Research and development (R&D) (rd)

1.1. Contact organisation

Statistics Austria

1.2. Contact organisation unit

Research and Digitalisation

Directorate Social Statistics

1.3. Contact name

Andreas Schiefer

Irmgard Frey

1.4. Contact person function

Andreas Schiefer

Deputy Head of Research and Digitalisation

Project manager R&D in the Business Enterprise Sector

Irmgard Frey

Deputy project manager R&D in the Business Enterprise Sector

1.5. Contact mail address

Guglgasse 13

1110 Wien

1.6. Contact email address

andreas.schiefer@statistik.gv.at

irmgard.frey@statistik.gv.at

 

 

1.7. Contact phone number

+43 1 77128 – 7162 (Andreas Schiefer)

+43 1 71128 – 7296 (Irmgard Frey)

1.8. Contact fax number

Not required.

22 August 2025

2.1. Metadata last certified

30 July 2025

2.2. Metadata last posted

22 August 2025

2.3. Metadata last update

22 August 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).

The guiding document to preparing the quality reports is the European Statistical System (ESS)  for Quality and Metadata Reports — re-edition 2021.

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

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 identical to the FM definition.

3.3.2. Sector institutional coverage

Business enterprise sector (BES)	All private and public enterprises that are potential R&D performers (8,207 legal units). Institutes that perform R&D for enterprises (44 legal units: full members of Austrian Cooperative Research, COMET competence centres) All NACE classes (A - U) and size classes are included. Region is determined on the level of the legal unit.
Hospitals and clinics	Private hospitals are included, but practically irrelevant for R&D.
Inclusion of units that primarily do not belong to BES and the borderline cases.	No such units are known.

3.3.3. R&D variable coverage

R&D administration and other support activities	Personnel working exclusively for R&D administration and their costs are included in labour costs and R&D personnel. General support activities can be included in "other current costs" as part of the "overhead costs".
External R&D personnel	Enterprises are asked to include their external R&D personnel in R&D personnel and simultaneously report their costs as "other current costs". Internal and external R&D personnel are not separately available.
Clinical trials: compliance with the recommendations in FM §2.61.	In line with FM recommendation. Clinical trials phase 1, 2 and 3 are considered R&D.

3.3.4. International R&D transactions

Receipts from rest of the world by sector - availability	In the question on funding of R&D the following categories can be distinguished: - by EU, by international organisations, by foreign enterprises of the same enterprise group, other foreign enterprises, other foreign sources.
Payments to rest of the world by sector - availability	Extramural R&D expenditures are surveyed for the business enterprise sector, classified by: To foreign affiliates, to other enterprises of the same enterprise group, to other foreign enterprises, to other foreign public institutions, to international organisations, to other foreign institutions n.e.c.
Intramural R&D expenditure in foreign-controlled enterprises – coverage	Foreign-controlled enterprises are fully covered. R&D micro-data are enriched with information from FATS statistics adding the information, if an enterprise is foreign-controlled and if so, by which country. R&D expenditures and R&D personnel (headcounts) are analysed by staff responsible for FATS statistics. In the framework of R&D statistics, respective tables on intramural R&D expenditure, R&D personnel (FTE) are published as well as information by country of headquarter of the foreign-controlled enterprise. All enterprises that are relevant for R&D statistics are analysed in that respect. FATS statistics itself are compiled by the unit at Statistics Austria responsible for SBS. FATS in R&D is based on micro-data from the 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. The following classifications are distinguished: a) Domestic: to domestic enterprises of the same enterprise group, to other domestic enterprises, to universities and other higher education institutions, to other public institutions, to PNP institutions, to co-operative research institutes. b) Foreign: to foreign affiliates, to other foreign enterprises of the same enterprise group, to other foreign enterprises, to other foreign public institutions, to international organizations, to other foreign institutions n.e.c.
Method for separating extramural R&D expenditure from intramural R&D expenditure	Extramural R&D expenditure are collected in a different part of the questionnaire than intramural R&D expenditures. Explanations to distinguish the two concepts as listed in the Frascati Manual are provided to the respondents. Purchases of raw materials, components, software, services which are made in the framework of an R&D project of the enterprise are considered as intramural R&D, only R&D assignments to other institutions are considered extramural R&D. The distinction is highlighted in the extensive explanatory notes for the respondents.
Difficulties to distinguish intramural from extramural R&D expenditure	During the field phase it is tried to avoid double-counting or wrong classifications by re-contacting firms. Special attention is given e.g. to enterprises where other current costs are much higher than labour costs (current costs might include extramural R&D expenditure) or where other current costs and extramural R&D expenditures are exactly of the same value. In most of these cases, enterprises are contacted for clarification. Furthermore, it is feasible to compare ex-post the figures for extramural R&D expenditures in BES for third parties with funding data from those sectors with macro plausibilty checks.

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	Uneven calendar years. Every two years an R&D survey is conducted.  The fiscal year is only taken into account if it is not equal to the calendar year. Then the fiscal year that ended before the end of the calendar year is asked. For instance, if a firm has a fiscal year from April to March, then the period April 2022 - March 2023 was relevant for the R&D survey on 2023.
Source of funds	Breakdown available by the different funding sectors. Nationally, more detailed sources of funds are available for funding.  Business enterprise sector (sub-classification: own funds, by other enterprises of the same group, by other enterprises, by research premium = national R&D tax incentive scheme), by government sector (sub-classification: by “Bund” (federal government), by “Laender” (regional governments), by FFG (Research promotion agency), by local governments ("Gemeinden"), by other public financing; by PNP, by higher education sector, by abroad (sub-classification: by EU, by international organizations, by foreign enterprises of the same enterprise group, other foreign enterprises, other foreign sources).
Type of R&D	Breakdown available by the three types of R&D.
Type of costs	4 types of costs are distinguished: Labour costs, other current costs, expenditures on equipment,  expenditures on buildings and lands.
Economic activity of the unit	Classification by main economic activity; information from SBS of the same calendar year is used
Economic activity of industry served (for enterprises in ISIC/NACE 72)	Not available.
Product field	Not available.
Defence R&D - method for obtaining data on R&D expenditure	Enterprises are asked to classify their R&D activities into the different NABS objectives.

