Research and development (R&D) (rd)

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

Sejrøgade 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 2021

2.2. Metadata last posted

31 October 2021

2.3. Metadata last update

31 October 2021

3.1. Data description

Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit 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.

The 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.

Statistics on science, technology and innovation were collected based on the Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.

3.2. Classification system

The distribution of principal economic activity and by product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
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 based on Classification and distribution by Fields of Research and Development (FORD).

3.2.1. Additional classifications

Additional classification used	Description

3.3. Coverage - sector

See below.

3.3.1. General coverage

Definition of R&D
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)

3.3.2. Sector institutional coverage

Private non-profit sector	Private institutions not elsewhere classified in a separate sector.
Inclusion of units that primary don`t 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
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
R&D expenditure of foreign affiliates - 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) 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

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	average number of persons employed during the calendar year
Function	More detailed than needed for international reporting
Qualification	Not included
Age	Not included
Citizenship	Not included

3.4.2.2. R&D personnel – Full Time Equivalent (FTE)

Coverage of years	Institutions performing defence R&D are included in the relevant sectors and receive the same questionnaire as other institutions in the same sector.
Function	More detailed than needed for international reporting
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

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.

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 PNP 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

3.7. Reference area

Not requested.

3.8. Coverage - Time

Not requested. See point 5.

3.9. Base period

Not requested.

PNP sector includes all non-profit institutions serving households (NPISH), as defined in ESA 2010, except those classified as part of the Higher education sector. Such units have a heterogeneous legal nature and can even be included in the PNP sector simply because they do not match the requirements for being included in any other sector. The inclusion in the SNA list S.15 is thus the main criterion to be applied to identify and to describe them.

calendar year

6.1. Institutional Mandate - legal acts and other agreements

See below.

6.1.1. European legislation

Legal acts / agreements

Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4)of Regulation(EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.

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

OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities

6.2. Institutional Mandate - data sharing

Not requested.

7.1. Confidentiality - policy

Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.

A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.

a) Confidentiality protection required by law:

b) Confidentiality commitments of survey staff:

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: https://www.dst.dk/en/Statistik/planlagte.

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
Ad-hoc releases

1) Y - Yes, N – No

10.2. Dissemination format - Publications

See below.

10.2.1. Availability of means of dissemination

Mean of dissemination	Availability (Y/N)¹	Content, format, links, ...
General publication/article (paper, online)
Specific paper publication (e.g. sectoral provided to enterprises) (paper, online)

1) Y – Yes, N - No

10.3. Dissemination format - online database

http://www.statbank.dk/statbank

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 paper publication is priced, but 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
CD-ROMs
Data prepared for individual ad hoc requests
Other

1) Y – Yes, N - No

10.6. Documentation on methodology

See: https://www.dst.dk/da/Statistik/Publikationer/VisPub?cid=17628 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.)
Request on further clarification, most problematic issues
Measure to increase clarity
Impression of users on the clarity of the accompanying information to the data

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) 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
User satisfaction survey specific for R&D statistics
Short description of the feedback received

12.3. Completeness

See below.

12.3.1. Data completeness - rate

The statistics is complete according to the Commission Regulation and the guidelines from the Frascati Manual.

12.3.2. Data availability

Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D):

12.3.2.1. Incorporation PNP sector in another sector

Incorporation of PNP in another sector	The GOV, HES and PNP sectors are covered by the same survey, using the same methodology. The PNP sector uses the same questionnaire as the GOV sector and part of the HES Sector. The Quality Report for the PNP sector is incorporated under the GOV Quality Report, as the methodology for these to sectors is largely identical, and several issues cannot be treated in isolation.
Reasons for not producing separate R&D statistics for the PNP sector	Data from the PNP sector is available as a subgroup of the combined GOV, HES and PNP statistic. A separate survey is seen as cost ineffective due to the small size of the sector.
Share of PNP expenditure in the total expenditure of the other sector	1.35% (share of GOV+HES+PNP)
Share of PNP R&D Personnel in the respective figure of the other sector	1.09% (share of Headcount in GOV+HES+PNP)

12.3.2.2. Non-collection of R&D data for the PNP sector

Reasons for not compiling R&D statistics for the PNP sector
*PNP R&D expenditure/ GERD100)**
Share of PNP R&D Personnel in the respective figure of the total national economy

12.3.2.3. Data availability on more detail level

Additional dimension/variable available at national level¹⁾	Availability²	Frequency of data collection	Breakdown variables	Combinations of breakdown variables	Level of detail

1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 995/2012 (neither as 'optional').

