STATISTICS AUSTRIA

Directorate Social Statistics

Research and Digitalisation Unit

Guglgasse 13

1110 Wien

Austria

Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit sector should consist of all R&D performing 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 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. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.  

• The distribution of principal economic activity and by product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
• The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
• The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
• The fields of research and development based on Classification and distribution by Fields of Research and Development (FORD).

See below.

According to the Frascati Manual, 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).

See below.

See below.

Every individual involved in R&D (internal and external personnel, but only as researchers and technicians) is subject to a time-use survey, similar to the one in GOV. According to the distribution of working time between administration, R&D and other activities, FTEs for R&D are calculated for each individual.

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

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 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 PNP Sector should consist of all R&D performing 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. 

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

Not requested. See point 3.4.

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.

R&D expenditure: "in 1,000 Euro"

R&D personnel in "headcounts" and "full-time equivalents" (one decimal place)

2021

See below.

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

- European Business Statistics Methodological Manual on R&D

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, data for aggregates (e.g. fields of science) 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 confidentilal treatment of information. 

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 PNP 2021 was published nationally on 18 July 2023.

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

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.

Every two years.

Detailed results of the survey can be found here:

https://www.statistik.at/en/statistics/research-innovation-digitalisation/research-and-experimental-development-rd/rd-in-all-economic-sectors/rd-data-for-all-sectors

or

https://www.statistik.at/en/statistics/research-innovation-digitalisation/research-and-experimental-development-rd/rd-in-all-economic-sectors/rd-in-the-government-sector-and-in-the-private-non-profit-sector

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

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 

A national quality report ("Standarddokumentation") is available on the website of Statistics Austria.

In chapter "Dokumentationen", "Standarddokumentationen": 

https://www.statistik.at/statistiken/forschung-innovation-digitalisierung/forschung-und-experimentelle-entwicklung-fe/fe-in-allen-volkswirtschaftlichen-sektoren/fe-im-sektor-staat-und-im-privaten-gemeinnuetzigen-sektor

An Executive summary of the quality report is available in English, in chapter "Documentation" and "Standard documentation":

https://www.statistik.at/en/statistics/research-innovation-digitalisation/research-and-experimental-development-rd/rd-in-all-economic-sectors/rd-in-the-government-sector-and-in-the-private-non-profit-sector

Not requested.

See below.

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. Written reminders are sent to the institutions, and extensions to deadlines are granted generously to respondents. A telephone hotline is available for clarifications. Respondents are re-contacted when missing or implausible data are reported. After the data collection is completed another round of plausibility checks is carried out.

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

Due to the implementation of a compulsory survey with relatively high response rates (2021: 94.3%) 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. 

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%

Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D): 0.5%

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

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.

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.

The main indicator used to measure sampling errors is the coefficient of variation (CV). 
Definition of coefficient of variation: 
CV= (Square root of the estimate of the sampling variance) / (Estimated value)

Coefficient of variation for Total R&D expenditure : Does not apply. Census survey.

Coefficient of variation for Total R&D personnel (FTE) : Does not apply. Census survey.

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.

a)       Extent of non-sampling errors: No non-sampling errors known.

b)       Measures taken to reduce the extent of non-sampling errors:

c)       Methods used in order to correct / adjust for such errors:

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

a)       Description/assessment of coverage errors: No coverage errors known.

b)       Measures taken to reduce their effect:

Not requested.

Not requested.

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 errors known.

b)      Measures taken to reduce their effect:

Not requested.

Not requested.

Not requested.

Not requested.

Not requested.

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.

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: 2021

b) Date of first release of national data: Not released nationally, only sent to Eurostat, t+10

c) Lag (days): 303

a) End of reference period: 2021

b) Date of first release of national data: 18 July 2023

c) Lag (days): 564

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.

No restrictions on comparabilty over time or with other sectors.

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 paragraphs and the EBS Methodological Manual on R&D Statistics with recommendations about these concepts / issues.

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

See below.

See below.

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

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.

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 (not so in 2020 and 2021, due to the economic uncertainties caused by CoVid-19). For estimating the indicators for 2021 requested by the regulation, an official estimate of GERD for 2020 was available from the “Global Estimate 2021”, based on the survey results 2019 and budget analyses, estimates of R&D expenditures 2020 from around 150 very large firms as well as further economic information. Budget data for 2021, economic forecasts for 2021 and estimates of around 150 large R&D performing firms - which were also asked to provide a forecast of their R&D expenditures for 2021 in the R&D survey on 2019 - was used to estimate GERD 2021.

The distribution of R&D expenditures between the 4 sectors for 2021 was kept stable compared to 2020. 

Taking into account the elasticity of the growth rates of total R&D expenditure and total R&D personnel in FTE from the years 2017 to 2019, 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 2017 to 2019. The distribution of the R&D personnel by sector of performance was kept stable compared to 2019. The percentage share of researchers among the total R&D personnel was also unchanged compared to the results from the R&D survey 2019.

See below.

Not requested.

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.

See below.

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

(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. 

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’)

Not requested.

Not requested.

Not requested.

Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.

R&D survey even second year, about uneven years.

See below.

Response rates are checked regularly in the course of data collection in a database, so that postal and reminders by telephone can be done timely. For most units previous micro-data from the most recent survey is available and used for comparisons. If there are inconsistencies, data are checked, errors investigated and if there is no other way to correct the errors, the reporting unit is contacted. Only micro-data is edited, there is no editing on macro-data level.

At the end of data collection another round of plausibility checks is performed.

See below.

Unit non-response: <6%. For unit non-response no imputation was made. In few cases, imputations had to be made for certain variables.

Not requested.

Not requested.