3.4.2. R&D personnel

See below.

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

Coverage of years	Uneven calendar years. Every two years an R&D survey is conducted.
Function	Personnel is broken down by all three types of function. Distinction between "researchers" and "technicians" is sometimes difficult for enterprises as firms do not use those terminologies primarily adapted for academia. Detailed FM definitions of the three types of functions are provided to the respondents.
Qualification	All personnel attributed to the functional categories “researchers” and “technicians” are classified by formal qualification (in terms of the Frascati categories, in full conformity with ISCED-11). Distinction can be made between ISCED levels 8, 7, 6, 5 and 4 and below: PhD, master study, bachelor or short study, post-secondary college, master craftman's diploma, school leaving examination in a higher technical or vocational school (e.g. BHS, HTL, HAK), school leaving examination in an academic secondary school (e.g. AHS, BMS, apprenticeship), other education. For the category “other supporting staff”, no information on formal qualification is available; R&D personnel of this category is attributed to the qualification category “other qualifications" (ISCED 4 and below).
Age	Not available.
Citizenship	Not available.

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

Coverage of years	Uneven calendar years. Every two years an R&D survey is conducted.
Function	Personnel is broken down by all three types of function. Distinction between "researchers" and "technicians" is sometimes difficult for enterprises as firms do not use those terminologies primarily adapted for academia. Detailed FM definitions of the three types of functions are provided to the respondents. FTEs are reported directly by the enterprises.
Qualification	All personnel attributed to the functional categories “researchers” and “technicians” are classified by formal qualification (in terms of the Frascati categories, in full conformity with ISCED-11). Distinction can be made between ISCED levels 8, 7, 6, 5 and 4 and below: PhD, master study, bachelor or short study, post-secondary college, master craftman's diploma, school leaving examination in a higher technical or vocational school (e.g. BHS, HTL, HAK), school leaving examination in an academic secondary school (e.g. AHS, BMS, apprenticeship), other edcuation. For the category “other supporting staff”, no information on formal qualification is available; R&D personnel of this category is attributed to the qualification category “other qualifications" (ISCED 4 and below).
Age	Not available.
Citizenship	Not available.

3.4.2.3. FTE calculation

FTE are directly asked in the questionnaire. Plausibility checks are in place to avoid that the number of FTE in a category are higher than the headcounts. It is also checked if the FTE in a category are at least 10% of the headcounts. If not, the number of headcounts is reduced, e.g. 5 headcounts together must have at least 0.5 FTE.

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, if there are deviations please explain.

Responding unit and observation unit in the R&D survey in the BES is the legal unit. The statistical unit is the statistical enterprise. 

Data for statistical enterprises are compiled as follows:

Based on information from SBS 2023 legal units are combined to statistical enterprises. Out of 8,251 legal units surveyed, 3,852 have reported either intramural or extramural R&D activity and are therefore R&D-relevant. For 3,443 of those the legal unit equals the statistical enterprise respectively is the only unit within the statistical enterprise with R&D activity. The remaining 409 legal units are only part of a statistical enterprise, i.e. they form a statistical enterprise together with at least one other R&D performing legal unit. In 133 cases 2 R&D-relevant legal units are part of the same statistical enterprise. In 23 cases, 3 R&D-relevant legal units are part of the same statistical enterprise. In 7 cases, 4 R&D-relevant legal units are part of the same statistical enterprise. In 3 cases, 5 R&D-relevant legal units are part of the same statistical enterprise. In 2 cases, 6 R&D-relevant legal units are part of the same statistical enterprise. There is one case with 8 and one with 11 R&D-relevant legal units which belong to the same statistical enterprise each. Individual R&D data for the various legal units belonging to the same statistical enterprise were added up and considered additive.  

Information on the newly formed statistical enterprise was enriched with NACE, size class and regional information from SBS. This information was used to aggregate R&D data. The statistical enterprise is not used for calculating regional R&D data.  

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 in the country.
Estimation of the target population size	According to SBS 2023, 602,753 enterprises
Size cut-off point	None
Size classes covered (and if different for some industries/services)	All
NACE/ISIC classes covered	All

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	All enterprises which are registered in the business register are considered part of the frame population.
Methods and data sources used for identifying a unit as known or supposed R&D performer	Generally speaking, Statistics Austria uses all sources which are available to identify potential R&D performers. For 2023, these sources were: All legal units which reported intramural or extramural R&D expenditures in at least one of the two previous R&D surveys, 2019 or 2021 (Data ownership: Statistics Austria) Enterprises which reported no R&D in 2019 or 2021, but according to information from these previous surveys likely or potentially had R&D activities in 2021 (Data ownership: Statistics Austria) Enterprises which applied for a grant for R&D projects at the FFG ("Austrian research promotion agency" - FFG) in 2023 or 2022 (Data ownership: FFG) Enterprises which reported R&D in the CIS 2022 (Data ownership: Statistics Austria) Enterprises which appeared in newspapers, magazines, on the Internet and were mentioned as performing R&D or innovation prize winners (Data ownership: Statistics Austria) All institutes which are members or affiliated members of the Austrian Cooperative Research (ACR), the association of co-operative research institutes, which mainly carry out R&D on behalf of other enterprises (Data ownership: Publicly available) Competence centres of the COMET programme (Data ownership: Publicly available) Enterprises with 100 and more employed persons which do not fall into any of the above mentioned categories (almost) regardless of their NACE (enterprises of selected industries, such as retail trade, were not surveyed without any other information source for R&D, even if falling into the "census" size class.) (Data ownership: Statistics Austria)
Inclusion of units that primarily do not belong to the frame population	Occasionally, units are included that are not (yet) part of the frame population. This refers mostly to very small newly founded units that are not yet in the business register and therefore not part of the SBS population. The frame population additionally includes enterprises in NACE classes that are not covered by SBS (e.g. section A - agriculture and forestry)
Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D	No units are included for which there is no indication of any R&D activity at all (except units with 100 and more persons employed)
Number of “new”1) R&D enterprises that have been identified and included in the target population	2,540 legal units were "new" as they were not surveyed in 2021 (and not considered R&D-relevant in 2021)
Systematic exclusion of units from the process of updating the target population	There is no process of systematically excluding certain groups of units. All potential R&D performers are included.
Estimation of the frame population	All 602,753 enterprises of SBS 2023.

