1.1. Contact organisation
Statistics Austria
1.2. Contact organisation unit
Directorate Social Statistics
Research and Digitalisation Statistics Unit
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Guglgasse 13
1110 Wien
AUSTRIA
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
2.1. Metadata last certified
8 October 2025
2.2. Metadata last posted
8 October 2025
2.3. Metadata last update
8 October 2025
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015),Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook 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 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
See below.
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.
3.3.2. Sector institutional coverage
| Tertiary education institution | All (public) universities, including: Universities of the arts, university clinics, University for Continuing Education Krems, Universities of applied sciences („Fachhochschulen“), private universities; university colleges of teacher education ("Paedagogische Hochschulen"), other institutions of the higher education sector |
|---|---|
| University and colleges: core of the sector | Included. |
| University hospitals and clinics | Included. |
| Inclusion of units that primarily do not belong to HES and the borderline cases | No such organisations known except 2 organisations which were founded and controlled by universities of applied sciences. Those are included in HES. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Corresponds to Frascati Manual. Included in the "overhead costs". |
|---|---|
| External R&D personnel | Corresponds to Frascati Manual. Included.
|
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | Corresponds to Frascati Manual. Clinical trials in phase 1, 2 and 3 are included. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | In the question on financing of R&D the following categories can be distinguished: - by EU, by international organisations, by foreign enterprises, by other foreign sources n.e.c. |
|---|---|
| Payments to rest of the world by sector - availability | For HES no information on payments to abroad is available. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Not available. |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Information concerning extramural expenditures is provided in the explanatory notes. |
| Difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
|---|---|
| Source of funds | The following sources of funds can be distinguished: by HES, by BES, by government sector (sub-classification: by “Bund” (federal government), by “Laender” (regional governments), by “Gemeinden” (local governments), by other public funding), by PNP, by abroad (sub-classification: by EU, all HES institutions but the public universities: by foreign enterprises of the same enterprise group; by international organisations, by foreign enterprises, by other foreign sources). For national purposes an even more detailed breakdown is available. Internal/external funds and transfers/grants cannot be distinguished. |
| Type of R&D | All 3 types of R&D are asked. |
| Type of costs | The four types of costs are distinguished: Labour costs; other current costs (incl. costs for external R&D personnel); instruments and equipment (incl. capitalised computer software, other intellectual property products); lands and buildings. |
| Defence R&D - method for obtaining data on R&D expenditure | Defence GERD available for reference years for all sectors of performance. A classification of units by main socio-economic objective is available. Each statistical unit is classified into one socio-economic objective according to the weighting given by them for their research projects. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons employed during the year. |
|---|---|
| Function | Distinction between researchers, technicians and other support staff. No problems encountered. |
| Qualification | All personnel attributed to the occupational 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. More detailed breakdown available. For the category “other support 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). No problems encountered. |
| Age | Available (for "researchers" and "technicians" only). No problems encountered. |
| Citizenship | Not available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Total number of persons employed during the calendar year. |
|---|---|
| Function | Distinction between researchers, technicians and other support staff. FTEs are calculated according to the information given by the respondent in the time-use survey. No problems encountered. |
| Qualification | All personnel attributed to the occupational 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. More detailed breakdown available. For the category “other support 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). No problems encountered. |
| Age | Available (for "researchers" and "technicians" only). No problems encountered. |
| Citizenship | Not available. |
Annexes:
Questionnaire public universities 2019 (German)
Explanatory notes public universities 2023 for university clinics (German)
Explanatory notes public universities 2023 for institutes (German)
3.4.2.3. FTE calculation
In HES, FTEs are calculated based on the time-use surveys carried out among the entire personnel there.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
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 HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | All potential R&D performers, i.e. all public and private universities, Universities of applied sciences etc. in the country. | Does not apply. |
| Estimation of the target population size | 1 443 units | Does not apply. |
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. 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. 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
- European Business Statistics 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:
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. FORD 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 units.
