STATISTICS AUSTRIA

Directorate Social Statistics

Research and Digitalisation Unit

Guglgasse 13

1110 Wien

AUSTRIA

Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the business enterprise sector consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).

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

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. 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.

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

See below.

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

See below.

See below.

In the BES respondents are asked to directly report the FTEs spent on R&D, vis a vis the headcounts. In the explanatory notes it is advised to approximate 1 FTE with 1,600 hours worked. Further examples are given how FTEs can be derived from headcounts.

The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, – if there are deviations please explain.

Reporting unit in the R&D survey in the BES is still the legal unit. From 2021 onwards, the statistical unit is the statistical enterprise. 

Data for statistical enterprises are compiled as follows:

Based on information from SBS 2021 legal units are combined to statistical enterprises. Out of 8,009 legal units surveyed, 3,902 have reported either intramural or extramural R&D activity and are therefore R&D relevant. For 3,520 legal units of those the legal unit equals the statistical enterprise resp. are the only unit within the statistical enterprise with R&D activity. The remaining 382 legal units are only part of a statistical enterprise. In 129 cases 2 R&D-relevant legal units are part of the same statistical enterprise. In 21 cases, 3 R&D-relevant legal units are part of the same statistical enterprise. In 3 cases, 4 R&D-relevant legal units are part of the same statistical enterprise. In 4 cases, 5 R&D-relevant legal units are part of the same statistical enterprise. In 3 cases, 6 R&D-relevant legal units are part of the same statistical enterprise. Once, 11 R&D-relevant legal units belong to the same statistical enterprise. Individual R&D data for the various legal units belonging to the same statistical enterprise were added up and considered additive.  

Information on the newly formed statistical enterprise was enriched with NACE, size class and regional information from SBS. This information was used to aggregated R&D data. The statistical enterprise is not used for calculating regional R&D data. No consolidation for potential internal transatction was made, as this is not considered relevant for R&D.  

See below.

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

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.

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

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

Not requested. See point 3.4.

Not requested.

R&D expenditures and its derived indicators are expressed "in 1,000 Euro".

R&D personnel is expressed in headcounts (i.e. full numbers) or full-time equivalents (number with one decimal place)

The calendar year 2021 was used as the reference period.

See below.

- 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, data for aggregates (e.g. NACE classes) where less than 3 units contribute to the figure are not published.

b)       Confidentiality commitments of survey staff: Every individual staff member is obliged by internal rules to a strictly confidential treatment of information about individual firms.

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

R&D data of the BES 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.

Deailed 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-in-the-business-enterprise-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-unternehmenssektor

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-business-enterprise-sector

Not requested.

See below.

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

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 used method defining the target population (all enterprises known or supposed to perform R&D regardless of NACE or size class), the implementation of a compulsory survey with very high response rates (2021: 96%) and the intensive follow-up activities to guarantee a very high data quality, the overall quality of the R&D survey 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 3 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%

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

Criteria:

A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.

B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.

2) Y-start year

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 R&D.

1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.  

2) 'Good' = In the event that at least one out of the three criteria above described would not be fully met.

3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.

4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.

5) 'Very Poor' = If all the three criteria are not met.

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)

Does not apply as a sample survey among all R&D performing units is carried out.

1)        Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)

2)        Services sector (NACE Rev 2.: 45-47,49-53,55-56,58-63,64-66 68,69-75,77-82,84,85,86-88,90-93,94-96,97-98,99)

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 (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:

Magnitude of error (%) = (Observed Value-True Value)/ True Value (%) 

Not requested.

Does not apply. Legal units are classified into a size class and an industry after the survey. The preliminary classification at the beginning of the survey can be different, but is irrelevant as not used for data dissemination.

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

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:

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.

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

1)        Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)

2)        Services (NACE Rev 2.: 45-47,49-53,55-56,58-63,64-66 68,69-75,77-82,84,85,86-88,90-93,94-96,97-98,99)

3 written reminders were sent out by ordinary mail, additional to the letter that announced the starting of the survey. Large enterprises were additonally reminded by e-mail (around 100 e-mails).

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

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.

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 issues known.

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 and EBS Methodological Manual on R&D Statistics paragraphs 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 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.

The distribution of R&D expenditures between the 4 sectors 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 with 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.

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

The Community innovation survey 2020 (CIS2020) (inn_cis12) (europa.eu) also collects the R&D expenditure of enterprises that form the coverage of the CIS2020 survey. 

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.

Inward FATS data with respect to extramural R&D expenditure and R&D personnel are directly based on micro-data from the R&D survey. Insofar, a full coherence should be guaranteed.

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.

See 12.3.3.

See below.

Micro-data are collected via web questionnaires. Each legal unit receives an individual user name and a password which is send by ordinary mail in the initial letter at the start of the survey. After data entry by the respondent, a number of plausibility checks are imposed on the data reported before the firm can send data to Statistics Austria. A few are "hard edits", which require a correction of data by all means, otherwise the questionnaire cannot be transmitted to Statistics Austria (e.g. reporting intramural R&D activity, but failing to reporting any R&D expenditure). A larger number of checks represent "soft edits" which are warnings for potential inconsistencies. If the data are confirmed by the respondent, they can be sent.

After data are received by Statistics Austria, data are subject to a large number of plausibility checks. These include those that the respondent was already faced with, but also additional ones. If inconsistent, missing or unclear data exists the firm is usually re-contacted for clarification. Important checks comprise a comparison with previous R&D expenditures (bigger changes are further investigated), and a check between labour costs for R&D and FTEs reported, which needs to be between a certain range to be accepted. Another example is the relation between labour costs and other current costs. High current costs, vis a vis labour costs, are investigated together with the respondent, if the figure could potentially include extramural R&D expenditure. If posstible, data are confirmed or corrected after re-contacting the respondent (mostly by e-mail). 

At the end of the data collection process and after the adaptions done due to further information received from the enterprises, the entire micro-data file is subject to another round of plausibility checks. If necessary, further automatic or manual corrections are made and (very few) imputations are carried out for items still missing. Especially data collected from the "short questionnaires" are imputed.

When analysing macro-data, comparisons are made with results from previous survey years. Bigger or unexpected changes are further investigated, and basically can always be explained.  

See below.

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) / (Total number of possible records for x)*100

1)       Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)

2)       Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)

Not requested.

Not requested.