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.

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

Specific R&D statistics regulation exists: Verordnung der Bundesministerin für Bildung, Wissenschaft und Kultur, des Bundesministers für Verkehr, Innovation und Technologie und des Bundesministers für Wirtschaft und Arbeit über Statistiken betreffend Forschung und experimentelle Entwicklung (F&E-Statistik-Verordnung) vom 29. August 2003, BGBl. II Nr. 396/2003; Verordnung des Bundesministers für Wissenschaft und Forschung, des Bundesministers für Verkehr, Innovation und Technologie und des Bundesministers für Wirtschaft und Arbeit, mit der die Verordnung über Statistiken betreffend Forschung und experimentelle Entwicklung (F&E-Statistik-Verordnung) geändert wird vom 8. Mai 2008, BGBl. II Nr. 150/2008

Are respondents obliged by the national law to provide raw and administrative data:

Yes

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:

a)       Confidentiality protection required by law:

Law on Federal Statistics:

Federal Confidentiality Law

According to national law, data may only be published in a way that no conclusions on individual units can be drawn. In practice, data for aggregates (e.g. NACE classes) where less than 3 units contribute to the figure are not published. 

b)       Confidentiality commitments of survey staff:

Every individual staff member is obliged by internal rules to a strictly confidential treatment of information about individual firms.

7.2. Confidentiality - data treatment

Categories (NACE classes, size classes etc.) containing information from less than 3 enterprises cannot be disclosed (primary confidentiality). In order to prevent identification of these cells by simple subtractions from totals, at least one additional category needs to be suppressed (secondary confidentiality). Usually, categories with the lowest values are selected to be suppressed to fulfil the needs of secondary confidentiality.

8.1. Release calendar

R&D data of the BES 2023 was published nationally on 15 July 2025.

The date of the publication is announced beforehand, and the release calendar is available on the website of Statistics Austria.

8.2. Release calendar access

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

At national level this is:

Release calendar at national level (German)

Release cander (English)

8.3. Release policy - user access

Data releases are announced in the official “release calendar” on Statistics Austria’s website. Data releases can have several forms: press conferences, press releases, tables on the website, written reports or a mix of those means. Usually all users are treated equally and receive information at the same time. In exceptional cases, for highly important statistics, this rule might be suspended when the Federal Chancellery ("Prime Minister´s Office") can be informed shortly beforehand (one day before); in such cases, this is publicly announced.

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

At national level final R&D data from R&D surveys is disseminated every two years. Provisional data is disseminated yearly in the second half of April.

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	Regular release
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	Y	General publication
Specific paper publication (e.g. sectoral provided to enterprises)	Y	Specific publication

1) Y – Yes, N - No

10.3. Dissemination format - online database

Statcube:  Statcube

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	Micro-data access for research purposes is possible via the Austrian Micro-Data Center (AMDC) located at Statistics Austria. The accessing party needs to be an acknowledged research organisation and apply for access via a detailed project description. Micro-data access
Access cost policy	There is a fee to access the micro-data that is individual to each research project.
Micro-data anonymisation rules	Micro-data sets are stored with all variables, except name and address and other direct identifiers. such as enterprise ID. Researchers receive in their individual micro-data set only those variables which are required for their analyses. Before the analyses done by the researcher can be extracted from the AMDC, a check is done if confidentiality rules were respected in the aggregated results.

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	Aggregate	Main results See "Detailed results" for approximately 50 tables of results.
Data prepared for individual ad hoc requests	Y		Those type of special analyses are mostly only available for a fee covering the costs that accrue from its compilation.
Other	N

1) Y – Yes, N - No

10.6. Documentation on methodology

Statistiken Forschung

Detailed quality report under „Dokumentationen“ and „Standarddokumentation“ (in German)

Statistics research-innovation-digitalisation

Only executive summary of the quality report under „Documentation“ and „Standard documentation“ (in 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.)	Statistiken forschung-innovation-digitalisierung Detailed quality report under „Dokumentationen“ and „Standarddokumentation“ (in German) Statistics Austria Executive summary of the quality report under „Documentation“ and „Standard documentation“
Requests on further clarification, most problematic issues	There are no specific points which require regular attention vis a vis the users. It appears that the long-standing stable concept of R&D statistics is supportive of that.

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).

Statistics Austria as an organisation is committed to a series of quality guidelines which are summed up on the website:

Statistics Austria Responsibilities and Principles Standards Guidelines

The R&D survey is conducted by highly qualified staff with a high expertise in R&D statistics. The sample is drawn from the national business register. The web questionnaire contains a large number of automatic plausibility checks. Three written reminders are sent to enterprises, and extensions to deadlines are quite generously granted to respondents. A telephone hotline is available for clarifications. Enterprises are re-contacted when missing or implausible data are reported. After the data collection another round of plausibility checks is carried out.

 

11.2. Quality management - assessment

The quality of the R&D data in BES is considered very good.

Relevance: R&D statistics appears to cover user needs in a very comprehensive way. However, sometimes data requests pop up which are difficult to fulfil in the short run as a change of the national regulation is a demanding task.

Completeness: R&D statistics cover all variables requested by the national regulation and the EU regulation.

Accuracy: A compulsory survey framework with a high response rates of around 95% results in data with high accuracy.