7.2. Confidentiality - data treatment
Categories (NUTS2 regions, fields of research etc.) containing information from less than 3 units cannot be disclosed (primary confidentiality). In order to prevent identification of these cells by simple subtractions from totals, at least one additional cell needs to be suppressed (secondary confidentiality).
R&D data of HES 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.
8.1. Release calendar
R&D data of HES 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
For Eurostat this is:
At national level this is:
- Veröffentlichungskalender (German).
- Release-calendar(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 Chancellary ("Prime Minister´s Office") can be informed shortly beforehand (one day before); in such cases, this is publicly announced.
The frequency of R&D data dissemination at Eurostat level 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
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Y | Globalschätzung |
| 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 | R&D in the higher education sector |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y | R&D for all economic sectors |
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 the 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. Austrian Micro Data Center |
|---|---|
| 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. 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 figures. | |
| Data prepared for individual ad hoc requests | Y | Aggregate figures. | Individual ad hoc requests are frequent, mostly not free of charge and from various user types, often from research institutes using data for policy advice. |
| Other | Y | Aggregate figures. |
1) Y – Yes, N - No
10.6. Documentation on methodology
A national quality report ("Standarddokumentation") is available on the website of Statistics Austria.
In chapter "Dokumentationen", "Standarddokumentationen":
An Executive summary of the quality report is available in English, in chapter "Documentation" and "Standard documentation":
Executive summary of the quality report
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Detailed quality report under „Dokumentationen“ and „Standarddokumentation“ (in German) Executive summary of the quality report 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's quality guidelines
The R&D survey is conducted by highly qualified staff with a high expertise in R&D statistics. The web questionnaire contains a large number of automatic plausibility checks. Up to two written reminders are sent to the institutions and extensions to deadlines are quite generously granted to respondents. A telephone hotline is available for clarifications. The institutions 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
Due to the implementation of a compulsory survey with very high response rates (2023: 100%) and the intensive follow-up activities to guarantee a very high data quality, the overall quality of the R&D data is very good. The methodological measures taken are in compliance with the Frascati manual recommendations. The high response rates are also due to up to 2 follow-up contacts with the respondents.
12.1. Relevance - User Needs
See below.
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 |
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
See below.
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. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 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 | |
| Obligatory data on R&D personnel | All compulsory data were delivered. |
| Optional data on R&D personnel | Data on citizenship is not available. |
| 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 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | From 2017 onwards reimbursements from the R&D tax incentive were considered as own funds, therefore "funding from HES" (previously only "funding from GOV"). | |||
| 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-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| Region | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| FORD | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| Type of institution | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 |
1) Y-start year, N – data not available
12.3.3.2. 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 onwards ISCED 11 was used, which lead to a break in series. | 2013 | Implementation of ISCED 2011 | |
| Age | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| Citizenship | N | |||||
| Region | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| FORD | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| Type of institution | 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 onwards ISCED 11 was used, which lead to a break in series | 2013 | Implementation of ISCED 2011 | |
| Age | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| Citizenship | N | |||||
| Region | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| FORD | Y-1998 | 1998, 2002, 2004, 2006, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023 | ||||
| Type of institution | 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 |
|---|---|---|---|---|---|
| No details given. | No details given. | No details given. | No details given. | No details given. | No details given. |
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').
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. | HC, 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. | : | : | : | : | : | |
| Total R&D personnel in FTE | Not applicable. | : | : | : | : | : | |
| Researchers in FTE | Not applicable. | : | : | : | : | : | |
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. 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 be 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
Does not apply. Census survey.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not applicable. |
| Government | Not applicable. |
| Higher education | Not applicable. |
| Private non-profit | Not applicable. |
| Rest of the world | Not applicable. |
| Total | Not applicable. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not applicable. |
| Technicians | Not applicable. | |
| Other support staff | Not applicable. | |
| Qualification | ISCED 8 | Not applicable. |
| ISCED 5-7 | Not applicable. | |
| ISCED 4 and below | Not applicable. |
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 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:
No such errors known.
b) Measures taken to reduce their effect:
Not applicable.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
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:
No such errors known.
b) Measures taken to reduce their effect:
Not applicable.
