Statistics Denmark

Science, Technology and Culture

Sejrøgade 11,
DK-2100 København Ø
Denmark

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.

• The distribution of principal economic activity and by product field 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).

See below.

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).

See below.

See below.

We ask for estimates from each unit. Some institutions still seem to be using ratios according to theemployment category. Post-graduate students performing R&D are included

No deviations

See below.

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.

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.

1)       i.e. enterprises previously not known or not supposed to perform R&D

Not requested. R&D statistics cover national and regional data.

Not requested. See point 3.5.

Not requested.

The statistical unit is the enterprise.

The statistics covers activities for the entire reference year.

See below.

- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development

- EBS Methodological Manual on R&D Statistics

Not requested.

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: N/A

b)       Confidentiality commitments of survey staff: N/A

Programs (SAS) as well as manual surveying.

The publication date appears in the release calendar. The date is confirmed some weeks before.

The Release Calender can be accessed on Statistics Denmarks English website:https://www.dst.dk/en/Statistik/planlagte.

Statistics are always published at 8:00 a.m. at the day announced in the release calendar. No one outside ofStatistics Denmark can access the statistics before they are published.

Yearly

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

StatBank Denmark, available on
http://www.statistikbanken.dk

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

See: https://www.dst.dk/da/Statistik/Publikationer/VisPub?cid=17627 where documents on the usedmethodology can be found.
The OECD's Frascati Manual defines concepts in research and development.

Not requested.

See below.

Quality management is defined as systems and frameworks in place within an organisation to manage the quality of statistical products and processes. 

Statistics Denmark follows the principles in the Code of Practice for European Statistics (CoP) and uses theQuality 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.

The quality system of Statistics Denmark is based on the 15 principles of the
European Statistics Code ofPractice (CoP) published by Eurostat:
1. Professional independence
2. Mandate for data collection 
3. Adequacy of resources
4. Commitment to quality
5. Statistical confidentiality
6. Impartiality and objectivity
7. Sound methodology
8. Appropriate statistical procedures
9. Non-excessive burden on respondents
10. Cost effectiveness
11. Relevance
12. Accuracy and reliability
13. Timeliness and punctuality
14. Coherence and comparability
15. Accessibility and clarity

Relevance is the degree to which statistics meet current and potential users’ needs. It includes the production of all needed statistics and the extent to which concepts used (definitions, classifications etc.) reflect user needs. The aim is to describe the extent to which the statistics are useful to, and used by, the broadest array of users. For this purpose, statisticians need to compile information, firstly about their users (who they are, how many they are, how important is each one of them), secondly on their needs, and finally to assess how far these needs are met.

See below.

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. )

To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.

See below.

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

Completeness is assessed via comparison of the data delivered against the requirements of CommissionImplementing Regulation (EU) No 2020/1197 of 30 July 2020.

Criteria:

A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.

B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) Y-start year, N – data not available

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'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.

2) Y-start 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:

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.

1)  Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.

2)  The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.

1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.  

2) 'Good' = In the event that at least one out of the three criteria above described would not be fully met.

3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.

4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.

5) 'Very Poor' = If all the three criteria are not met.

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.

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)

CLAN

1)        Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)

2)        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)

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.

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.

a)       Description/assessment of coverage errors: N/A

b)       Measures taken to reduce their effect: N/A

Magnitude of error (%) = (Observed Value-True Value)/ True Value (%) 

Not requested.

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.

Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.

a)       Description/assessment of measurement errors: N/A

b)      Measures taken to reduce their effect:

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.

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)

1)        Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)

2)        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)

4 reminders were sent out to non-responding enterprises, followed by a telephone reminder.

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

Not requested.

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.

Not requested.

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.

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: End of year

b) Date of first release of national data: 11 month after end of reference period

c) Lag (days): 11 month

a) End of reference period: End year

b) Date of first release of national data: 1 year and 11 month after reference period

c) Lag (days): 1 year and 11 month

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.

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

Comparability aims at measuring the impact of differences in applied statistical concepts and definitions on the comparison of statistics between geographical areas, non-geographical domains or over time. It is the extent to which differences between statistics are attributed to differences between the true values of the statistical characteristics.
The factors that may cause two statistical figures to lose comparability are attributes of the surveys that produce them. These attributes may be grouped into two major categories: (a) concepts of the survey and (b) measurement / estimation methodology.
The two following sections present lists of key attributes. Information on some of the attributes will have already been reported in previous sections of this report but they are repeated here for completeness of the lists. We provide references to the relevant earlier sections and you do not need to provide the information again.

The coherence of statistics is their adequacy to be reliably combined in different ways and for various uses. It is, however, generally easier to show cases of incoherence than to prove coherence.

When originating from a single source, statistics are coherent in that elementary concepts can be combined reliably in more complex ways. When originating from different sources, and in particular from statistical surveys of different frequencies, statistics are coherent insofar as they are based on common definitions, classifications and methodological standards. The messages that statistics convey to users will then clearly relate to each other, or at least will not contradict each other. The coherence between statistics is orientated towards the comparison of different statistics, which are generally produced in different ways and for different primary uses.

The definition of coherence: The extent to which the statistical characteristics confirm with those in other statistics such that the statistics can be expected to be used together in conjunction with, or as an alternative to.

See below.

Not requested.

No general 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.

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.

See below.

See below.

1)       Breaks years are years for which data are not fully comparable to the previous period.

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.

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. 

Not requested.

Business enterprise R&D expenditures is a primary source to the National Accounts.

Not applicable

Not requestet

See below.

This part compares key R&D variables as preliminary and final data.

(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. 

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

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’)

Not requested.

Not requested.

Not requested.

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.

See 12.3.3.

See below.

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.

See below.

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

1)       Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)

2)       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)

Not requested.

Not requested.