Timeliness: Data is available 18 month after the end of the reference period. This also due to the fact that many enterprise themselves do not yet have the requested information available at the time of the R&D survey, as the national law on the R&D tax incentive (indirect R&D funding) has very extended deadlines. Those can result in a very late data delivery from the enterprises to the national statistical institute. 

Coherence and comparability: There is full comparability over the years and with SBS.

 

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	National ministries responsible for research policy: BMFWF, BMWET, BMIMI.	Detailed information on R&D activities, often sector-specific interest.
1	Eurostat
4	Researchers, policy advisors compiling studies for public institutions
1	Regional governments	Regional R&D data
1	OECD
2	Chamber of commerce (Statutory interest group for enterprises)

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	Between November 2024 and January 2025 a user satisfaction survey on all products of Statistics Austria was conducted among 327 users and experts. 4 questions on the topics "Research, Innovation, Digitalisation" were posed to 97 individuals who were identified as users of those statistics fields, with the following results: Percentage of users assessing the following dimensions with "very good" or "good": Timeliness: 80% Accuracy: 78% Comparability: 72% Quality: 74%
User satisfaction survey specific for R&D statistics	No specific user satisfaction survey for R&D statistics is undertaken.
Short description of the feedback received

12.3. Completeness

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

12.3.1. Data completeness - rate

Not applicable.

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	All compulsory data were delivered.
Obligatory data on R&D expenditure	All compulsory data were delivered.
Optional data on R&D expenditure	E.g. "Question on industry served" is not asked.
Obligatory data on R&D personnel	All compulsory data were delivered.
Optional data on R&D personnel	E.g. a distinction between internal and external personnel is not made in the survey.
Regional data on R&D expenditure and R&D personnel	All compulsory data were delivered.

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	Y-1998	annual		From 2017 onwards reimbursements from the R&D tax incentive were considered as "funding from BES" (previously "funding from GOV")	2017	Implementaion of Frascati Manual 2015
Type of R&D	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
Type of costs	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
Socioeconomic objective	Y-2015	2015, 2017, 2019, 2021, 2023
Region	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
FORD	not available
Type of institution	Y-2017	2017, 2019, 2021, 2023

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	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
Function	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
Qualification	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023		From 2013 ISCED 11 was used, which lead to a break in series	2013	Implementation of ISCED 2011
Age	Not available
Citizenship	Not available
Region	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2013
FORD	Not available
Type of institution	Y-2017	2017, 2019, 2021, 2023
Economic activity	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
Product field	Not available
Employment size class	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023

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	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
Function	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
Qualification	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023		From 2013 ISCED 11 was used, which lead to a break in series	2013	Implementation of ISCED 2011
Age	Not available
Citizenship	Not available
Region	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
FORD	Not available
Type of institution	Y-2017	2017, 2019, 2021, 2023
Economic activity	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023
Product field	Not available
Employment size class	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023

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
Extramural R&D expenditure	Y-1998	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021	domestic / abroad		12 types of institutions from which R&D was purchased

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
Function x sex x qualification available	Headcounts, FTE	1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023

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	Not applicable	:	1 (existence of R&D tax incentive could inflate R&D expenditure)	:	:	:	+
Total R&D personnel in FTE	Not applicable	:	:	:	:	:	:
Researchers in FTE	Not applicalble	:	1 (distinction between researchers and technicians can be difficult)	:	:	:	-

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. A sample survey among all R&D performing units is carried out.

13.2.1.2. Confidence interval for key variables by NACE

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

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.
R&D personnel (FTE)					Not appliable.

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:

Out of the 8,251 legal units surveyed, it turned out that 233 did not exist anymore at the time of the survey.

b)       Measures taken to reduce their effect:

Information on such units is forwarded to the business register to keep the frame up to date.

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 applicable	not applicable	not applicable	not applicable
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)	not applicable	not applicable	not applicable	not applicable
Misclassification rate	not applicable	not applicable	not applicable	not applicable	In the census survey only the final stratum (NACE, size class) of the individual enterprise is used for tabulation. Therefore any changes in the stratum have no impact on the final data or data quality.
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 applicable	not applicable	not applicable	not applicable
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)	not applicable	not applicable	not applicable	not applicable
Misclassification rate	not applicable	not applicable	not applicable	not applicable

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:

There can be a large number of potential measurement errors. The existence of an R&D tax incentive can inflate figures for R&D expenditure; the distinction between intramural and extramural R&D expenditure can be difficult; extramural R&D expenditure could be "internalised" as other current costs; funding from enterprises of the same group from abroad could be misunderstood, as funding flows within large multi-nationals is often a black box; distinction between researchers and technicians can be blurred; detailed information on qualification of R&D personnel is not always available, especially in larger firms; sometimes firms report all FTE instead of FTE for research, this applies mostly to small firms; enterprises reporting R&D although it is not, as they think this helps for their application for the R&D tax incentive; enterprises reporting no R&D, although they perform R&D, as they have not received a final confirmation from the tax authorities on their status as an applicant for the R&D tax incentive; a change in the repondent of the firm can lead to a "break in series" in the data of the enterprise.

All those measurement errors certainly do exist, but it is assumed that they only happen very rarely and that the impact on data quality is very low.

b)      Measures taken to reduce their effect:

 Enterprises are given detailed explanatory notes for reporting the data. A hotline and an e-mail address are offered for help. There is frequent contact with the enterprises via those means. A large number of follow-up contacts on unclear or implausible data are made. Respondents can receive their individual R&D data from the previous survey if requested to help compiling the data for the current R&D survey. Generous extentions are given if enterprises cannot meet the determined deadlines. A large number of plausibility checks are introduced in the field phase of the data collection. The first checks are within the web questionnaire which result in "errors" (the data cannot be sent) or "warnings" (data are potentially implausible, but can be sent), so that the respondents themselves can correct any mistakes. After data are sent it, Statistics Austria does the first round of plausibility checks which potentially result in a re-contact with the respondent. In that phase, data are also compared with data from the same enterprise in previous survey rounds. Before the final dataset is determined another round of plausibility check is carried out. Finally, a macro-check of aggregated data is carried out where results are compared to previous years. Those measures are considered sufficient to keep any measurement errors at a minimum.

