1.1. Contact organisation
- Stifterverband für die Deutsche Wissenschaft: Responsible for data collection and micro data plausibility on the legal unit level. In the following, “a)” refers to "Stifterverband für die Deutsche Wissenschaft".
- Statistisches Bundesamt: Responsible for data aggegation and confidentiality as well as transition from the legal to the statistical unit. In the following, “b)” refers to "Statistisches Bundesamt".
1.2. Contact organisation unit
- SV Wissenschaftsstatistik GmbH
- Unit H24 - Research, Culture
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
- Baedekerstr. 1, 45128, Essen, Germany.
- Gustav-Stresemann-Ring 11, 65180, Wiesbaden, Germany.
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
29 October 2025
2.2. Metadata last posted
29 October 2025
2.3. Metadata last update
29 October 2025
3.1. Data description
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
Total coverage of all legal units in NACE sections A to U undertaken R&D |
|---|---|
| Hospitals and clinics | Yes |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | not applicable |
3.3.3. R&D variable coverage
| R&D administration and other support activities | yes |
|---|---|
| External R&D personnel | included in the Total |
| Clinical trials: compliance with the recommendations in FM §2.61. | yes |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | BES: Separately asked in the questionnaire |
|---|---|
| Payments to rest of the world by sector - availability | BES: Separately asked in the questionnaire |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | included |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | The difference is explained to the surveyed companies in the accompanying notes. |
| Difficulties to distinguish intramural from extramural R&D expenditure | None |
3.4. Statistical concepts and definitions
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
3.4.1. R&D expenditure
| Coverage of years | every year since the 1960s |
|---|---|
| Source of funds | asked in Survey |
| Type of R&D | asked in Survey |
| Type of costs | asked in Survey |
| Economic activity of the unit | asked in Survey |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | asked in Survey |
| Product field | asked in Survey |
| Defence R&D - method for obtaining data on R&D expenditure | only via NACE classification |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total HC/FTE at the end of calendar year or annual average, depending on availability |
|---|---|
| Function | HC and FTE asked in the questionnaire |
| Qualification | not asked |
| Age | not asked |
| Citizenship | not asked |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | see above (HC) |
|---|---|
| Function | see above (HC) |
| Qualification | see above (HC) |
| Age | see above (HC) |
| Citizenship | see above (HC) |
3.4.2.3. FTE calculation
If a company only provides either the number of employees (HC) or the full-time equivalents (FTE), the conversion is done using industry averages.
3.5. Statistical unit
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
The survey is conducted by the Stifterverband Wissenschaftsstatistik gGmbH, using the smallest legal unit as the basis for data collection. Reporting to Eurostat is carried out by the Federal Statistical Office in accordance with EU regulations.
3.6. Statistical population
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
3.6.1. National target population
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | All enterprises and cooperative research institutes performing R&D | |
| Estimation of the target population size | ||
| Size cut-off point | no | |
| Size classes covered (and if different for some industries/services) | 1-19, 20-49, 50-99, 100-249, 250-499, 500-999, 1000-1999, 2000-4999, 5000-9999, 10000- | |
| NACE/ISIC classes covered | all |
3.6.2. Frame population – Description
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
| Method used to define the frame population |
|
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer |
|
| Inclusion of units that primarily do not belong to the frame population | Yes |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | A sample of approximately 2.000 enterprises has been contacted to participate in the survey. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | 4469 |
| Systematic exclusion of units from the process of updating the target population | Units that are included in other sectors are excluded. Regular consultation with Destatis. |
| Estimation of the frame population | 32569 |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3. (data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
Calendar year 2023.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | No |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: Federal Statistics Act (BStatG).
- Confidentiality commitments of survey staff: Is ensured by an oath of office.
7.2. Confidentiality - data treatment
Confidential data/cells delivered to Eurostat with the relevant remarks.
8.1. Release calendar
No public release calendar; Data are disseminated nationally but without a predefined release calendar.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
Data availability is announced in an annual press release at the beginning of March
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data. It is the same with national publications.
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.7 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | |
| Ad-hoc-releases | N |
1) Y - Yes, N – No
Annexes:
a) Businesses continue to invest heavily in research – despite the economic downturn.
