Research and development (R&D) (rd)

1.1. Contact organisation

Statistics Sweden.

1.2. Contact organisation unit

Economic Statistics and Analysis

Innovation, Business sector production and Research

Statistics Sweden

1.3. Contact name

Restricted from publication

1.4. Contact person function

Restricted from publication

1.5. Contact mail address

Statistics Sweden

Att. Elin Stendahl

ESA/NUP/INF

Solna Strandväg 86, Solna

SWEDEN

 

1.6. Contact email address

Restricted from publication

1.7. Contact phone number

Restricted from publication

1.8. Contact fax number

Restricted from publication

7 October 2025

2.1. Metadata last certified

28 October 2025

2.2. Metadata last posted

28 October 2025

2.3. Metadata last update

28 October 2025

3.1. Data description

Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists 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. 

3.2. Classification system

• The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
• The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
• The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
• The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
• The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
• The BERD by industry orientation is based on the Statistical classification of products by activity (CPA) 

3.3. Coverage - sector

Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.

3.3.1. General coverage

Definition of R&D

R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.

R&D definition used is in line with the Frascati Manual (FM) definition.

3.3.2. Sector institutional coverage

Business enterprise sector (BES)	Included are all active public and private business enterprises with ten or more employees. All research institutes, regardless of size, are included. Enterprises from all NACE codes (A to U) are included. Inclusion in BES is based on the SNA institutional sector classification. The affiliation of enterprises is obtained from the Statistical Business Register. Enterprises belonging to HES are excluded.
Hospitals and clinics	Private hospitals and clinics are included in BES. Hospitals and medical centres owned by county councils are included in the GOV.
Inclusion of units that primarily do not belong to BES and the borderline cases.	Units that primarily do not belong to BES are excluded. Only units identified as belonging to BES are included.

3.3.3. R&D variable coverage

R&D administration and other support activities	R&D administration and other supporting activities are included in R&D labour costs and R&D personnel. Exclusions of indirect supporting activities are made in line with FM §2.122.
External R&D personnel	External R&D personnel (HC and FTE) are collected separately by gender and occupation. External personnel are included in total R&D personnel delivered to Eurostat.
Clinical trials: compliance with the recommendations in FM §2.61.	Not specifically mentioned in the actual questionnaire. However, in the instructions for the survey it is specified that phase 1-3 clinical trials should be included in the reporting of R&D and that phase 4 trials should be excluded. The exclusion of phase 4 clinical trials is in practice hard to verify.

3.3.4. International R&D transactions

Receipts from rest of the world by sector - availability	The BES survey covers funding from the rest of the world. By the following categories: other enterprises within the same group enterprises outside the group foreign government entities EU other international organisations private non-profit organisations.
Payments to rest of the world by sector - availability	The BES survey covers funding to the rest of the world. By the following categories: other enterprises within the same group enterprises outside the group higher education institutions foreign government entities international organisations incl. EU private non-profit organisations.
Intramural R&D expenditure in foreign-controlled enterprises – coverage	The BES survey covers R&D expenditure in foreign-controlled enterprises as foreign-controlled enterprises are included in the R&D population. We can distinguish between foreign-controlled affiliates and domestic enterprises. IFATS statistics regarding R&D is based on microdata from the BES R&D survey.

3.3.5. Extramural R&D expenditures

According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).

Data collection on extramural R&D expenditure (Yes/No)	Yes.
Method for separating extramural R&D expenditure from intramural R&D expenditure	The BES survey collects data for both extramural and intramural R&D. The distinction between the two are made by having questions in the questionnaire which asks for intramural R&D and extramural R&D separately. The respondents are also provided with the definitions of intramural/extramural R&D in the beginning of the survey as well as in relation to each question to help with the distinction.
Difficulties to distinguish intramural from extramural R&D expenditure	The questionnaire makes clear the distinction between intramural and extramural R&D by having two separate questions as well as clear definitions of the types of R&D expenditure the respondent is asked to report. However, there could be some difficulties for some respondents to distinguish between purchase of a service used for R&D (intramural R&D, other current costs) and extramural R&D. There might be some double counting as a result of the implementation of the statistical unit enterprise. A legal unit within an enterprise might purchase R&D from another legal unit in the enterprise. As the legal units are the responding unit the same cost might be reported as both intramural and extramural R&D at the enterprise level. It is not possible to clear for internal flows as legally we cannot let a legal unit know which other legal units are in the same enterprise unit as them.

3.4. Statistical concepts and definitions

Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.

3.4.1. R&D expenditure

Coverage of years	By calendar year.
Source of funds	Data are collected for each source of funds as identified in FM §4.104-4.108 table 4.3. For the external sources of funds, transfer funds are distinguished from exchange funds.
Type of R&D	Type of R&D is available according to FM guidelines in section 2.5. Intramural R&D expenditure are broken down by basic research, applied research and experimental development.
Type of costs	The BES survey collects a detailed breakdown of current costs and capital costs. Current costs are distinguished by labour cost; cost for external R&D personnel; and other operating expenses (excl. costs for external personnel). Capital costs are distinguished by land and buildings; machinery and equipment; capitalised computer software; and other intellectual property products. In section 4.4 of the Frascati manual it is described that Capital expenditures are the annual gross amount paid for the acquisition of fixed assets that are used repeatedly or continuously in the performance of R&D for more than one year. We make no distinction in our questionnaire regarding the time the fixed asset has to have been used in R&D-performance. All acquisition of fixed assets (according to the enterprises' accounting systems) used in R&D is included, in order to make the question answerable. Otherwise in line with Frascati manual recommendations.
Economic activity of the unit	The main economic activity of the institution conducting the R&D activity is used, in line with FM. The enterprises economic activity is obtained from the National Business Register.
Economic activity of industry served (for enterprises in ISIC/NACE 72)	Not collected in the BES survey.
Product field	Data is collected according to product field by NACE classification at 2-digits.
Defence R&D - method for obtaining data on R&D expenditure	No specific method for obtaining data on R&D expenditure for defence.

3.4.2. R&D personnel

See below.

3.4.2.1. R&D personnel – Head Counts (HC)

Coverage of years	Total number of persons in R&D on 31 December 2023.
Function	Only the functional categories “researchers” and “other supporting staff” are collected. Staff other than researchers is not broken down by “technicians & equivalent staff” and “other supporting staff”.
Qualification	From 2007 only one level of formal education is separately collected, ISCED 8.
Age	No breakdown by age is available for the Business Enterprise sector.
Citizenship	No breakdown by citizenship is available for Business Enterprise sector.