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	2,424	2,349	1,850	933	7,556
Total number of units in the sample	2,893	2,501	1,904	953	8,251 (legal units)
Unit Non-response rate (un-weighted)	0.103	0.048	0.026	0.017	0.058
Unit Non-response rate (weighted)	not applicable	not applicable	not applicable	not applicable	not applicable

13.3.3.1.2. Unit non-response rates by NACE

	Industry1)	Services2)	TOTAL
Number of units with a response in the realised sample	2,834	4,722	7,556
Total number of units in the sample	3,036	5,215	8,251 (legal units)
Unit Non-response rate (un-weighted)	0.049	0.063	0.058
Unit Non-response rate (weighted)	not applicable	not applicable	not applicable

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

3 written reminders were sent out by ordinary mail, additional to the letter that announced the starting of the survey. Large R&D performers were additionally reminded by e-mail (around 50 e-mails).

13.3.3.1.4. Unit non-response survey

Conduction of a non-response survey	No
Selection of the sample of non-respondents	Not applicable.
Data collection method employed	Not applicable.
Response rate of this type of survey	Not applicable.
The main reasons of non-response identified	Non-response occurs mostly among very small units (67% of all non-responders have less than 10 persons employed). Some of them might not exist anymore; even though no R&D activity is the most likely reason for non-response.

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) (%)	0.2% (9 out of 3,651 units)	Unknown, but estimated less than 5%	Unknown, but estimated less than 5%
Imputation (Y/N)	Y	Y	Y
If imputed, describe method used, mentioning which auxiliary information or stratification is used	Nominal values from 2021 were imputed	Nominal values from 2021 were imputed for those 9 legal units with no response at all. For other units with missing FTEs, those were calculated according to headcounts and labour costs for FTEs.	Nominal values from 2021 were imputed for those 9 legal units with no response at all. For other units with missing FTEs, those were calculated according to headcounts and labour costs for FTEs.

13.3.3.3. Magnitude of errors due to non-response

	Magnitude of error (%) due to non-response
Total intramural R&D expenditure	Practically 0.
Total R&D personnel in FTE	Practically 0.
Researchers in FTE	Practically 0.

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 collection was made by a web questionnaire only; a pdf file of the questionnaire is offered on the website for information. Data reported electronically went through a phase of first plausibility checks and, if necessary, after enterprises were contacted to clarify missing or unreliable data, data are transferred automatically into a database. Subsequently, around 100 plausibility checks are carried out. A relatively low number of plausibility checks is implemented in the web questionnaire, mostly as "warnings" to the respondent that potentially implausible information was entered. Filtering is also implemented in the electronic questionnaire, but respondents can enter the same information as in the paper version. Questionnaire design (wording and order of the questions) is the same.
Estimates of data entry errors	Not applicable
Variables for which coding was performed	No coding was undertaken.
Estimates of coding errors	Not applicable.
Editing process and method	It is not possible to give editing rates. After the end of the data collection, another round of plausibility checks was carried out and necessary corrections are made.
Procedure used to correct errors	Main sources for correcting errors or adding missing values is re-contacting the enterprises, mostly by e-mail.

13.3.5. Model assumption error

Not requested.

14.1. Timeliness

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

14.1.1. Time lag - first result

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

 

a) End of reference period: 31 December 2023

b) Date of first release of national data: No preliminary data were published.

14.1.2. Time lag - final result

a) End of reference period: 31 December 2023

b) Date of first release of national data: 15 July 2025

c) Lag (days): 562 days

14.2. Punctuality

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

14.2.1. Punctuality - delivery and publication

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

14.2.1.1. Deadline and date of data transmission

	Transmission of provisional data	Transmission of final data
Legally defined deadline of data transmission (T+_ months)	10	18
Actual date of transmission of the data (T+x months)	10	18.5
Delay (days)	0	16
Reasoning for delay		Late availability of SBS data to construct the statistical unit "statistical enterprise" and receive latest infrormation for NACE and size class categorisation of R&D performing units.

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

No issues of comparability known.

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	Internal and external personnel included.
Researcher	FM2015, §5.35-5.39.	No
Approach to obtaining Headcount (HC) data	FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No	Total number of persons engaged in R&D during the (calendar) year
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	The "fixed-date"-approach is not used. All personnel in FTE is collected, regardless if the staff is still working in the enterprise at the end of the reference period
Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel	FM2015, §5.25	No	No distinction possible between internal and external personnel.
Intramural R&D expenditure	FM2015 Chapter 4 (mainly sub-chapter 4.2).	No
Special treatment for NACE 72 enterprises	FM2015, § 7.59.	No	NACE 72 enterprises" are classified in NACE 72 and not according to the "industry-served" concept (for which the necessary information is not available).
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	Statistical unit is the "statistical enterprise": Reporting unit is the legal unit, as in all previous R&D surveys. SBS information is used to determine which legal units together form a statistical enterprise. Individual R&D date of those legal units which form a statistical enterprises are added up. The statistical enterprise created that way receives the NACE classification and number of employed persons from SBS.
Target population	FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No	All legal units known or supposed to perform 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	Data sources for the information on known or supposed R&D performers are mainly previous R&D surveys, the Oesterreichische Forschungsfoerderungsgesellschaft - FFG (enterprises that have applied for public R&D funding) and own media analyses of newspapers, magazines and Internet information. Additional information sources used are described in 2.1.2. Enterprises with 100 and more employed persons are automatically considered as potential R&D performers. Also all enterprises that are members of the Austrian Cooperative Research and all COMET competence centres are considered "supposed to perform R&D".
Sector coverage	FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No	Private and public enterprises are included.
NACE coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No	NACE 01 to 99 are included.
Enterprise size coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No	Enterprises of all sizes are included, including micro-enterprises.
Reference period for the main data	Reg. 2020/1197 : Annex 1, Table 18	No	Every two years data are results of the R&D survey. Data for reference years without a survey being conducted and which are to be transmitted on a compulsory basis are estimates.
Reference period for all data	Reg. 2020/1197 : Annex 1, Table 18	No	R&D surveys are carried out every two years about uneven reference years.