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 | |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y |
1) Y – Yes, N - No
Annexes:
a) Insights 2025: Research and development in the business sector 2023
b) The statistical unit as a new reference for R&D business statistics
10.3. Dissemination format - online database
a) Stifterverband Datennavigator
b) Genesis online
Annexes:
a) A common platform for accessing all data provided by the Stifterverband
b) A common plattform for accessing all data provided by the Statistisches Bundesamt
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | a) Access to microdata is provided by Stifterverband’s Research Data Centre (FDZ) for scientific purposes only, and is subject to prior researcher registration. The data is provided on the basis of the smallest legal entity. Data based on the European company is not offered. b) The Federal Statistical Office does not provide access to microdata. |
|---|---|
| Access cost policy | a) See the research data center's homepage. |
| Micro-data anonymisation rules | a) Identifying individual companies is not possible or would only be feasible with considerable effort |
Annexes:
a) Reseasrch data center
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 | ||
| Data prepared for individual ad hoc requests | Y | ||
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
a) biennial methods report (only in German), 2023: Coming soon.
Annexes:
a) Methodology report 2021
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Please see the sub-concept 10.7.1 in the full metadata view.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | a) A methods and quality report in German is prepared and published every year. b) A national quality report is in preparation. |
|---|---|
| Requests on further clarification, most problematic issues | a) Insights 2025: Research and development in the business sector 2023 b) The statistical unit as a new reference for R&D business statistics |
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).
11.2. Quality management - assessment
- In 2010, the R&D BES survey underwent an external evaluation conducted by the Austrian research institute Joanneum Research and Professor Dirk Czarnitzky (KU Leuven, Belgium). Eurostat received a copy of the evaluation report.
- A quality circle, composed of internal and external researchers as well as representatives of official statistics, was established as a methodological council. It has been in place for several years and convenes on an ad hoc basis when required.
- A scientific advisory board meets annually.
12.1. Relevance - User Needs
Please see the sub-concept 12.1.1 in the full metadata view.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | Federal Ministry of Research Federal Ministry of Economics Federal Chancellery Other federal and state ministries |
Total and regional intramural R&D expenditures Funding. Requests are usually made on the basis of legal entities. Requests based on the European company structure have not yet been made. |
| 2 | Business associations | Intramural R&D expenditures by industry sector |
| 3 | Total and regional intramural R&D expenditures Funding |
|
| 4 | Researchers from universities and research institutions | Researchers have full access to microdata through the research data center; specific needs will depend on the individual research project. |
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.
Please note: On the national level there are no users needs for R&D data in terms of the statistical unit.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction |
|
|---|---|
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received |
|
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
All mandatory data transmitted.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | No missing cells |
| Obligatory data on R&D expenditure | No missing cells |
| Optional data on R&D expenditure | Lack of resources at the Stifterverband |
| Obligatory data on R&D personnel | No missing cells |
| Optional data on R&D personnel | Lack of resources at the Stifterverband |
| Regional data on R&D expenditure and R&D personnel | No missing cells |
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 | biennal | ||||
| Type of R&D | Y | biennal | ||||
| Type of costs | Y | biennal | ||||
| Socioeconomic objective | N | |||||
| Region | Y | biennal | ||||
| FORD | Y | biennal | ||||
| Type of institution | N |
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 | biennal | ||||
| Function | Y | biennal | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y | biennal | ||||
| FORD | Y | biennal | ||||
| Type of institution | N | |||||
| Economic activity | Y | biennal | ||||
| Product field | Y | biennal | ||||
| Employment size class | Y | biennal |
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 | biennal | ||||
| Function | Y | biennal | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y | biennal | ||||
| FORD | Y | biennal | ||||
| Type of institution | N | |||||
| Economic activity | Y | biennal | ||||
| Product field | Y | biennal | ||||
| Employment size class | Y | biennal |
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 |
|---|---|---|---|---|---|
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not available | ||
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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- 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 | - | 3 | 2 | 4 | 1 | - | + |
| Total R&D personnel in FTE | - | 3 | 2 | 4 | 1 | - | + |
| Researchers in FTE | - | 3 | 2 | 4 | 1 | - | + |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
No sample, therefore no variance estimate.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | not relevant | not relevant | not relevant |
| R&D personnel (FTE) | not relevant | not relevant | not relevant |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | not relevant | not relevant | not relevant | not relevant | not relevant |
| R&D personnel (FTE) | not relevant | not relevant | not relevant | not relevant | not relevant |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors: To ensure frame quality and to prevent coverage and sampling errors, companies that are expected to fall outside the target sample are contacted to verify their classification.
