Statistics Austria

Directorate Social Statistics

Research and Digitalisation Statistics Unit

Guglgasse 13

1110 Wien

 Austria

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

Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities (FM 2015, Chapter 12), which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).

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

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

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

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

See below.

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

Not requested.

Budget lines or budget items in the federal budget.

See below.

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

Not requested.

Not requested. See point 5.

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

Not requested.

a) Calendar year: 2021

b) Fiscal year: Does not apply. Federal budget refers to calendar years.

    Start month:

    End month:

See below.

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

Statistics Austria (national NSI) is obliged by the national staitistics law and the national R&D statistics regulation to collect R&D data and report them to international organisations.

There is one national R&D statistics regulation which covers all aspects of EU-relevant R&D statistics:

 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

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

-          EBS Methodological Manual on R&D Statistics

Not requested.

Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.

A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.

a)       Confidentiality protection required by law: According to national law, data may only be published in a way that no conclusions on individual units can be drawn. Practically, this does not apply to a statistics based on adminstrative data, as GBARD.

b)       Confidentiality commitments of survey staff: Every individual staff member is obliged by internal rules to a strict confidentilal treatment of information.

GBARD data are data publicly available. No confidentiality precautions need to be made.

However, in Statistics Austria receives the relevant budget data earlier than they are published and is obliged to keep information confidential before they are officially released by the Federal Ministry of Finance.

Preliminary GBARD on 2021 data were published nationally on 11 June 2021. 

Final GBARD 2021 data were published nationally on 23 January 2023.

Data are published when available, and are therefore not listed in the release calendar. This depends also when budgets are approved by parliament.

Usually preliminary data are published on the website 6 months before the end of the calendar year (t-6).

Final data are published t+18 on the website, but - as for 2021 - might be published earlier in the future.

https://www.statistik.at/medien/veroeffentlichungskalender (German)

https://www.statistik.at/en/medien/release-calendar (English)

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.

Annually.

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

Statistical database STATCUBE: https://www.statistik.at/en/databases/statcube/statcube-statistical-database/login

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

https://www.statistik.at/fileadmin/shared/QM/Standarddokumentationen/B_1/std_b_f-e-jahresrechnungen.pdf

See below.

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

https://www.statistik.at/en/about-us/responsibilities-and-principles/standards/statistics-austrias-quality-guidelines

- Close cooperation with the responsible ministries which fund R&D during the compilation of the "Detailed table Allocation of R&D-relevant funds of the federal government" ("Detailübersicht Forschungswirksame Mittelverwendungen des Bundes") of the federal budget, which is the main source for GBARD statistics. This includes the role of Statistics Austria to add missing budget items or delete budget lines without R&D content and the compilation and checking of the used coefficients. 

- A direct feedback to the Ministry of Finance from the results of the biennial R&D surveys increases coherence with the R&D survey data. 

- Regular revisions of data improve data quality as the most recent information available is used, especially to derive the R&D coefficients used.

See below.

1)       Users' class codification

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

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

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

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

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

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

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

See below.

100%

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

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

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

See below.

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

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

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

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

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

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

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

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

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

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

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

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

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

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

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

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

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

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

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

Not requested.

Not requested.

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

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

a)       Description/assessment of coverage errors: Funding from regional government is not included.

b)      Measures taken to reduce their effect:

Does not apply.

Not requested.

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

a)       Description/assessment of measurement errors: No such errors known.

b)      Measures taken to reduce their effect:

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

a) Problems in obtaining data from targeted information providers: Does not apply.

b) Measures taken to reduce their effect:

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

Not requested.

Not requested.

Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.

a)       Data processing and editing processes: Does not apply. Budget analysis.

b)      Description of errors:

c)       Measures taken to reduce their effect:

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

Description/assessment: 

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

Date of first release of national data: 

Provisional data on 2019 were published nationally on 11 June 2021

Date of first release of national data: Final data on 2021 were published nationally on 24 January 2023

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

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

See below.

Not requested.

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

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

See below.

See below.

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

GBARD includes only Federal government ("Bund") and excludes R&D financed by provincial governments and by other public sector sources. It is possible to compare GBARD with “intramural R&D expenditures funded by the federal government” of the same calendar year. For 2021 (the latest year available), this comparison looks as follows: According to the R&D survey 2021, 3.217 billion € of total GERD were funded by the federal government (2.721 by the "core" federal government, 295 mn by the FFG and 201 mn € by FWF, both federal funding institutions). GBARD for 2021 amounted to 3.270 billion €; 100 million € of these went abroad and were not spent domestically, and are therefore not part of GERD. GERD funded by the federal government 2021 (3.2173 million €) therefore amounted to approximately 101% of domestic GBARD (3.170 billion €). This high correspondence is also due to the use of coefficients for GBARD, i.e. for determining the R&D-relevant share of budget items directly from the results of the R&D survey. E.g., when the most recent R&D survey reveals the amount of federal R&D funding for a specific R&D performer, this information is proposed to the Ministry of Finance to be used to determine the coefficient in the budget line which shows the total amount of funding for this specific institution. The coefficient is set that the R&D share of total funding equals the results from the most recent R&D survey. This is mostly used for future budget appropriations until new, more updated data is available from the next R&D survey. This coefficent is especially important for determining the R&D part of overall GUF, which currently amounts to exactly 50%.

Not requested.

Not requested.

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

The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible. 

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

1)        T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)

Final data are subject to revision even after t+12, when detailed funding information for certain institutions from the two-yearly R&D surveys are taken into account to determine the final coefficients for those budget lines describing the public funding for those institutions. The coefficients used previously for determining the "R&D content" of these budget lines are then revised. The consequence can be very slight changes in total GBARD even later than 12 months after the end of the calendar year

Not requested.

Not requested.

a)       Provisional data: "Detailed table Allocation of R&D-relevant funds of the federal government" - Appropriation ("Detailübersicht Forschungswirksame Mittelverwendungen des Bundes" - Finanzierungsvoranschlag)

b)      Final data: "Detailed table Allocation of R&D-relevant funds of the federal government" ("Detailübersicht Forschungswirksame Mittelverwendungen des Bundes" - Erfolg)

c)       General University Funds (GUF): "Detailed table Allocation of R&D-relevant funds of the federal government" ("Detailübersicht Forschungswirksame Mittelverwendungen des Bundes"): The respective lines for total GUF are identified. A coefficient derived from the R&D survey, which is revised annually, is used to determine "GUF used for R&D". Currently this coefficient is exactly 50 (50% of total GUF are R&D-relevant).

See 12.3.3.

See below.

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

Same as for GBARD data: "Detailed table Allocation of R&D-relevant funds of the federal government" ("Detailübersicht Forschungswirksame Mittelverwendungen des Bundes")

The R&D-relevant share of GUF is estimated by using a coefficent derived from the most recent R&D survey.

Statistics Austria receives a provisional version of the R&D-relevant annex of the budget, namely the"Detailed table Allocation of R&D-relevant funds of the federal government" ("Detailübersicht Forschungswirksame Mittelverwendungen des Bundes").

In a first step, it is checked if there are budget items missing, if known. In a second step, coefficients are checked by Statistics Austria and potential changes in the value of the coefficient to derive the R&D content are proposed to the Minsitry of Finance for the final version. Results of the most recent R&D surveys is taken into account. 

Budget lines in a specific year are also compared with the previous years, as well as the coefficients.  

See below.

Does not apply. Budget analysis, no survey.

See below.

Not requested.

Not requested.