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
Data collection method	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No
Cooperation with respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No	Respondents are granted extensions of the legally defined deadline to provide data, if necessary.
Follow-up of non-respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No	3 written reminders are sent out, additionally to the initial letter. Furthermore, around 50 large enterprises were reminded via e-mail.
Data processing methods	FM2015 Chapter 6 (mainly sub-chapter 6.7).	No
Treatment of non-response	FM2015 Chapter 6 (mainly sub-chapter 6.7).	No	Non-Responders were - after a careful review of their R&D activites using other sources - considered as non-R&D performers, with the exception of 9 larger firms with intramural R&D expenditures of more than 5 mn Euro in 2021. These data were imputed.
Data weighting	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No	Census. Each enterprise receives a weight of 1.
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	Final data for uneven reference years are results from R&D surveys in the BES. Final data for even reference years and all preliminary data are estimated.
Survey type	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No	Census among all known or supposed R&D performing enterprises. The survey is designed as a web questionnaire, accessible with a password on the website of Statistics Austria. The questionnaire could also be downloaded from the website as a pdf file. 99% of respondents reported data electronically.
Sample design	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No	Census survey among all known or supposed R&D performing enterprises.
Survey questionnaire	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No	8,251 enterprises were surveyed altogether. Of these, 1,703 enterprises received a short questionnaire which included all main indicators (R&D expenditures, R&D funding, R&D personnel etc.), but not all sub-categories. 6,548 enterprises have received the regular questionnaire.

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)	from 1998	2017	Reclassification of a few larger organisations from the BES to GOV, decrease of BES
Function	from 1998	2017	Reclassification of a few larger organisations from the BES to GOV, decrease of BES
Qualification	from 1998	2013, 2017	2013: ISCED 2011 is used for the first time. Increase of R&D personnel with tertiary education. 2017: Reclassification of a few larger organisations from the BES to GOV, decrease of BES
R&D personnel (FTE)	from 1998	2017	Reclassification of a few larger organisations from the BES to GOV, decrease of BES
Function	from 1998	2017	Reclassification of a few larger organisations from the BES to GOV, decrease of BES
Qualification	from 1998	2013, 2017	2013: ISCED 2011 is used for the first time. Increase of R&D personnel with tertiary education. 2017: Reclassification of a few larger organisations from the BES to GOV, decrease of BES
R&D expenditure	from 1998	2017	Reclassification of a few larger organisations from the BES to GOV, decrease of BES
Source of funds	from 1998	2017	Reclassification of a few larger organisations from the BES to GOV, decrease of BES
Type of costs	from 1998	2017	Reclassification of a few larger organisations from the BES to GOV, decrease of BES
Type of R&D	from 1998	2017	Reclassification of a few larger organisations from the BES to GOV, decrease of BES
Other		2021	Due to the implementation of the statistical enterprise as the statistical unit in 2021. data classified by NACE and size class are not comparable with the previous years.

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

Data produced on even calendar years are estimated, as a comprehensive R&D survey is only carried out every two years about uneven calendar years.

Estimate for BERD 2022: Annually in April, Statistics Austria carries out the so-called “Global Estimate of Gross Domestic Expenditure on R&D” (“Globalschaetzung der Bruttoinlandsausgaben für F&E”). Based on detailed budget analyses and further information from different available sources at this time of the year, an estimate is made for GERD by source of funds for the current calendar year and for the years before. In April 2024, there were data from the R&D survey 2021 available. For estimating the indicators for 2022 requested by the regulation, an estimate was made based on the survey results 2021 and budget analyses of 2022 (as well as for 2023 and 2024). Estimates of R&D expenditures 2022 from around 150 very large firms form the R&D survey 2021 were used as well as further economic information.

The distribution of R&D expenditures between the 4 sectors was kept stable compared to 2021. 

Taking into account the elasticity of the growth rates of total R&D expenditure and total R&D personnel in FTE from the years 2019 to 2021, an estimate was made for the growth rate of the total R&D personnel in FTE using the growth rate of the total R&D expenditure from 2019 to 2021. The distribution of the R&D personnel by sector of performance was kept stable compared to 2021. The share of researchers among the total R&D personnel was also unchanged compared to the results from the R&D survey 2021.

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

Micro-data from the R&D survey of all sectors of performance are made available to National Accounts statistics.

R&D data are used for the SNA calculation of self-produced R&D investment in the SNA sectors S11, S12 and S15. R&D data on current expenditure are used precisely for the estimation of intermediate consumption and compensation on employees as cost components of R&D investment. R&D data on capital expenditures are used to estimate depreciation with the help of a PIM method. Depreciation on the capital stock used to produce R&D is a further cost component of R&D investment. Own account R&D of the Government Sector S13 is calculated using Government Statistics by COFOG, the classification of government expenditure by function. However, Government Statistics on return uses information of R&D statistics. Concerning purchased R&D investment, R&D Data on extramural expenditure and on R&D financed by abroad is used among several other data sources like for example BoP Statistics.