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

Coverage of years	Calendar year.
Function	Only the functional categories “researchers” and “other supporting staff” is collected. Staff other than researchers is not broken down by “technicians & equivalent staff” and “other supporting staff”.
Qualification	No breakdown by formal qualification is available for the Business Enterprise sector.
Age	No breakdown by age is available for the Business Enterprise sector.
Citizenship	No breakdown by citizenship is available for Business Enterprise sector.

3.4.2.3. FTE calculation

The BES questionnaire request information on the number of FTE performed on R&D during the reference year.

The FTE is defined as work on R&D performed by one full-time employed person during one year. The FTE should, according to the questionnaire, be reported with an accuracy of 0.01

3.5. Statistical unit

The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.

The statistical unit is the enterprise, but the observation unit is the legal unit. As the population is all enterprises which are presumed R&D performers and/or funders mostly legal units that were identified as R&D performers/funders were included in the frame. Hence for most enterprises the enterprise equals the observation unit. The legal unit is also the sample unit. For enterprises with 200 or more employees and enterprises in NACE 72 where no legal unit was identified as an R&D performer/funder a legal unit is chosen as a representative for the whole enterprise. In these cases, the enterprise is not the same as the observation unit. The chosen representative is the one whose characteristics are closest to the enterprise in terms of criteria such as economic activity, number of employees and net turnover.

The implementation of enterprise as defined by the Council Regulation No 1993/696 for reference year 2023 and the profiling process resulted in many complex enterprises, from approximately 30 to 50 000. This has had an effect on the R&D-statistics when presented by industry and size-class mainly, but also by region. This means that there is a time-series break for reference year 2023.

3.6. Statistical population

Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.

3.6.1. National target population

The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.

 

	Target population when sample/census survey is used for collection of raw data	Target population when administrative data or pre-compiled statistics are used
Definition of the national target population	All enterprises known or presumed to perform and/or fund R&D.
Estimation of the target population size	The size of the target population is 2 468 enterprises. These are the enterprises which reported that they either performed or funded R&D. Included is also the enterprises which did not answer the survey. The assumption that these are supposed R&D performers/funders as they have been identified as such prior to the survey was conducted.
Size cut-off point	Cut-off point is 10 employees. However, all research institutes serving the enterprise sector and all enterprises in NACE 72 are included regardless of size.
Size classes covered (and if different for some industries/services)	Size classes covered are: 10-49; 50-249; and 250+. Research institutes and enterprises in NACE 72 with less than 10 employees were included in the 10-49 size-class.
NACE/ISIC classes covered	All NACE classes are covered (NACE 01-99).

3.6.2. Frame population – Description

The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.

 

Method used to define the frame population	The statistics cover enterprises in all economic activities that, according to the National Business Register, were active in November 2023. All enterprises in sector 72 and Research Institutes/Industrial Research Institutes were also surveyed in total regardless of size class, as well as all enterprises with 200 or more employees which were not already identified in another data source.
Methods and data sources used for identifying a unit as known or supposed R&D performer	To identify known or supposed R&D performers/funders both administrative data and data from surveys were used. The administrative data were data from the Swedish Tax Agency over enterprises receiving tax deduction for R&D personnel as well as a list of enterprises receiving government grants for R&D and innovation projects. Survey data sources used to identify R&D performers/funders consisted mainly of the R&D survey and the community innovation survey (CIS). Enterprises that reported R&D expenditure from the two previous R&D survey rounds and the lates CIS survey round were identified as R&D enterprises. Other surveys used to identify R&D performers/funders were the surveys ICT usage in enterprises, and foreign trade in services. Furthermore, all active enterprises with 200 and more employees in the National Business Register and not already identified by another source were included. By using the above-mentioned sources and methods to find R&D enterprises the target population can be identified and established.
Inclusion of units that primarily do not belong to the frame population	Not included in the frame population.
Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D	Using the different data sources to identify R&D enterprises ensures that new R&D performers/funders are found and included in the target population every new survey round.
Number of “new”1) R&D enterprises that have been identified and included in the target population	2 428 enterprises were new in 2023, i.e they were not surveyed in 2021. However, some could have been surveyed in previous years.
Systematic exclusion of units from the process of updating the target population	No systematic exclusions were made.
Estimation of the frame population	4 578 enterprises. Which consists of 5 397 legal units.

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

3.7. Reference area

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

3.8. Coverage - Time

Not requested, see concept 12.3.3. (data availability).

3.9. Base period

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.

R&D expenditure is published in the following units:  Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).

R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.

Calendar year 2023.

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 the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.

The 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.

6.1.2. National legislation

Existence of R&D specific statistical legislation	No existence of R&D specific legislation at the national level.
Are respondents obliged by the national law to provide raw and administrative data:	Yes. Statistics Sweden has a mandate to regulate on the obligation to provide raw data and administrative data for business enterprises.

6.1.3. Standards and manuals

• Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
• EBS Methodological Manual on R&D Statistics

6.2. Institutional Mandate - data sharing

Not requested.

7.1. Confidentiality - policy

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.

At the level of the ESS,  the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009). 

At national level:

• Confidentiality protection required by law:

The major policy in place to ensure confidentiality and prevent unauthorised disclosure of data that identify a person or economic entity is the Public Access to Information and Secrecy Act (2009:400). There are also specific conditions concerning the confidentiality of official statistics in the Official Statistics Act (2001:99).

• Confidentiality commitments of survey staff:

Statistics Sweden has a confidentiality policy to which all survey staff must adhere. It contains guidance on the practical application of the legal acts stated above.

7.2. Confidentiality - data treatment

From 2023, data in BES sector are protected by the so-called EZS method. This method, introduced by Evans, Zayatz, and Slanta (1998: Journal of Official Statistics, 14, 537-551), adds noise to microdata to ensure table additivity and preserve links among tables. Each enterprise is assigned random values for the direction and noise factor, which are kept confidential. The perturbed values are then computed as perturbed value = original value * (1 + direction * noise factor/100), where both the direction and noise factor are applied to all values reported by the enterprise. The distribution of directions of perturbation is chosen so that it is symmetric around 0 and thus does not introduce any consistent bias.

To reduce the overall amount of noise added to the data we implemented the balancing procedure proposed by Massell and Funk (2007: Proceedings of the 2007 Third International Conference on Establishment Surveys (ICES-III), Montreal, Canada), which allows for altering directions for some enterprises. This method is applied only to cells that do not have any disclosure risk according to a used disclosure rule and is not compromising level of protection.

Although using this method would allow for disseminating results in all cells, there remain some cells on a very granular level where the estimates are uncertain: the quality and comparabilty over time cannot be guaranteed.These cells are not published and are flagged as confidential.

