size | Full Target population included in survey | ||
| Survey frame quality | Good |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | |
| Description of collected data / statistics | |
| Reference period, in relation to the variables the survey contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | The individual R&D units. However, where there are more units in one institution, some of the information is provided from the central office of the institution. |
| Description of collected information | One questionnaire |
| Data collection method | A number of questionnaires is sent to central contacts at each institution. Reminders, by post, e-mail and phone is used. |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | |
| Incentives used for increasing response | |
| Follow-up of non-respondents | |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | |
| R&D national questionnaire and explanatory notes in the national language: | Questionnaire for public institutions |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
The methodology is decribed in danish in the attached publication:"Forskning og udvikling i den offentlige sektor, Kvalitetshåndbog" Summary in english.
Annexes:
Kvalitetshåndbog_fouoff
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Not relevant.
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | The R&D survey is carried out every year. |
| Data compilation method - Preliminary data | The validation has taken place within 16 months after the end of the calendar year. The data transmitted within 10 monts is based on previous years reportingadjusted for the development in the national R&D budget. |
18.5.3. Measurement issues
| Method of derivation of regional data | No problem for almost all units a few units report R&D performed in other regions. |
| 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 a standard key to NABS exists, see Table 8.2, Frascati Manual. |
18.5.4. Weighting and estimation methods
| Description of weighting method | No weighting |
| Description of the estimation method | The calibrated weights are used in all estimations. The coefficient of variation and confidence intervals are calculated using the SAS-macro CLAN. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No further comments.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
See below.
GOV and PNP: The statistical units are the smallest homogeneous unit predominantly involved in only one of the six major fields
of science and technology and for which a complete or almost complete set of factor input data can be obtained.
Neither the HES, the GOV nor the PNP data are combined with data collected from legal business entities.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested.
Calendar year.
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 defined in the Frascati manual as being the smallest homogeneous unit predominantly involved in only one of the six fields of science and for which a complete (or almost complete) set of factor input data can be obtained. In the Danish terminology this means the individual institute in universities, a centre and the individual department in university hospitals.
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.
Comparability over time: is very high as the questionnaire has remained overall unchanged, and a large part of the respondents have been the same.