- Measures taken to reduce their effect: To ensure frame quality and to prevent coverage and sampling errors, companies that are expected to fall outside the target sample are contacted to verify their classification.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Frame misclassification rate
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
| By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
|---|---|---|---|---|---|
| Number or surveyed enterprises in the stratum (according to frame) | 1434 | 4473 | 3842 | 2293 | 12042 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 79 | 160 | 184 | 79 | 502 |
| Misclassification rate | 5,5 | 3,6 | 4,8 | 3,4 | 4,4 |
| By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
| Number or surveyed enterprises in the stratum (according to frame) | 2856 | 3982 | 1655 | 638 | 9131 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 72 | 175 | 113 | 40 | 400 |
| Misclassification rate | 2,5 | 4,4 | 6,8 | 6,3 | 4,4 |
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.
- Description/assessment of measurement errors: Some measuremet errors may occur.
- Measures taken to reduce their effect: Explanatory notes are provided together with the questionnaire to clarify the meaning of individual variables.
A dedicated telephone hotline and email contact are available for respondent support.
In addition, respondents are contacted directly in case of implausible or inconsistent answers to verify the reported data.....
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response, which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response, which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate.
Definition: Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
- Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
- Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100
13.3.3.1.1. Unit non-response rates by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| Number of units with a response in the realised sample | 2280 | 3853 | 2640 | 1278 | 10376 (including 325 with unknown size class without R&D activity) |
| Total number of units in the sample | 5046 | 9443 | 6161 | 3119 | 31357 (including 7588 with unknown size class without R&D activity) |
| Unit Non-response rate (un-weighted) | 55 | 59 | 57 | 59 | 67 |
| Unit Non-response rate (weighted) | not available | not available | not available | not available | not available |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 5688 | 4260 | 10376 (including 428 units with unknown nace class without R&D activity) |
| Total number of units in the sample | 13418 | 10249 | (including 7690 units with unknown nace class without R&D activity) |
| Unit Non-response rate (un-weighted) | 58 | 58 | 67 |
| Unit Non-response rate (weighted) | not available | not available | not available |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.3.3.1.3. Recalls/Reminders description
All enterprises received two reminder letters. Large enterprises were additionally followed up by telephone.
Furthermore, all enterprises for which an email address was available were contacted in two additional email waves.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | Yes |
|---|---|
| Selection of the sample of non-respondents | All non-responding enterprises with known email addresses that were not individually processed and that were expected to have R&D activities (medium priority class) were contacted. |
| Data collection method employed | Very short online survey, which was sent by mail. |
| Response rate of this type of survey | 14% |
| The main reasons of non-response identified | Answering the questions is too time-consuming. |
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) (%) | 25% |
26% |
59% |
| Imputation (Y/N) | Y |
Y |
Y |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used |
|
|
|
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | not available |
| Total R&D personnel in FTE | not available |
| Researchers in FTE | not available |
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | data keying, responses through electronic online questionnaires and paper/PDF questionnaire |
|---|---|
| Estimates of data entry errors | due to various plausibility checks all entry errors are corrected |
| Variables for which coding was performed | None. |
| Estimates of coding errors | None. |
| Editing process and method | Depending on the priority class, firms are processed manually, i.e. annual reports are analysed or direct telephone contact is made with the information provider to clarify any errors, and information from previous years is used to identify possible misspecification. Firms in the low priority class (i.e. where there is a high degree of uncertainty about whether they carry out R&D at all) are checked manually, but item non-response is filled in by imputation procedures (estimates by size and economic activity class; average rates). |
| Procedure used to correct errors | Re-contact, imputation, external information |
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: 07 Mach 2025
c) Lag (days): 430
14.1.2. Time lag - final result
a) End of reference period: 31 December 2023
b) Date of first release of national data: 01 September 2025
c) Lag (days): 610
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 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No problems regarding regional comparability.