For more information, see Using Perturbative Methods for Magnitude Tables in Statistical Disclosure Control

8.1. Release calendar

The release policy and the release calendar are publicly available at Statistics Sweden's website.

8.2. Release calendar access

At Eurostat level this is: Release calendar - Eurostat (europa.eu)

The publication calendar is available on Statistics Sweden's website: Publishing calendar

8.3. Release policy - user access

Statistics Sweden's release policy states that all statistics must be made available to all users equally and at the same time. Statistics are always released at 8.00 am on weekdays. Users are also informed of the availability of new statistics by news releases on Statistics Sweden's website. It is possible for users to subscribe to get e-mail notifications when new statistics within a certain subject area are released. Statistics are released by being made available in the statistical database. The release policy is available on Statistics Sweden's website.

Annexes:
Release policy (Swedish)

At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data. 

National data is disseminated yearly.

10.1. Dissemination format - News release

Please see the sub-concepts 10.1 to 10.5 in the full metadata view.

10.1.1. Availability of the releases

	Availability (Y/N)1)	Links
Regular releases	Y.	R&D activity in Sweden grows in 2023
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)	Links
General publication/article	N.
Specific paper publication (e.g. sectoral provided to enterprises)	Y.	Research and development in the Business Enterprise Sektor 2023 (Only available in Swedish)

1) Y – Yes, N - No

10.3. Dissemination format - online database

An online statistical database is available on Statistics Sweden's website: Statistical database - Select table

10.3.1. Data tables - consultations

Not requested.

10.4. Dissemination format - microdata access

As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.

10.4.1. Provisions affecting the access

Access rights to micro-data	Microdata are available for research or statistical purposes. An application must be made in which the research project is described, and the use of the microdata specified. The system for researchers to access microdata stored at Statistics Sweden is called Microdata Online Access (MONA). Access is only granted if the confidentiality of data can be ensured by the receiving party.
Access cost policy	Statistics Sweden applies the principle of full cost coverage, i.e. the charge covers the actual cost of processing and producing the microdata requested.
Micro-data anonymisation rules	All microdata are anonymised. Statistics Sweden can use a common anonymisation key when microdata from several sources are requested at the same time.

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.	Only aggregate figures are available for the Business Enterprise sector.	Data are available in the online statistical database on Statistic Sweden’s website.
Data prepared for individual ad hoc requests	Y.	Both microdata and aggregate figures.	Access to microdata is only granted for research or statistical purposes. All ad hoc requests are priced at full cost coverage.
Other	N.

1) Y – Yes, N - No

10.6. Documentation on methodology

The main documentation on methodology is titled Statistikens framställning (translates to Statistical production) which is updated when new statistics are published. There is a common document covering all sectors for the R&D statistics in which the specific methodology for each sector is described. This documentation is only available in Swedish.

Annexes:
Methodology Report (Swedish)
Methodology Report (English)

10.6.1. Metadata completeness - rate

Not requested.

10.7. Quality management - documentation

Please see the sub-concept 10.7.1 in the full metadata view.

10.7.1. Documentation and users’ requests

Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.)	Statistical data is always accompanied by a quality report and a methodology report. These reports are available online on Statistics Sweden's website and follows a common standard for all official statistics in Sweden. Statistical database tables also contain footnotes in case there is important information about the data that users need to be aware of when using the data.
Requests on further clarification, most problematic issues	Sometimes users ask questions about reasons behind changes in the figures over time. We can most of the time clarify if the reason for the change between two years is because of changes in reporting or real changes, however confidentiality prevents us from giving any more detailed answers.

Annexes:
Quality Report (Swedish)
Quality Report (English)

11.1. Quality assurance

At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).

The quality management process at Statistics Sweden is described in a Quality policy. There is also a handbook on quality in official statistics which provides guidance concerning quality management and definitions and guidance on the quality criteria. The following quality criteria for official statistics are regulated by the Official Statistics Act (2001:99) and are the same as are reported in this document:

- Relevance
- Accuracy
- Timeliness
- Punctuality
- Availability and clarity
- Comparability
- Coherence

The framework for quality assurance set out in the Quality policy is a cyclic process with four steps. First is understanding legal requirements and user needs. Second is ensured processes. The third step is evaluation and analysis followed by improvement and development as the fourth step.

The first step requires a good dialog with users of the statistics. One forum for such dialog is the User Council for R&D statistics. The second step is based on standardised, efficient, and secure processes which are ensured partly by automatization and digitalisation, partly by following the standardised methods, tools and processes set up for statistical production and found in Statistikproduktionsstödet (translates to the Statistical Production Guide). The third step means that the production processes continuously need to be evaluated. One way in which this done is by a yearly survey to all producers of official statics in which they evaluate the quality of the statistics produced or published during the year. Based on the results of the evaluations, decisions are made concerning which improvement and development activities are to be prioritised over the coming period, constituting the fourth and final step before the process begins again at the first step.

Annexes:
Quality Work (In English)
Quality policy (In English)

11.2. Quality management - assessment

The methodology used is based on the Frascati Manual recommendations. The quality of the statistics is assessed regularly, and the R&D statistics meet the quality requirements. Being a census survey using several different sources to identify R&D enterprises allows us to cover all of the known or presumed R&D performing, and funding, enterprises. With the BES survey being compulsory the response rate is high, approx. 86 percent. Given the concentration of R&D expenditure to the top performers, the responses from the largest R&D enterprises are carefully reviewed and re-contact is made for clarification of any inconsistencies or changes in their responses. Measurement error is considered the most important source of error in the statistics as a result of the relatively complex concepts involved in R&D statistics which respondents are required to report on. For large enterprises with large and complex operations there is sometimes a difficulty distinguishing the R&D activities from their other activities such as innovation.

12.1. Relevance - User Needs

Please see the sub-concept 12.1.1 in the full metadata view.