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 satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
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
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 1 443 | 1443 | 0% |
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) (%) | Not applicalble. | Not applicalble. | Not applicalble. |
| Comments | Not applicalble. | Not applicalble. | Not applicalble. |
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 | For the survey among public universities, an interactive web questionnaire is used to collect the data. Plausibility checks are carried out during the completion of the questionnaire by the respondent. Then another round of plausibility checks is done to identify implausible or missing data, which are clarified in direct contact with the units. In extremely few cases, imputations of item-non-responses must be done. For this exercise, information from the most recent R&D survey is used. Otherwise, estimates are made by experts of Statistics Austria based on the individual case. For the other sub-sectors, most of the respondents report via web questionnaire and the data is imported into a database. In case of paper questionaires the data are entered manually. Plausibility checks are carried out and respondents are contacted for necessary clarifications. |
|---|---|
| Estimates of data entry errors | No data available, but estimated to be extremely small. |
| Variables for which coding was performed | The following variables have to be coded: Unit main activity (by fields of R&D) Individual staff member function |
| Estimates of coding errors | Very few, if any. |
| Editing process and method | There are no editing rates available. |
| Procedure used to correct errors | Only if there are no other sources of information and the unit cannot provide the requested information, expert estimation will be used to complete the data (necessary only in few cases). |
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
c) Lag (days): Not applicable.
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 | A combination of different internal restraints led to the delay. |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
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 R&D personnel engaged 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 | Total number of R&D personnel engaged during the calendar year. Time-use survey for each individual engaged with R&D |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | University clinics are part of the HES, provincial hospitals are part of GOV. |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No | R&D surveys are to be 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 method | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No. | Census. Public universities are surveyed with a web questionnaire only. |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No. | Web questionnaire for all institutions (no paper questionnaire available). |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No. | Public universities: The presidents/vice-chancellors are informed about the upcoming survey and provided with all necessary information (reasons for the survey, timeliness, legal base etc.). The heads of the statistical units are informed about the survey and provided with the same information. E-mail addresses and telephone numbers of persons responsible at Statistics Austria are provided for assistance. At public universities, a contact person (university staff), continuously in contact with and trained by Statistics Austria, provides additional assistance. A detailed manual (plus a specific manual for clinics) provides definitions and explanatory notes for every single question asked. Universities additionally are offered some flexibility as regards the date of the survey start for their statistical units. Respondents are offered at least 2 extensions in case they cannot provide the data until the requested date. Other institutions: The statistical units are contacted directly. E-mail addresses and telephone numbers of persons responsible at Statistics Austria are provided for assistance. |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No. | Detailed information on external funding is asked in the questionnaire on the level of the statistical unit. A distinction between funds from HES into internal/external is not feasible. |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No. | The questionnaire for public universities distinguishes between expenditures funded from GUF and from other sources. |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No. | After follow-up action and contacting the units to clarify missing or unclear data, plausibility checks are carried out and missing items are imputed (few cases). |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No. | No unit non-response. Item-non-responses trigger contacts with the unit; if not successful, records of the previous survey are used or expert estimations are made by using comparable data from similar units (few cases) |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | Not applicable. | Census. |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No. | No such coefficients are used. A time-use survey is carried out: Researchers and technicians or equivalent staff have to report the time use for the whole reference period (one year) – the time use for the supporting staff is not surveyed, but calculated (individually for each unit, depending on the share of time the researchers spent on R&D) |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No. | No such coefficients are used. |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | No. | Final data on uneven calendar years are results from the R&D survey in the HES. Preliminary data and final data on even years are estimated. |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1) | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| Function | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| Qualification | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| R&D personnel (FTE) | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| Function | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| Qualification | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| R&D expenditure | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| Source of funds | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| Type of costs | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| Type of R&D | from 1998 | 2007, 2017 | 2007: Due to organsational and legal changes, pedagaogical institutes were turned into university colleges of teacher education and reclassified from GOV to HES. 2017: Reclassfication of the Austrian Academy of Sciences from HES to GOV. |
| Other |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
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 HERD 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). Further economic information was used, such as GDP trends.