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 deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| 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 deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No deviation
|
|
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | All companies that are known to conduct research, or that are presumed to do so. No sample selection. |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | Main sources: Funding data from the Ministry of Research and the Ministry of Economic Affairs, as well as publications by the companies. | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation |
|
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No weighting will take place. | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No variance estimation because there are no samples. | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | Complete survey of all research-oriented companies | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No sample | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). |
15.2. Comparability - over time
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | 32 years, 0 years |
1991, 2023 |
German reunification, change to the statistical unit |
| Function | 32 years, 0 years |
1991, 2023 |
German reunification, change to the statistical unit |
| Qualification | |||
| R&D personnel (FTE) | 32 years, 0 years |
1991, 2023 |
German reunification, change to the statistical unit |
| Function | 32 years, 0 years |
1991, 2023 |
German reunification, change to the statistical unit |
| Qualification | |||
| R&D expenditure | 32 years, 0 years |
1991, 2023 |
German reunification, change to the statistical unit |
| Source of funds | 32 years, 0 years |
1991, 2023 |
German reunification, change to the statistical unit |
| Type of costs | 32 years, 0 years |
1991, 2023 |
German reunification, change to the statistical unit |
| Type of R&D | 32 years, 0 years |
1991, 2023 |
German reunification, change to the statistical unit |
| 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
The data for odd-numbered years is collected through a complete census. In addition to the already known research-performing companies, companies that are potentially engaged in R&D activities are identified. The primary source of information for this is the funding data of the federal government. In even-numbered years, a sample is drawn from the known research-performing companies (small and medium-sized enterprises). Large companies are also included in the survey using a complete census approach. However, no new companies are added to the sample in these years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
The Stifterverband provides R&D data to the Federal Statistical Office for the national accounts. The Federal Statistical Office is responsible for adapting the data to the concepts used in the national accounts.
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
|---|---|---|---|---|---|
| Intramural R&D expenditure (just C Manufacturing industry) | 71,8 bn Euro | 92,8 bn Euro | Innovations Survey | 29% | Alternative statistical method |
15.4. Coherence - internal
Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 88706509 |
533260 |
307763 |
| Final data (delivered T+18) | 90407703 |
543452 |
312852 |
| Difference (of final data) | +1701194 |
+10192 |
+5089 |
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) | on average 74 thousand Euro per FTE |
internal and external personnel cannot be differentiated |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available |
unkown |
(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 available separately | |
| Data collection costs | Not available separately | |
| Other costs | Not available separately | |
| Total costs | Not available separately |
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 :
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 10522 | |
| Average Time required to complete the questionnaire in hours (T)1 | unknown | |
| Average hourly cost (in national currency) of a respondent (C) | unknown | |
| Total cost | unknown |
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
The data is collected using questionnaires. The survey claims to be a complete census; however, small samples are also used.
For estimations, data from company reports or commercial data providers is also used.
18.1.2. Sample/census survey information
| Sampling unit | No sampling |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | Not applicable |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | Not applicable |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Not applicable |
| Variables the survey contributes to | Not applicable |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | No administrative data |
|---|---|
| Description of collected data / statistics | No administrative data |
| Reference period, in relation to the variables the administrative source contributes to | No administrative data |
| Variables the administrative source contributes to | No administrative data |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
| Realised sample size (per stratum) | Census (no sample) |
|---|---|
| Mode of data collection | Census (no sample) |
| Incentives used for increasing response | Census (no sample) |
| Follow-up of non-respondents | |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 33,1% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) |
|
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | Fragebogen_FuE-Erhebung2023.pdf FuE-Erhebung203_Erklärungen.pdf |
| Other relevant documentation of national methodology in the national language: |
Annexes:
Questionnaire 2023
Explanation of the questionnaire 2023
18.4. Data validation
Much of the information obtained from annual reports and other documents of very large companies relates to global research and development (R&D) activities. The rates of change in these figures are then compared with the changes reported by the companies themselves and checked for plausibility.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)
18.5.1.1. Imputation rate by Size class
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | 72,2% | % | 72,3% | % |
| 10-49 employees and self-employed persons | 74,0% | % | 74,3% | % |
| 50-249 employees and self-employed persons | 73,9% | % | 74,1% | % |
| 250-and more employees and self-employed persons | 73,4% | % | 73,6% | % |
| TOTAL | 73,5% | % | 73,7% | % |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | 72,6% | % | 72,8% | % |
| Services2) | 74,7% | % | 75,0% | % |
| TOTAL | 73,5% | % | 73,7% | % |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
| Data compilation method - Final data | Census |
|---|---|
| Data compilation method - Preliminary data | Census |
18.5.3. Measurement issues
| Method of derivation of regional data | Census (no sample) |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT excluded |
18.5.4. Weighting and estimation methods
| Weight calculation method | No weighting |
|---|---|
| Data source used for deriving population totals (universe description) | Census |
| Variables used for weighting | Census |
| Calibration method and the software used | Census |
| Estimation | Non probabilistic methods: secondary data, previous year, industry averages |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
29 October 2025
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
The survey is conducted by the Stifterverband Wissenschaftsstatistik gGmbH, using the smallest legal unit as the basis for data collection. Reporting to Eurostat is carried out by the Federal Statistical Office in accordance with EU regulations.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
Not requested. R&D statistics cover national and regional data.
Calendar year 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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data. It is the same with national publications.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