12.1.1. Needs at national level

Users’ class1)	Description of users	Users’ needs
1. Institutions	Among the most important users in this class are the European Commission (through Eurostat), the Ministry of Climate and Enterprise, the Ministry of Education, the Swedish Higher Education Authority and the Swedish Research Council. Regional and local government, as well as higher education institutions are also users of R&D statistics concerning the Business Enterprise sector	Comparability over time is one of the most important requirements. The Ministry of Education in particular also require a high degree of timeliness as the statistics are used when formulating the state budget and other policy indicators. For the European Commission, comparability between member states is a priority. Some of the most important breakdowns of the statistics required by these users are: Region (NUTS 2) Sex Employment category
2. Social actors	Trade associations such as Teknikföretagen (a trade association for the Swedish industry sector) and the Swedish Association of Local Authorities and Regions are identified as some of the most important users in this class.	Comparability between groups is an important quality aspect for these users. They tend to have specific interests and want to be able to compare the development, short-and longtime trends, in those industries or sectors that they represent with other industries or sectors. Breakdown by region is the most requested by this group of users.
3. Media	Trade media is considered to be the most important users in this class.	Timeliness and accessibility are important aspects to this group of users. Press releases containing citations from experts on the statistics at the time of publication is one measure taken to better accommodate the needs of the news media. This user group tends to use the statistics for short time changes.
4. Researcher and students	Researchers and students at higher education institutions and research institutes such as RISE and the Research Institute of Industrial Economics are the most important users in this class.	Accuracy is an important quality aspect for this user class as well as comparability both over time, between groups and with other statistics. This is also a group of users who request detailed data and often microdata. Access to microdata and the possibility to make ad-hoc requests for data on other breakdowns than those that are openly available is therefore important to this group.
5. Enterprises or businesses	No mapping has been done to identify the most important users among enterprises and businesses.
6. Other	Other important users are the public.	Clarity is among the most important aspects for the general public. This user class cannot be expected to have a detailed knowledge about the concepts and definitions used in the R&D statistics which makes clarity in the documentation and in other publications important.

1)       Users' class codification

1- Institutions:

• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.

2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.

3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.

4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)

5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)

6- Other (User class defined for national purposes, different from the previous classes. )

12.2. Relevance - User Satisfaction

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

12.2.1. National Surveys and feedback

Conduction of a user satisfaction survey or any other type of monitoring user satisfaction	No user satisfaction survey has been conducted. Statistics Sweden regularly arranges meetings with our primary users to consider their suggestions for improvements.
User satisfaction survey specific for R&D statistics	No specific user satisfaction survey for R&D statistics has been conducted. There is, however, a specific user council for R&D statistics.
Short description of the feedback received	Overall user satisfaction is high. One issue that has recently been discussed is that comparability over time is very important, especially in the light of the implementation of the new interpretation of the statistical unit "enterprise" as well as the use of the EZS-method for data confidentiality.

12.3. Completeness

Please see the sub-concept 12.3.2 in the full metadata view.

12.3.1. Data completeness - rate

100%.

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.

 

	Reasons for missing cells
Preliminary variables	Not applicable, no missing cells.
Obligatory data on R&D expenditure	Not applicable, no missing cells.
Optional data on R&D expenditure	Since enterprises with fewer than 10 employees are excluded from the survey no data is collected for the size-class 0-9 employees. Estimates are only produced for the same size-class breakdown as is published nationally.
Obligatory data on R&D personnel	Not applicable, no missing cells.
Optional data on R&D personnel	In general, optional breakdowns specified in the Commission implementing regulation are not delivered since the corresponding variables are not collected. This is to avoid increasing the response burden for the respondents of the survey. The variables are also not available in other administrative sources.
Regional data on R&D expenditure and R&D personnel	Not applicable, no missing cells.

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	Changes - Description	Changes - Year of introduction	Changes - Reasons
Source of funds	1981.	Every other year.	Even years.
Type of R&D	2013.	Every other year.	Even years.
Type of costs	Y.	Every other year.	Even years.	Intellectual property products included as separate post.	2019.	Frascati manual 2015 implementation
Socioeconomic objective	N.
Region	Y.	Every other year.	Even years.	Not all enterprises are asked to distribute their R&D expenditure by region in the questionnaire. Earlier years, small enterprises' expenditure were distributed by the average distribution of larger enterprises and companies in R&D intense industries. However, since 2019 the smaller enterprises' R&D expenditure has been allocated to their seat county according to the business register.
FORD	N.
Type of institution	2017.	Every other year.	Even years.

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	Changes - Description	Changes - Year of introduction	Changes - Reasons
Sex	1999.	Every other year.	Even years.
Function	2005.	Every other year.		No separate cell for technicians.	2013.
Qualification	2007.	Every other year.	Even years.
Age	N.
Citizenship	N.
Region	Y.	Every other year.	Even years.	HC:s no longer distributed on regions in the questionnaire. Instead, the R&D expenditure regional distribution is applied on the total number of HC:s.	2017.	Since the distributions of expenditure, HC:s and FTE:s by region were very similar on the enterprise level, this change was made to reduce the response burden.
FORD	N.
Type of institution	N.
Economic activity	Y.	Every other year.	Even years.
Product field	N.
Employment size class	Y.	Every other year.	Even years.

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	Changes - Description	Changes - Year of introduction	Changes - Reasons
Sex	1997.	Every other year.	Even years.
Function	2007.	Every other year.		No separate cell for technicians.	2013.
Qualification	N.
Age	N.
Citizenship	N.
Region	2007.	Every other year.	Even years.	FTEs no longer distributed on regions in the questionnaire. Instead, the R&D expenditure regional distribution is applied on the total number of FTEs.	2017.	Since the distributions of expenditure, HCs and FTEs by region were very similar on the enterprise level, this change was made to reduce the response burden.
FORD	N.
Type of institution	N.
Economic activity	Y.	Every other year.	Even years.
Product field	N.
Employment size class	Y.	Every other year.	Even years.

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
Energy-related R&D expenditure.	2023.		By technology area.	By technology area and economic activity.	Seven different technology areas withing energy-related R&D. Based on International Energy Agency's definitions for energy-related R&D statistics.

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

12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)

Cross-classification	Unit	Frequency
No cross-classification is available.

13.1. Accuracy - overall

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

 

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

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

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

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.

13.1.1. Accuracy - Overall by 'Types of Error'

	Sampling errors1)	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	:	4	1	3	2	5	+/-
Total R&D personnel in FTE	:	4	1	3	2	5	+/-
Researchers in FTE	:	4	1	3	2	5	+/-

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 (6). If errors of a particular type do not exist, the sign ‘:‘ is used.

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.

13.1.2. Assessment of the accuracy with regard to the main indicators

Indicators	5 (Very Good)1)	4 (Good)2)	3 (Satisfactory)3)	2 (Poor)4)	1 (Very poor)5)
Total intramural R&D expenditure	X
Total R&D personnel in FTE	X
Researchers in FTE	X

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' = If at least one out of the three criteria described above is not fully met.

3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.

4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.

5) '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

See below. 

13.2.1.1. Variance Estimation Method

Not applicable. The BES survey is a census survey, all known and supposed R&D enterprises are included in the estimates. No variance estimation is needed.