2) Y-start year

13.1. Accuracy - overall

Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).

Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:

1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.

2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

13.2. 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)

Coefficient of variation for Total R&D expenditure :

Coefficient of variation for Total R&D personnel (FTE) :

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.

a) Extent of non-sampling errors:

b) Measures taken to reduce the extent of non-sampling errors:

c) Methods used in order to correct / adjust for such errors:

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.

13.3.1.1. Over-coverage - rate

All known institutions are included, a little number of PNP have R&D as their main activity. Over coverage is not possible as the PNP's are excluded from the business sector.

13.3.1.2. Common units - proportion

Not requested.

13.3.2. Measurement error

Not requested.

13.3.3. Non response error

Not requested.

13.3.3.1. Unit non-response - rate

Not requested.

13.3.3.2. Item non-response - rate

Not requested.

13.3.4. Processing error

Not requested.

13.3.5. Model assumption error

Not requested.

14.1. Timeliness

Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.

14.1.1. Time lag - first result

Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)

a) End of reference period:

b) Date of first release of national data:

c) Lag (days):

14.1.2. Time lag - final result

a) End of reference period:

b) Date of first release of national data:

c) Lag (days):

14.2. Punctuality

Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.

14.2.1. Punctuality - delivery and publication

Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)

14.2.1.1. Deadline and date of data transmission

	Transmission of provisional data	Transmission of final data
Legally defined deadline of data transmission (T+_ months)	10	18
Actual date of transmission of the data (T+x months)
Delay (days)
Reasoning for delay

15.1. Comparability - geographical

See below.

15.1.1. Asymmetry for mirror flow statistics - coefficient

Not requested.

15.1.2. General issues of comparability

Probably a little underestimated, but that is very little compared to the total R&D of Denmark.

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 995/2012 or Frascati manual 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).
Researcher	FM2015, § 5.35-5.39.
Approach to obtaining Headcount (HC) data	FM2015, § 5.58-5.61 (in combination with the Eurostat's harmonised Methodological Guidelines).
Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel	FM2015, §5.25
Approach to obtaining FTE data	FM2015, § 5.49-5.57 (in combination with the Eurostat's harmonised Methodological Guidelines).
Intramural R&D expenditure	FM2015,Chapter 4 (mainly paragraph 4.2).
Statistical unit	FM2015, § 10.40-10.42 (in combination with the Eurostat's harmonised Methodological Guidelines).
Target population	FM2015, § 10.40-10.42 (in combination with the Eurostat's harmonised Methodological Guidelines).
Sector coverage	FM2015, § 10.2-10.8 (in combination with the Eurostat's harmonised Methodological Guidelines).
Reference period for the main data	Reg. 995/2012: Annex 1, section 1, § 4-6.
Reference period for all data	Reg. 995/2012: Annex 1, section 1, § 4-6.

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
Survey questionnaire / data collection form
Cooperation with respondents
Data processing methods
Treatment of non-response
Data compilation of final and preliminary data

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)
Function
Qualification
R&D personnel (FTE)
Function
Qualification
R&D expenditure
Source of funds
Type of costs
Type of R&D
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

15.3. Coherence - cross domain

See below.

15.3.1. Coherence - sub annual and annual statistics

Not requested.

15.3.2. Coherence - National Accounts

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 PNP R&D expenditure (in 1000 of national currency)	Total PNP R&D personnel (in FTEs)	Total number of PNP researchers (in FTEs)
Preliminary data (delivered at T+10)
Final data (delivered T+18)
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)
Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2)

(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).

(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).

The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.

16.1. Costs summary

	Costs for the statistical authority (in national currency)	% sub-contracted¹⁾
Staff costs
Data collection costs
Other costs
Total costs
Comments on costs

1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.

16.2. Components of burden and description of how these estimates were reached

	Value	Computation method
Number of Respondents (R)
Average Time required to complete the questionnaire in hours (T)¹
Average hourly cost (in national currency) of a respondent (C)
Total cost

1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)

17.1. Data revision - policy

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 the public sector
Type of survey	Census. However, small institutions with no or little continuous R&D are omitted
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)
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
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	N/A
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	N/A
Description of collected data / statistics	N/A
Reference period, in relation to the variables the survey contributes to	N/A

18.2. Frequency of data collection

Yearly

18.3. Data collection

See below.

18.3.1. Data collection overview

Information provider
Description of collected information
Data collection method
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. 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:
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)
Data compilation method - Preliminary data

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.

31 October 2021

See below.

Not requested.

calendar year

Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:

1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.

2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

See below.

Yearly

See below.