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 expenditure 2023	10,157 mn €	9,423 mn €	CIS 2022 (Intramural R&D expenditure 2022)	734 mn €	Differences are due to: different reference years (2023 vs. 2022), different concepts (compulsory census survey among all potential R&D performers (R&D survey 2023) vs. voluntary sample survey (CIS 2022); although the same definition for R&D is used, a different understanding of R&D can be assumed, especially in the CIS. R&D data from the dedicated R&D survey is considered to be of considerably higher quality than CIS data. R&D data refer to the same population as the CIS: only for firms >10 employees plus and only for the core industries of the CIS. Intramural R&D expenditures from 2021 to 2023 (according to the R&D survey) increased by 17%. A difference of 8% between 2023 (R&D survey 2023) and 2022 (CIS 2022) is therefore credible.
Intramural R&D expenditure 2023	965 mn €	916 mn €	CIS 2022 (Extramural R&D expenditure 2022)	49 mn €	Differences are due to: different reference years (2023 vs. 2022), different concepts (compulsory census survey among all potential R&D performers (R&D survey 2023) vs. voluntary sample survey (CIS 2022); although the same definition for R&D is used, a different understanding of R&D can be assumed, especially in the CIS. R&D data from the dedicated R&D survey is considered to be of considerably higher quality than CIS data. R&D data refer to the same population as the CIS: only for firms >10 employees plus and only for the core industries of the CIS. Extramural R&D expenditures from 2021 to 2023 (according to the R&D survey) increased by 10%. A difference of 5% between 2023 (R&D survey 2023) and 2022 (CIS 2022) is therefore credible.

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)	10,729.518	68,209	40,582
Final data (delivered T+18)	10,618.189	65,712.7	39,119.5
Difference (of final data)	111,329 (1.0%)	2,496.3 (3.8%)	1,462.5 (3.7%)

 

Comments :

The main reason for the differences is that preliminary data are estimated as described in 15.2.3 while final data are results of the comprehensive R&D survey.

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)	84,600 €	84,600 € labour costs per FTE (5.560 bn Euro / 65,712.7 FTE). The number of FTEs used for calculation, however, also includes external R&D personnel. The share of external R&D personnel is considered very low. Number of FTEs also includes proprietors and other individuals working on R&D that do not formally get a salary. This applies mostly to small enterprises. Therefore the figure can be considered underestimated.
Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2)	Not available.

(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 separately available.	No work sub-contracted to third parties.
Data collection costs	Not separately available.	No work sub-contracted to third parties.
Other costs	Not separately available.	No work sub-contracted to third parties.
Total costs	Not separately available.	No work sub-contracted to third parties.

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 :

Costs for the entirety of activities for R&D statistics in the BES are available, but these comprise many more activities than just for the R&D survey alone. A distinction is not possible. However, no work was sub-contracted to third parties. All work was done within the National Statistical Office (Statistics Austria).

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

	Value	Computation method
Number of Respondents (R)	3,651	Legal units with intramural R&D. Enterprises with no R&D activities which have to answer once "No" are not included.
Average Time required to complete the questionnaire in hours (T)1	3 h 47 min	Legal units with intramural R&D are asked at the end of the questionnaire to report how long it took them to answer the questionnaire.
Average hourly cost (in national currency) of a respondent (C)	Not available.
Total cost	Not available.

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

R&D data is collected by a mandatory web survey among all potential R&D performers. For 2023, 8,251 legal units were included in the R&D survey. In total, 1,703 small legal units received a short version of the full questionnaire, where information not directly asked in the questionnaire was estimated ex-post. As the survey is a census of all known or supposed R&D performer, no grossing-up of the data was made. Therefore no stratification variables or classes were needed. 

The short version of the questionnaire collects intramural R&D expenditure (but only by current and capital costs), funding by sector (but not by detailed source of funds), socio-economic objectives, extramural R&D expenditure (but not by detailed categories), R&D personnel in headcounts and FTE by qualification and sex (but not by function and detailed qualification). 

NACE, size class, combination of legal units to (statistical) enterprises and classification by private or public enterprise, were taken from SBS respective the business register. All R&D-related information was collected in the survey itself.

18.1.2. Sample/census survey information

Sampling unit	Legal unit
Stratification variables (if any - for sample surveys only)	Not applicable. Census among all potential R&D performers.
Stratification variable classes	Not applicable. Census among all potential R&D performers.
Population size	8,251 potential R&D performers.
Planned sample size	8,251
Sample selection mechanism (for sample surveys only)	Not applicable
Survey frame	Business register.
Sample design	Census
Sample size	8,251
Survey frame quality	Business register. Good quality.
Variables the survey contributes to	R&D expenditure, R&D personnel and its sub-classifications

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

Source	Structural Business Statistics, business register
Description of collected data / statistics	NACE, size class (both Structural business statistics), private/public enterprise (business register)
Reference period, in relation to the variables the administrative source contributes to	2023
Variables the administrative source contributes to	Classifcation by NACE and size class

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)	7,556 legal units
Mode of data collection	Web survey. Paper questionnaire could be downloaded from web site, but respondents were urged to report electronically.  More than 99% of all responses were via web questionnaire. 3 reminders were sent out.
Incentives used for increasing response	Mandatory survey. No incentives used. Enterprises can be fined when not reporting data.
Follow-up of non-respondents	3 written reminders. Additional e-mail reminders for large firms
Replacement of non-respondents (e.g. if proxy interviewing is employed)	Otherwise, non-respondents, after a careful review, were considered as enterprises without R&D activities. Imputation of unit-non-responses only occurs when considerable R&D activities of a non-respondent is known to the data collecting agency (9 legal units in 2023).
Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility)	94.2
Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods)	Not applicable.

18.3.2. Questionnaire and other documents

Annex	Name of the file
R&D national questionnaire and explanatory notes in English:	Long questionnaire for BES 2023 (English) Explanatory notes for BES 2023 (English)
R&D national questionnaire and explanatory notes in the national language:	Long questionnaire for BES 2023 (German) Short questionnaire for BES 2023 (German) Explanatory notes for BES 2023 (German)
Other relevant documentation of national methodology in English:	National Quality Report in English (Short version)
Other relevant documentation of national methodology in the national language:	National Quality Report in German (Standarddokumentation)

18.4. Data validation

As soon as the enterprises send in their data, plausibility checks are carried out. If needed, respondents are re-contacted for clarifications. Any changes in the data are done directly in the database. Once the data collection has ended, it is checked if „large“ R&D performers (according to the previous R&D survey, with 5 mn € intramural R&D expenditures or more) are still missing. If this is the case (9 legal units in 2023), their data will be imputed. This is followed by a final round of plausibility checks which result in further corrections. Often the number of FTE is corrected (lowered).