13.2.1.2. Confidence interval for key variables by NACE

	Industry sector1)	Services sector2)	TOTAL
R&D expenditure			Not applicable. Census survey.
R&D personnel (FTE)			Not applicable. Census survey.

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)

13.2.1.3. Confidence interval for key variables by Size Class

	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250- and more employees and self-employed persons	TOTAL
R&D expenditure					Not applicable. Census survey.
R&D personnel (FTE)					Not applicable. Census survey.

13.3. Non-sampling error

Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.

13.3.1. Coverage error

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

 

a)       Description/assessment of coverage errors:

The BES survey does not include enterprises with less than ten employees in the frame population. The only exceptions are enterprises in NACE 72 and research institutes. The coverage between the target population and the frame population overlaps to a high degree. Some over-coverage occurs in that some enterprises identified by the sources to find R&D enterprises might not perform/fund R&D or by enterprises which are not active by the time of the survey round. However, this does not affect the quality of the data. Under coverage occurs as we might not be able to identify all existing R&D enterprises through the sources used and not including enterprises with fewer than 10 employees, the only exceptions are enterprises in NACE 72 and research institutes. The number of unidentified R&D performers/funders are negligible as the top 50 R&D performers’ share of R&D expenditure is over 70 percent of the sectors total R&D and these enterprises are known. The microenterprises contribution to total R&D expenditure is also negligible. Thus, not covering not known R&D enterprises and microenterprises is believed to have a negligible effect on the quality and comparability of the statistics.

b)       Measures taken to reduce their effect:

Some cases of over-coverage are mostly handled during the data collection period. These cases become known as respondents contact us regarding not meeting the criteria of ten or more employees, and subsequently the objects will be coded as over-coverage and excluded. 

13.3.1.1. Over-coverage - rate

Not requested.

13.3.1.2. Common units - proportion

Not requested.

13.3.1.3. Frame misclassification rate

Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.

 

By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number or surveyed enterprises in the stratum (according to frame)					Not available. The enterprises are classified into a size-class and industry based on their information in the frame. The units’ information in the frame is never updated after the initial update of the frame.
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)					Not available.
Misclassification rate					Not available.
By size class for the 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)	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number or surveyed enterprises in the stratum (according to frame)					Not available. The enterprises are classified into a size-class and industry based on their information in the frame. The units’ information in the frame is never updated after the initial update of the frame.
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)					Not available.
Misclassification rate					Not available.

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.

 

a)       Description/assessment of measurement errors:

Measurement errors are caused by the fact that the R&D definitions are complicated and that the time that respondents are willing to take to fill in the questionnaire is limited. A risk is that respondents have their own definitions of R&D (or their accounting system definition) in mind when answering, which may or may not correspond to the definitions provided in the questionnaire.

b)      Measures taken to reduce their effect:

Values are compared with corresponding values from previous survey years. There are several flags in the survey as well as in the internal tool used for evaluating the data, which are triggered by reported values too far from the correspondent value of the previous survey. A closer contact is kept with the most important R&D companies to try to make sure that they report in line with the Frascati definitions of R&D to the extent it is possible.

13.3.3. Non response error

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

There are two elements of non-response:

• Unit non-response, which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
• Item non-response, which occurs when data only on some, but not all survey variables are collected on a designated population unit.

The extent of response (and accordingly of non response) is also measured with response rates.

13.3.3.1. Unit non-response - rate

The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate.
Definition:
Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100 
Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100

13.3.3.1.1. Unit non-response rates by Size Class

	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number of units with a response in the realised sample	190 legal units	1 397 enterprises/ 1 886 legal units	1 187 enterprises/1 422 legal units	1 325 enterprises/ 1 042 legal units	3 909 enterprises/ 4 540 legal units
Total number of units in the sample	243 legal units	1 660 enterprises/ 2 224 legal units	1 379 enterprises/1 681 legal units	1 539 enterprises/ 1 249 legal units	4 578 enterprises/ 5 397 legal units
Unit Non-response rate (un-weighted)	21,8%	15,8% enterprise level/15,2% legal unit level	15,0% enterprise level/ 15,4% legal unit level	13,9% enterprise level/ 16,6% legal unit level	14,6% enterprise level/ 15,9% legal unit level
Unit Non-response rate (weighted)	Legal units with fewer than 10 employees are included in the frame however these legal units belong to enterprise units with 10 or more employees.	Not applicable. Census survey.	Not applicable. Census survey.	Not applicable. Census survey.	Not applicable. Census survey.

13.3.3.1.2. Unit non-response rates by NACE

	Industry1)	Services2)	TOTAL
Number of units with a response in the realised sample	1 316 enterprises/ 1 552 legal units	2 593 enterprises/ 2 988 legal units	3 909 enterprises/ 4 540 legal units
Total number of units in the sample	1 543 enterprises/ 1 858 legal units	3 035 enterprises/ 3 539 legal units	4 578 enterprises/ 5 397 legal units
Unit Non-response rate (un-weighted)	14,7% enterprise level/ 16,5% legal unit level	14,6% enterprise level/ 15,6% legal unit level	14,6% enterprise level/ 15,9% legal unit level
Unit Non-response rate (weighted)	Not applicable. Census survey.	Not applicable. Census survey.	Not applicable. Census survey.

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)

13.3.3.1.3. Recalls/Reminders description

In total there are three reminders sent by post to the enterprises. To important R&D performers emails are sent if they have not answered despite the reminders. This to ensure we get responses to the survey from all the biggest R&D performers.

13.3.3.1.4. Unit non-response survey

Conduction of a non-response survey	No non-response survey was conducted.
Selection of the sample of non-respondents
Data collection method employed
Response rate of this type of survey
The main reasons of non-response identified

13.3.3.2. Item non-response - rate

Definition:
Un-weighted Item non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100

13.3.3.2.1. Un-weighted item non-response rate

	R&D Expenditure	R&D Personnel (FTE)	Researchers (FTE)
Item non-response rate (un-weighted) (%)	0 %	0 %	0 %
Imputation (Y/N)
If imputed, describe method used, mentioning which auxiliary information or stratification is used

13.3.3.3. Magnitude of errors due to non-response

	Magnitude of error (%) due to non-response
Total intramural R&D expenditure	We have not estimated magnitude of errors due to non-response.
Total R&D personnel in FTE	We have not estimated magnitude of errors due to non-response.
Researchers in FTE	We have not estimated magnitude of errors due to non-response.