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. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual (FM) regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
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. Coherence – Education statistics
No data transfer between the R&D- and the Education statistics. Due to the use of the ISCED 2011 classification for coding the qualification and the ISCED Fields of Education and Training 2013 for coding the fields of study, comparisons between R&D-statistics and Education statistics would be possible.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 3,598,181 | 23,413 | 18,288 |
| Final data (delivered T+18) | 3,539,312 | 21,270.6 | 16,731.3 |
| Difference (of final data) | 58,869 (1.7%) | 2,142.4 (10.1%) | 1,566.7 (0.3%) |
Comments:
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 78,200 | 78,200 € labor costs per FTE (1,663.859 mn Euro / 21,270,6). The number of FTEs used for calculation, however, includes also external R&D personnel. The share of external R&D personnel is considered low. |
| 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. | |
| Data collection costs | Not separately available. | |
| Other costs | Not separately available. | |
| Total costs | Not separately available. |
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:
Information available includes all work done on "R&D statistics (except the BES)" (not restricted to survey work) that comprises many more activities than carrying out surveys. Furthermore, as the majority of indicators collected in the survey are requested by the European legislation, but not all, a split between working time spent for national and/or European purposes would be impossible.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 1 443 | Number of surveyed R&D units in HES |
| Average Time required to complete the questionnaire in hours (T)1) | Not known. | |
| Average hourly cost (in national currency) of a respondent (C) | Impossible to quantify. | |
| Total cost | Not known. |
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
HES R&D data is collected by a mandatory web survey among all potential R&D performers. For 2023, 1 443 units were included in the R&D survey. 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.
18.1.2. Sample/census survey information
| Sampling unit | In (public) universities, the statistical units are "organisational units" such as institutes or clinics (or corresponding units). In the universities of applied sciences the the entire "Fachhochschule" is the statistical unit. In private universities and other organisations the institution as a whole is the statistical unit. |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable. |
| Stratification variable classes | Not applicalble. |
| Population size | 1 443 |
| Planned sample size | Census. |
| Sample selection mechanism (for sample surveys only) | Not applicable. |
| Survey frame | List of higher education institution kept at Statistics Austria. |
| Sample design | Not applicable. |
| Sample size | Not applicable. |
| Survey frame quality | Very good. All HES institutions in the country are known. |
| Variables the survey contributes to | R&D expenditures + R&D personnel and its sub-classifications |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | wage tax data |
|---|---|
| Description of collected data / statistics | wage data (pay slips issued to emloyees and pensioners) collected by the Austrian tax authorities |
| Reference period, in relation to the variables the administrative source contributes to | 2023 |
| Variables the administrative source contributes to | R&D expenditures |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Statistical units; central university administrations, wage tax statistics |
|---|---|
| Description of collected information | Units: Fields of R&D (by choosing keywords on a 6-digit level from the national fields of science classification), information concerning socio-economic objectives of R&D performed (“R&D projects”), expenditures financed from GUF (by teaching and education, R&D, administration, other), R&D expenditure (financed by various external sources of funds), current expenditures (labour costs and other), capital expenditures (without land and buildings), type of R&D, gross annual wage, costs for contributions of the employer (such as social security costs) and all associated costs. Individual staff members of the units (researchers, technicians): Qualification, extent of employment, time-use (teaching and education, R&D, administration, other), sex, gross annual wage, age. Qualification and time-use are not asked for support staff. central university administrations (of public universities): labour costs, other current costs, expenditures for machinery and equipment, expenditures for lands and buildings |
| Data collection method | For the sub-sector of (public) universities: The survey is conducted using an interactive web questionnaire. No paper questionnaire is used In fact, for some universities the above mentioned data is provided directly from the central university administrations; the data are collected by the university itself and afterwards sent to the statistical office. This depends on agreements between the NSI and the individual universities and data availabilty inside the individual universities. |