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	%	%
10-49 employees and self-employed persons	not available	not available	%	%
50-249 employees and self-employed persons	not available	not available	%	%
250-and more employees and self-employed persons	not available	not available	%	%
TOTAL	0.2%	1.6% (percentage of R&D expenditure imputed)	not available. But assumed less than 5%, usually in very small legal units.	not available, assumed much less than 5%

18.5.1.2. Imputation rate by NACE

NACE	R&D Expenditure		R&D personnel (FTE)
	Unweighted	Weighted	Unweighted	Weighted
Industry1)	%	%	%	%
Services2)	%	%	%	%
TOTAL	0.2%	1.6%	not available. But assumed less than 5%, usually in very small legal units.	not available, assumed much less than 5%

 

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

R&D survey

Data compilation method - Preliminary data

Data is estimated as described in 15.2.3  Estimate for BERD 2023: Annually in April, Statistics Austria carries out the so-called “Global Estimate of Gross Domestic Expenditure on R&D” (“Globalschaetzung der Bruttoinlandsausgaben für F&E”). Based on detailed budget analyses and further information from different available sources at this time of the year, an estimate is made for GERD by source of funds for the current calendar year and for previous years. In April 2024, there were data from the R&D survey 2021 available. For estimating the preliminary variables for 2023 requested by the regulation, an estimate was made based on the survey results 2021 and budget analyses for 2022 and 2023. Estimates of R&D expenditures 2022 from around 150 very large firms form the R&D survey 2021 were used for 2022 as well as further economic information. The distribution of R&D expenditures between the 4 sectors was kept stable compared to 2021.  Taking into account the elasticity of the growth rates of total R&D expenditure and total R&D personnel in FTE from the years 2019 to 2021, an estimate was made for the growth rate of the total R&D personnel in FTE using the growth rate of the total R&D expenditure from 2019 to 2021. The distribution of the R&D personnel by sector of performance was kept stable compared to 2021. The share of researchers among the total R&D personnel was also unchanged compared to the results from the R&D survey 2021.

18.5.3. Measurement issues

Method of derivation of regional data	The legal units are primarily classified to the NUTS2 region of their main location according to the business register. The legal units are asked to report if they perform R&D in another NUTS2 region than their headquarter is located in. If yes, those firms must report a distribution of their R&D personnel (headcount) to the various NUTS2 regions in percentage; this distribution is also applied to allocate R&D expenditure. If, e.g., a firm reports 50% R&D personnel in region A and 50% in region B, R&D expenditures and R&D personnel are distributed to these region according to these shares by „R&D location“. This distribution „by R&D location“ is used for compiling Eurostat regional indicators. However, there are relatively few units carrying out R&D in another NUTS2 region than the one of their main location. This applies to only 7% of all legal units of the BES with intramural R&D expenditures. Nationally, classification by NUTS 2 region of the main location is additionally used and disseminated.
Coefficients used for estimation of the R&D share of more general expenditure items	Not applicable.
Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures	Respondents are explicitly requested to exclude VAT and depreciation costs.

18.5.4. Weighting and estimation methods

Weight calculation method	Census survey. No weights are used, all units receive a weight of "1".
Data source used for deriving population totals (universe description)	Business register for the universe of all firms. Register of potential R&D performers maintained at Statistics Austria
Variables used for weighting	Not applicable.
Calibration method and the software used	SAS
Estimation	Not applicable.

18.6. Adjustment

Not requested.

18.6.1. Seasonal adjustment

Not requested.

Long questionnaire for BES 2023 (German)
Short questionnaire for BES 2023 (German)
Explanatory notes for BES 2023 (German)
Long questionnaire for BES 2023 (English)
Explanatory notes BES 2023 (English)
National Quality Report in German (Standarddokumentation)
National Quality Report in English (Short version)

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).

The guiding document to preparing the quality reports is the European Statistical System (ESS)  for Quality and Metadata Reports — re-edition 2021.

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.

22 August 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, if there are deviations please explain.

Responding unit and observation unit in the R&D survey in the BES is the legal unit. The statistical unit is the statistical enterprise. 

Data for statistical enterprises are compiled as follows:

Based on information from SBS 2023 legal units are combined to statistical enterprises. Out of 8,251 legal units surveyed, 3,852 have reported either intramural or extramural R&D activity and are therefore R&D-relevant. For 3,443 of those the legal unit equals the statistical enterprise respectively is the only unit within the statistical enterprise with R&D activity. The remaining 409 legal units are only part of a statistical enterprise, i.e. they form a statistical enterprise together with at least one other R&D performing legal unit. In 133 cases 2 R&D-relevant legal units are part of the same statistical enterprise. In 23 cases, 3 R&D-relevant legal units are part of the same statistical enterprise. In 7 cases, 4 R&D-relevant legal units are part of the same statistical enterprise. In 3 cases, 5 R&D-relevant legal units are part of the same statistical enterprise. In 2 cases, 6 R&D-relevant legal units are part of the same statistical enterprise. There is one case with 8 and one with 11 R&D-relevant legal units which belong to the same statistical enterprise each. Individual R&D data for the various legal units belonging to the same statistical enterprise were added up and considered additive.  

Information on the newly formed statistical enterprise was enriched with NACE, size class and regional information from SBS. This information was used to aggregate R&D data. The statistical enterprise is not used for calculating regional R&D data.  

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.

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.

At national level final R&D data from R&D surveys is disseminated every two years. Provisional data is disseminated yearly in the second half of April.

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.