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	Data is collected through a web-questionnaire which are then saved in an internal IT-platform and then read into an SQL-database.
Estimates of data entry errors	No error estimates available.
Variables for which coding was performed	Not applicable. No coding was performed.
Estimates of coding errors	Not applicable. No estimates for coding error available.
Editing process and method	Throughout the questionnaire there are plausibility checks for inconsistencies or error in the reporting. These checks that warns the respondents if they have entered “non-plausible” values. If any errors or inconsistencies are found during a review of the data on a macro-/micro-level then the respondents are contacted through e-mail or phone to validate the data
Procedure used to correct errors	Errors detected are only corrected after contact with the respondents. The respondents are contacted and asked to validate their data; they are asked to clarify their reporting and give a background to any larger changes in their reporting. If their initial reporting is confirmed to be errors by the respondents their answers are adjusted directly in the IT-platform using the correct information provided by the respondent. The corrected values are then automatically updated in the system and read to the SQL-database.

13.3.5. Model assumption error

Not requested.

14.1. Timeliness

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

14.1.1. Time lag - first result

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

• End of reference period: 31 December 2023.
• Date of first release of national data: 11 July 2024.
• Lag (days): 193.

14.1.2. Time lag - final result

• End of reference period: 31 December 2023.
• Date of first release of national data: 31 October 2024.
• Lag (days): 305.

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)	10	18
Delay (days)	0	0
Reasoning for delay

15.1. Comparability - geographical

This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.

15.1.1. Asymmetry for mirror flow statistics - coefficient

Not requested.

15.1.2. General issues of comparability

Overall, international comparability is good. Divergences from FM are described in the following sections.

15.1.3. Survey Concepts Issues

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) 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 sub-chapter 5.2).	No deviations.
Researcher	FM2015, §5.35-5.39.	No deviations.
Approach to obtaining Headcount (HC) data	FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviations.	Respondents are asked to report the number of persons engaged in R&D at the end of the reference period, December 31 2023.
Approach to obtaining Full-time equivalence (FTE) data	FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics).	No deviations.	Respondents are asked to report the number of full-time equivalents performed in R&D during the reference year, 2023.
Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel	FM2015, §5.25	No deviations.	Total personnel include internal and external personnel.
Intramural R&D expenditure	FM2015 Chapter 4 (mainly sub-chapter 4.2).	Yes.	In section 4.4 of the Frascati manual it is described that Capital expenditures are the annual gross amount paid for the acquisition of fixed assets that are used repeatedly or continuously in the performance of R&D for more than one year. We make no distinction in our questionnaire regarding the time the fixed asset has to have been used in R&D-performance. All acquisition of fixed assets (according to the enterprises' accounting systems) used in R&D is included, in order to make the question answerable. Otherwise in line with Frascati manual recommendations
Special treatment for NACE 72 enterprises	FM2015, § 7.59.	No deviations.	No special treatment for NACE 72 enterprises to record economic activity of industry served.
Statistical unit	FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviations.	The statistical unit is the enterprise.
Target population	FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviations.	The target population is all enterprises known or likely to perform and/or fund R&D.
Identification of not known R&D performing or supposed to perform R&D enterprises	FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviations.
Sector coverage	FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviations.
NACE coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No deviations.
Enterprise size coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No deviations.
Reference period for the main data	Reg. 2020/1197 : Annex 1, Table 18	No deviations.
Reference period for all data	Reg. 2020/1197 : Annex 1, Table 18	No deviations.

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 (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.

 

Methodological issues	Reference to recommendations	Deviation from recommendations	Comments on national treatment / treatment deviations from recommendations
Data collection preparation activities	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation	A directory with known or likely to perform R&D is compiled before the survey round. Different sources are used to identify the R&D enterprises. Such as, previous R&D and innovation surveys, R&D tax data, list of beneficiaries of R&D/innovation grants etc.
Data collection method	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation	Census survey. Data collected through an electronic questionnaire.
Cooperation with respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation	Respondents can contact us directly, by email and phone, for any questions regarding the survey.
Follow-up of non-respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation	Several reminders by email and/or post are sent to non-respondents during the data collection period. Important R&D performers are contacted by mail or phone besides the official reminders sent as a way to ensure they respond to the survey.
Data processing methods	FM2015 Chapter 6 (mainly sub-chapter 6.7).	No deviation	Follow ups are made to respondents for clarification of missing or suspicious values to either correct or confirm the data.
Treatment of non-response	FM2015 Chapter 6 (mainly sub-chapter 6.7).	No deviation	Item non-response is essentially not possible because of the flags in the questionnaire. However, respondents might sometime have issues with reporting on the level of detailed asked for some variables such as R&D personnel by gender or function. In these cases, respondents are contacted to find the best way to estimate these breakdowns. Unit non-response is handled by using post-stratification and imputing the mean values in each strata.
Data weighting	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation
Variance estimation	FM2015 Chapter 6 (mainly sub-chapter 6.9).	Not applicable.	Census survey.
Data compilation of final and preliminary data	Reg. 2020/1197 : Annex 1, Table 18	No deviation
Survey type	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No deviation	Census survey. Known or presumed R&D performers/funders are identified using several different data sources.
Sample design	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No deviation	Census survey. All enterprises known or presumed likely to perform/fund R&D are included.
Survey questionnaire	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No deviation

15.2. Comparability - over time

For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.