| Time-use surveys for the calculation of R&D coefficients | Individual staff members of the units (researchers and technicians only) must report: Age, sex, qualification, extent of employment, time-use (administration, teaching, R&D, other), gross annual wage (except public universities), fields of study, type of occupation (only researchers at public universities, e.g. full and associate professors, senior scientists, …). Qualification, age and time-use are not asked for supporti staff. Support staff must only report: Sex, extent of employment, gross annual wage (exception: public universities) |
| Realised sample size (per stratum) | 1 443 |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Web questionnaires (100%). |
| Incentives used for increasing response | None. Compulsory survey. Nevertheless, the rectorates of the public universities are free to choose the starting date of the survey at their own universities within a certain period of time in order to find the most convenient starting date for the respective institutes and clinics. |
| Follow-up of non-respondents | Non-respondents received up to two written reminders. Universities which have not responded until the deadline agreed were also contacted by phone, resp. the persons responsible for coordinating the survey within the university. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Does not apply. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 100% |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Does not apply. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | |
| R&D national questionnaire and explanatory notes in the national language: | Questionnaire public universities 2019 (German): FE_19_Frageliste_Universitaeten.pdf Explanatory notes public universities 2023 (German): for university clinics: FE23_Hilfetext_klinisch_FINAL.pdf for institutes: FE23_Hilfetext_nicht-klinisch_FINAL.pdf Questionnaire HES other than public universities (German): FE23_Papierfragebogen_F4_DE.pdf FE23-F4-Pers-online.ods Explanatory notes HES other than public universies (German): FE23_Erlaeuterungen_F4.pdf Austrian Classification of FORD (English): KDB_STR_OEFOS 2012_2025-10-06 13_12_26.968.pdf Keyword register of the Austrian Classification of FORD (German and English): OEFOS_2012_Alphabetikum_A_EN_20231113.pdf Austrian Classification of SEO (German): SOEZ.pdf |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
Annexes:
Questionnaire HES other than public universities (German)
Questionnaire public universities 2019 (German)
Explanatory notes public universities 2023 for university clinics (German)
Explanatory notes public universities 2023 for institutes (German)
Questionnaire Personnel HES other than public universities (German)
Explanatory notes HES other than university sub-sector (German)
Austrian Classification of FORD (english)
Keyword register Austrian Classification of FORD (German, English)
Austrian Classification of SEO (German)
18.4. Data validation
Data of previous surveys are used in validating the administrative data obtained by the universities. Noticeable developments regarding e.g. FTE, headcounts, expenditure or source of funds will be settled with the contact person.
Missing data will be completed with data of previous surveys if necessary and meaningful.
The revised data is provided to the respondents for completion and validation.
These data will be revised again using estimates, statements of accounts and other available sources of information (e.g. online research databases of the universities, data published by R&D funding organisations, etc.).
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)
18.5.2. Data compilation methods
| Data compilation method - Final data | R&D survey for uneven calendar years. For even calendar years estimation like for the preliminary data. |
|---|---|
| Data compilation method - Preliminary data | Data is estimated as described in 15.2.3 Estimate for HERD 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. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | No such coefficients are used, with the exception of the time spent on R&D yielded by the time-use survey. |
|---|---|
| Revision policy for the coefficients | Coefficients are directly deducted from the time-use survey within the framework of the R&D survey. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | In every R&D survey in HES a time-use survey is conducted (every two years). |
18.5.4. Measurement issues
| Method of derivation of regional data | Units are classified to the region of their main location. Practically all units have their R&D activities only in one region. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Central university administrations provide administrative data to Statistics Austria, which are used for calculating overhead costs. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation is excluded from R&D expenditure, VAT included. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | Funds from GUF and funds from other non-GUF government sources are collected separately in the survey. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Not applicable. Census. No grossing-up is made, each unit receives a "weight" of "1.0". |
|---|---|
| Description of the estimation method | Not applicable. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015),Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook 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.
8 October 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
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.
The frequency of R&D data dissemination at Eurostat level 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.
See below.
See below.
