Statistics Denmark

Government Finances

Sejrøgade 11, 2100 København Ø, Denmark

Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and development (R&D) activities, and thereby provide information about the priority governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content.

Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities (FM 2015, Chapter 12), which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Businesshttps://circabc.europa.eu/ui/group/03aaf70e-70f6-42dc-81de-d809196ba05f/library/0f87d37f-4fe9-4ff1-9371-992a322119c2?p=1 Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020.

The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail (Commission Implementing Regulation (EU) 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics (europa.eu)).

Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

Distribution by socioeconomic objectives (SEO) is based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) at one digit level.

See below.

GBARD statistics are assumed to report detailed data on all the government's budget items that may support R&D activities and to measure or estimate their R&D content. For the purposes of GBARD, the Government sector comprises (a) the central (federal) government, (b) regional (state) government and (c) local (municipal) government subsectors (FM2015, Chapter 12).

Not requested.

Million DKK

See below.

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.

Not requested.

Not requested. See point 5.

Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.

Not requested.

a) Calendar year: Calendar year = Fiscal year

b) Fiscal year:

    Start month: 1

    End month: 12

See below.

Since the beginning of 2021, GBARD statistics are based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.  GBARD statistics were based until the end of 2020 on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

Paragraph 6 of the Act on Statistics Denmark (link in Danish).

-          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 - No information in this survey is confidential.

b)       Confidentiality commitments of survey staff:

 N/A - No information in this survey is confidential.

N/A - No information in this survey is confidential.

The release calendar for Statistics Denmark, including the data that is the basis for GBARD, is available here: https://www.dst.dk/en/Statistik/planlagte. The tables in question are FOUBUD, FOUBUD1, FOUBUD4, and FOUBUD5 under the subject 'Education and research' and subsubject 'Research and development'. 

The release calendar for Statistics Denmark, including the data that is the basis for GBARD, is available here: https://www.dst.dk/en/Statistik/planlagte. The tables in question are FOUBUD, FOUBUD1, FOUBUD4, and FOUBUD5 under the subject 'Education and research' and subsubject 'Research and development'. 

The release calendar for Statistics Denmark, including the data that is the basis for GBARD, is available here: https://www.dst.dk/en/Statistik/planlagte. The tables in question are FOUBUD, FOUBUD1, FOUBUD4, and FOUBUD5 under the subject 'Education and research' and subsubject 'Research and development'. 

When data is published, it becomes available in the StatBank at 8 am. Data becomes available to everyone in the public at the same time. The press, government entities, interest organisations and such do not get advance access to unpublished data.

Annually. 

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

The data is published in four tables:
FOUBUD
FOUBUD1
FOUBUD4
FOUBUD5

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

Documentation is available in the documentation section of Statistics Denmark's website. For GBARD, it can be found here: Government budget allocations for research and development

Not requested.

See below.

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.

Quality is expected to be good as data is reported by the ministries in charge of the relevant accounts and data is validated by the Ministry of Finance. Quality might be improved by a thorough reading of the text in the state budget with additional attention to the difference in research and development and other activities.

A part of the budget under 'General advancement of knowledge' is categorized as the socio-economic objective 14.7: R&D not categorized according to purpose. The is due to research foundations that focus on a broad range of objectives and thus aren't able to know in advance which objectives the budget will be spent on.

The budgets from local and regional authorities depict the development in the reported costs to research and development during previous years including the errors that might be in these statistics. There is some uncertainty in the estimates for regional and municipal funding, as these are based on R&D in the most recent available accounts (which are 1-2 years prior) as well as the total budget for the year.

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.

Not available. 

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

1)  Criteria: Obligatory data (provisional budget and final budget). Only 'Very Good' = 100% and 'Very Poor' <100% apply.

2)  Criteria: Optional data (final budget). 'Very Good' = 100%; 'Good' = >75%;'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Stage: P - provisional, F - final. 

2) Availability of the data: No, data are not available, Y: Yes, data are available + start year.

3) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

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 GBARD.

1) High level of coverage (At least all national or federal ministries and the ministries and agencies responsible for R&D funding at state or regional level). High rate of response (>90%) in data collection. All figures broken down by NABS.  