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)	2009-2019	2021	2021: Total personnel (HC) now includes internal and external personnel. In previous years total personnel only included internal personnel.
Function	2013-2019	2013	2013: R&D personnel: "Technical experts" and "Other R&D personnel" were replaced with the category "Supporting R&D personnel (e.g. technical and administrative personnel)"
Qualification
R&D personnel (FTE)	2009-2019	2021	2021: Total personnel (FTE) now includes internal and external personnel. Previous years, total personnel only included internal personnel.
Function	2013-2019	2013	2013: R&D personnel: "Technical experts" and "Other R&D personnel" were replaced with the category "Supporting R&D personnel (e.g. technical and administrative personnel)"
Qualification
R&D expenditure	2009-2021	See "Other" for breaks affecting R&D expenditure
Source of funds	2009-2021	See "Other" for breaks affecting Source of funds
Type of costs	2013-2017, 2019-2021	2013, 2019	2013: Intramural R&D expenditures, capital costs: The category "capitalised computer software" was included. 2019: Cell for Intellectual property products added in the 2019 survey.
Type of R&D	2013-2021	See "Other" for breaks affecting Type of R&D
Other	2009-2021	1997, 1985, 1993, 1995, 2001, 2005, 2009, 2017, 2023	The following affect all variables: Until 1979, public and private institutes serving industry, now included in the Business enterprise sector, were included in the Government sector. As from 1981 the increased coverage of firms whose main activity is R&D (essentially ISIC rev.2, item 9320) brought an increase of some 4 to 5% in the business enterprise sector total when compared with 1979. In 1983, the changes in the industrial classification occasionally reduced the comparability of Business enterprise sector data for 1983 with those for earlier years at individual branch level. In 1985, enterprises in business services (ISIC rev. 2, 832) were included, resulting in an increase of 1% as compared to 1983. As from 1993: SSH R&D was also included in the business enterprise sector, this may affect service industries essentially); Statistics Sweden introduced a new Swedish Standard Industrial Classification, SE-SIC 92 (SNI 92), in January 1994. It replaced the former Industrial classification SNI 69. Harmonised at the four digit level with the Industrial classification NACE Rev 1, SNI 92 also goes to the five-digit level. The results of the 1993 survey of R&D in the Business Enterprise Sector were presented in SNI92 as well as SNI 69. From 1995 the results were presented in SNI 92. In order to enable analysts to compare the statistical data for 1993, 1995 and 1997 with earlier years, the main statistics for the period 1985-1991 were converted from SNI 69 to SNI 92. 1995 - A number of institutions in the PNP sector are reclassified mainly in the business enterprise sector. 2001 - Enterprises in Financial Sector (NACE Rev.1 65-67) were included in the survey, resulting in increase of 1.4% as compared to 1999. 2005 - Enterprises with 10-49 employees were included in the survey. 2009 - First time NACE REV.2 is used. 2017 – No longer possible to answer via paper questionnaire. 2023 – A few changes in design and methodology were implemented: New survey design. The survey is now conducted as a census survey. The implementation of the statistical unit “Enterprise” as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993. New methodology for confidentiality by using the EZS-method.

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

No data is collected during the even years. To produce estimates for the totals intramural R&D expenditure and R&D personnel for even years, data from the innovation survey (CIS) is mainly used. Data for reference year for each enterprise is gathered in four steps:

• For the enterprises included in the R&D survey for reference year 2023 which are also in the sample for CIS 2022-2024 data from CIS is used for the enterprise.
• In the R&D survey the enterprise is asked to forecast their R&D expenditure and number of R&D personnel (in FTE) for the even year. If the enterprise has not answered CIS by the time the estimates for even years are produced, or they are not in the sample for the CIS, the forecasted data from the R&D survey is used.
• If the enterprise has not answered or is not included in the survey sample for the CIS and has not answered the questions about forecasted R&D activities for even years, then the values for the previous odd reference year are used. The data is price adjusted using a deflator.
• Lastly, if an enterprise did not report data for the CIS and did not answer the R&D survey then industry quotients are used to estimates intramural R&D expenditure and R&D personnel. These quotients are calculated based on the reported data for R&D expenditure and personnel for each industry/industry aggregates. 

15.3. Coherence - cross domain

This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics.  Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).

The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.

15.3.1. Coherence - sub annual and annual statistics

Not requested.

15.3.2. Coherence - National Accounts

Data from the R&D survey is used as input to National Accounts The R&D statistics is fully in coherence with the National Accounts as it uses the classification ESA2010.

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
Intramural R&D			Community Innovation Survey (CIS 2024).Community Innovation Survey (CIS 2024).		Comparisons not possible between the variables. The R&D survey and CIS covers different reference years. Furthermore, the surveys differ in coverage, CIS is a sample survey and does not include enterprises from all industries. CIS also does not include all enterprises known to perform/fund R&D, only the approximately 400 biggest R&D performers/funders are included in the sample beforehand.
Extramural R&D			Community Innovation Survey (CIS 2024).		Comparisons not possible between the variables. The R&D survey and CIS covers different reference years. Furthermore, the surveys differ in coverage, CIS is a sample survey and does not include enterprises from all industries. CIS also does not include all enterprises known to perform/fund R&D, only the approximately 400 biggest R&D performers/funders are included beforehand.

15.4. Coherence - internal

Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.

15.4.1. Comparison between preliminary and final data

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

	Total R&D expenditure (in 1000 of national currency)	Total R&D personnel (in FTEs)	Total number of researchers (in FTEs)
Preliminary data (delivered at T+10)	164 237 093	95 247	83 679
Final data (delivered T+18)	166 118 923	94 499	83 231
Difference (of final data)	+1 881 830	-748	-448

 

Comments :

....

15.4.2. Consistency between R&D personnel and expenditure

	Average remuneration per year (cost in national currency)	Explanation of consistency issues if any
Consistency between FTEs of internal R&D personnel and R&D labour costs (1)	0,978 SEK million.
Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2)	1,276 SEK million.

(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)	Cost for the NSI in time use / person / day
Staff costs	Not available for the BES survey.
Data collection costs	Not available for the BES survey.
Other costs	Not available for the BES survey.
Total costs	Not available for the BES survey.

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

Comments on costs :

....

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

	Value	Computation method
Number of Respondents (R)	4 532 respondents	As the responding unit is the legal unit the number of respondents is the number of legal units who answered the survey. The number of respondents for the statistical unit were 3 913 enterprises.
Average Time required to complete the questionnaire in hours (T)1	55 minutes.	As the responding unit is the legal unit the average time is calculated based on the time reported by the legal units.
Average hourly cost (in national currency) of a respondent (C)	1 031 SEK.
Total cost	5,8 SEK million.

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. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.

18.1.1. Data source – general information

The R&D statistics for the Business enterprise sector are based on a web-questionnaire which is sent out to all legal units in the frame population. As the population surveyed is all known or presumed R&D performers/funder the survey is a census survey. The survey collects information about the units R&D expenditure, R&D personnel for the previous calandar year (odd year) as well as forecasts for R&D expenditure and personnel for the current calendar year (even year). The respondents are asked to allocate their extramural R&D by recipient, and their intramural R&D by type of cost, source of funds, product field, type of R&D and by region. For R&D personnel they are asked to report personnel by function and sex.

18.1.2. Sample/census survey information

Sampling unit	Legal units.
Stratification variables (if any - for sample surveys only)	Not applicable. Census survey.
Stratification variable classes	Not applicable. Census survey.
Population size	4 578 enterprises. Which consists of 5 397 legal units.
Planned sample size	Not applicable. Census survey.
Sample selection mechanism (for sample surveys only)	Not applicable. Census survey.
Survey frame	All active legal units in the Business Register from November 2023 categorised as belonging to the Business Enterprise Sector and which have been identified as a likely R&D performer or funder.
Sample design	Not applicable. Census survey.
Sample size	Not applicable. Census survey.
Survey frame quality	Very good.
Variables the survey contributes to	Extramural R&D expenditure, intramural R&D expenditure, and R&D personnel.

18.1.3. Information on collection of administrative data or of pre-compiled statistics

Source	No such data collection is carried out.
Description of collected data / statistics
Reference period, in relation to the variables the administrative source contributes to
Variables the administrative source contributes to

18.2. Frequency of data collection

See 12.3.3.

18.3. Data collection

Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.

18.3.1. Data collection overview

Realised sample size (per stratum)	Census survey. 5 397 legal units.
Mode of data collection	Online questionnaire.
Incentives used for increasing response	Mandatory survey. No incentives are used.
Follow-up of non-respondents	Three reminders are sent. By email to those were we have an email address, otherwise by mail.
Replacement of non-respondents (e.g. if proxy interviewing is employed)	No.
Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility)	Approximately 86 percent at the enterprises level and 84 percent at the legal unit level.
Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods)	No non-response analysis is done.

18.3.2. Questionnaire and other documents

Annex	Name of the file
R&D national questionnaire and explanatory notes in English:	Survey questionnaire in English:  'Research and development in the Business Enterprise sector 2023 (English)'. Instructions in English: ‘Instructions for R&D in the business enterprise sector 2023 (English)’.
R&D national questionnaire and explanatory notes in the national language:	Survey questionnaire in Swedish:  'Research and development in the Business Enterprise sector 2023 (Swedish)'. Instructions in Swedish: ‘Instructions for R&D in the business enterprise sector 2023 (Swedish)’.
Other relevant documentation of national methodology in English:	Documentation on the EZS-method and its implementation in the BES survey is available on the product page Using Perturbative Methods for Magnitude Tables in Statistical Disclosure Control
Other relevant documentation of national methodology in the national language:	Methodological documentation 'Production of Statistics'.

18.4. Data validation

Several measures are taken to ensure data validation. Data validations are done both at a micro and macro level. Micro validation measures consist of internal and external controls in the questionnaires to check for any reporting inconsistencies, and individual examination of large R&D performers reports. Respondents are contacted to verify or correct changes or supplement any missing data in the reporting. Data validation on a macro level consists of evaluating macrodata, totals and by requested breakdowns, comparing against previous years and to detect any outliers that need to be handled.

18.5. Data compilation

See below.

18.5.1. Imputation - rate

Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)

18.5.1.1. Imputation rate by Size class

Size class	R&D Expenditure		R&D personnel (FTE)
	Unweighted	Weighted	Unweighted	Weighted
0-9 employees and self-employed persons (optional)	Not available.	Not available.	Not available.	Not available.
10-49 employees and self-employed persons	Not available.	Not available.	Not available.	Not available.
50-249 employees and self-employed persons	Not available.	Not available.	Not available.	Not available.
250-and more employees and self-employed persons	Not available.	Not available.	Not available.	Not available.
TOTAL	Not available.	Not available.	Not available.	Not available.

18.5.1.2. Imputation rate by NACE

NACE	R&D Expenditure		R&D personnel (FTE)
	Unweighted	Weighted	Unweighted	Weighted
Industry1)	Not available.	Not available.	Not available.	Not available.
Services2)	Not available.	Not available.	Not available.	Not available.
TOTAL	Not available.	Not available.	Not available.	Not available.

 

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)

18.5.2. Data compilation methods

Data compilation method - Final data

The R&D survey only collect data biennially. To produce the data to be reported annually, data from the innovation survey (CIS) and prognosis data from the R&D survey for even reference years are used. For enterprises that were in both the CIS-sample 2024 and R&D-frame 2023, the data for intramural R&D expenditure from CIS are used. In these cases, R&D personnel is estimated and imputed based on their relation with R&D expenditure for the prognosis or 2023-data from the R&D survey. Enterprises not in the CIS survey which have reported odd year values in the R&D survey but give non-response for the even year prognosis are imputed with the odd year values which are then adjusted for inflation. If an enterprise has reported values for some even year variables but not others, the percentual change of the reported value compared to the odd year value is applied to the odd year value of the non-reported even year variable.

Data compilation method - Preliminary data

Estimation is done using the same methodology as final data, at the latest possible time before deadline to ensure as much data is available for the preliminary statistics.

18.5.3. Measurement issues

Method of derivation of regional data	Enterprises with 200 and more employees and enterprises of all sizes in R&D-intensive industries are asked to distribute their R&D expenditures regionally in the questionnaire. The proportions for the distributions of expenditure are then used to distribute the personnel variables by region. Small enterprises not in R&D-intensive industries are not asked this questions in the questionnaire, instead their R&D expenditures are allocated to the region of their county seat in accordance with the information in the Statistical Business Register.
Coefficients used for estimation of the R&D share of more general expenditure items	No coefficients are used.
Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures	VAT and depreciations are excluded in the measurement of R&D expenditure. The exclusions are mentioned in the text to the questions as well as in instructions for the questionnaire.

18.5.4. Weighting and estimation methods

Weight calculation method	Not applicable. As the BES survey is a census survey, no weights are used when the results are compiled.
Data source used for deriving population totals (universe description)	Statistics Sweden's Business Register is used.
Variables used for weighting	Not applicable. Census survey.
Calibration method and the software used
Estimation	As the BES survey is a census survey, the results are produced by summing the values.

18.6. Adjustment

Not requested.

18.6.1. Seasonal adjustment

Not requested.

No comments.

Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists 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. 

28 October 2025

Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.

The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.

The statistical unit is the enterprise, but the observation unit is the legal unit. As the population is all enterprises which are presumed R&D performers and/or funders mostly legal units that were identified as R&D performers/funders were included in the frame. Hence for most enterprises the enterprise equals the observation unit. The legal unit is also the sample unit. For enterprises with 200 or more employees and enterprises in NACE 72 where no legal unit was identified as an R&D performer/funder a legal unit is chosen as a representative for the whole enterprise. In these cases, the enterprise is not the same as the observation unit. The chosen representative is the one whose characteristics are closest to the enterprise in terms of criteria such as economic activity, number of employees and net turnover.

The implementation of enterprise as defined by the Council Regulation No 1993/696 for reference year 2023 and the profiling process resulted in many complex enterprises, from approximately 30 to 50 000. This has had an effect on the R&D-statistics when presented by industry and size-class mainly, but also by region. This means that there is a time-series break for reference year 2023.

Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.

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

Calendar year 2023.

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.

R&D expenditure is published in the following units:  Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).

R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.

See below.

Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.

At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data. 

National data is disseminated 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.

This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.

For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.