2) If at least one out of the three criteria described above would not be fully met.

3) In the event that the rate of response would be lower than 80% even by meeting the two remaining criteria.

4) In the event that the average rate of response would be lower than 70% and at least one of the two remaining criteria would not be met.

5) If all the three criteria described above are not met.

Not requested.

Not requested.

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 are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.

a)       Description/assessment of coverage errors:

There are no coverage errors concerning the central government appropriations. There will problably always remain small coverage errors when it comes to data concerning local data used for estimates of the regional and municipal budgets.

Accounts on the state budget might include activities that a deeper investigation would reveal as innovation activities instead of research and development. Balancing this, there might also be research and development activities that aren't included due to their small amounts.

b)      Measures taken to reduce their effect:

The units that make up the local data are regularly checked to ensure the list remains accurate, which will improve the estimates of the regional and municipal budgets for R&D.

Activities, where it is unclear if they relate to innovation or R&D, are examined further as time and ressources permit. 

Data is only collected from units that belong to the target population. 

Not requested.

Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values.

a)       Description/assessment of measurement errors:

 No measurement errors are expected. The majority of the data comes from one source, which avoids the risk of double-counting R&D funding. The remaining come from a handful of other sources, none of which overlap.

b)      Measures taken to reduce their effect:

 N/A

Non response errors: occur when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.

a) Problems in obtaining data from targeted information providers:

  None - all data is obtained from the information providers.

b) Measures taken to reduce their effect:

 N/A

c) Effect of non-response errors on the produced statistics:

 N/A

Not requested.

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.

a)       Data processing and editing processes:

 No processing errors are expected. The sources outside the budget of the central government which are calculated using coefficients only comprise a small share of the total budget, so any processing error within this area has limited effect.

b)      Description of errors:

 N/A

c)       Measures taken to reduce their effect:

N/A

Model assumption errors occur when the assumptions made for the estimation of parameters, models, the testing of statistical hypotheses, etc., are violated. As a result, the quality of the resulting statistics is affected (e.g. degrees of confidence might be inflated).

Description/assessment: 

N/A

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.

Date of first release of national data:

T-6

Date of first release of national data:

T+6

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)

See below.

Not requested.

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Regulation No 2020/1197, Frascati manual and the EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.

GBARD encompass all spending allocations met from sources of government revenue foreseen within the budget, such as taxation. Spending allocations by extra-budgetary government entities are within the scope only to the extent that their funds are allocated through the budgetary process (FM2015 §12.9). The following table lists a number of key methodological issues, which may affect the international comparability of national GBARD statistics.

See below.

See below.

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

GBAORD was compared with the total government-financed GERD in 2003 and 2005. Prior to 2003 no systematic comparisons between the GERD and the GBAORD series were made.

In 2003 and 2005 there was less than 1% in difference, when international appropriations are excluded from GBAORD.

Not requested.

Not requested.

This part compares GBARD statistics from the provisional and final budget for the reference year.

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’)

Statistics Denmark revises published figures in accordance with the Revision Policy for Statistics Denmark.

Not requested.

Not requested.

a)       Provisional data:

The sources consist of:

• government budget data from the Ministry of Finance
• the Danish National Research Foundation
• the Nordic Council of Ministers
• EU funds
• municipal and regional funds (from Statistics Denmark's survey)

b)      Final data:

 The sources are the same as for the provisional data

c)       General University Funds (GUF):

The sources are the same as for the provisional data

See 12.3.3.

See below.

1) Time of data collection (T+x): T is assumed to represent the end of reference period. x expresses the number of months after (positive) or before (negative) T when data is collected.

General University Funds are collected through the same method as other appropriations.

The government budget data from the Ministry of Finance is validated primarily by the Ministry of Finance themselves as they gather the relevant budget data. Once the data set is received by Statistics Denmark, it is compared to previous years to spot unusual developments and similar that might indicate incorrect data that should be investigated further.

Data from the Danish National Research Foundation, the Nordic Council of Ministers, and EU funds is collected and compared to previous years for validation.

Data regarding municipalities and regions is an estimate based on a survey and is received already validated, but is, like the other data sources, compared with data from previous years. 

See below.

0

See below.

Not requested.

Not requested.