1.1. Contact organisation
Statistisches Bundesamt
1.2. Contact organisation unit
Unit H24 - Research, Culture
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Martin Szibalski
Gustav-Stresemann-Ring 11
D-65180 Wiesbaden
Germany
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
31 October 2023
2.2. Metadata last posted
31 October 2023
2.3. Metadata last update
31 October 2023
3.1. Data description
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government 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.
Statistics on science, technology and innovation were collected based on the Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.
3.2. Classification system
- 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 are based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
| no | |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Research and experimental development (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. |
| Fields of Research and Development (FORD) | "natural sciences", "engineering and technology", "medical sciences", "agricultural sciences", "social sciences" and "humanities" data are separately available. "Sports science" is included in "humanities" and "pharmacy" is included in "natural sciences". Until 2014: Humanities generally include educational sciences, linguistics, psychology. Since 2014: Engineering and technology generally include computer information science. |
| Socioeconomic objective (SEO) | For the Government sector, every forth year a detailed breakdown ist asked |
3.3.2. Sector institutional coverage
| Government sector | Research institutes of federal, Länder (federal states) and local governments, the national research centres, the Max-Planck and the Fraunhofer societies, institutions of the Leibniz association, scientific museums and libraries, private non profit organisations working in science, research and development as long as they receive more than EUR 160,000 from the government in the reporting year. The coverage is not fully in accordance with the national accounts, we question some institutions which are classified in BES by the national accounts, science they are mainly funded by government. We refer to FM Chapter 3.20, thus, funding may also be a factor for classification. |
| Hospitals and clinics | University hospitals are included in the HE sector. |
| Inclusion of units that primary don`t belong to GOV | The target population are research institutes of federal, Länder (federal states) and local governments, the national research centres, the Max-Planck and the Fraunhofer societies, institutions of the Leibniz association, scientific museums and libraries, private non profit organisations working in science, research and development as long as they receive more than EUR 160,000 from the government in the reporting year. Some institutes carrying out R&D to a minor degree could be missing because R&D not their main task. So the target population includes a) government units (federal, regional, state level) b) Non-market non-profit institutions controlled by goverment c) The PNP-sector d) Some institutions which are allocated to BES or PNP according SNA, but which are are mainly financed and controlled by government (Max-Planck, Fraunhofer, Leibniz Association) |
3.3.3. R&D variable coverage
| not includedR&D administration and other support activities | Persons working in R&D organisation (administration, etc.) are included. |
| External R&D personnel | not included |
| Clinical trials |
3.3.4. International R&D transactions
| Receipts from Rest of the world by sector - availability | yes |
| Payments to Rest of the world by sector - availability | no |
| R&D expenditure of foreign affiliates - coverage |
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) 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 | in questionnaire forwarded assignments and grants are shown separately |
| Difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
| Source of funds | For the Government sector, every fourth year a detailed breakdown is asked and applied together with a rough breakdown which is reported every year in the meantime. |
| Type of R&D | Since 2006 every fourth year a detailed breakdown is asked in the survey. No breakdown available from 1994 to 2006. Up to 1993 basic research was separately reported. Applied research and experimental development were aggregated. Basic research data were provided from the surveys of the Federal Ministry for Education, Science, Research and Technology. Expenditure of the Max-Planck Institutes was totally credited to basic research |
| Type of costs | A detailed breakdown is asked annually |
| Defence R&D - method for obtaining data on R&D expenditure |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | fixed date |
| Function | Researcher = persons with university qualification (ISCED 5A, 6) (including universities of applied sciences); Technicans = persons with other tertiary education (ISCED 5B) and holders of diplomas of secondary education (except workers); |
| Qualification | ISCED 8, ISCED 7, ISCED 6, ISCED 5 are asked separately, Qualification ISCED 4 and below is reported together |
| Age | year of birth |
| Citizenship | yes |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Fixed date |
| Function | Researcher = persons with university qualification (ISCED 5A, 6) (including universities of applied sciences); Technicans = persons with other tertiary education (ISCED 5B) and holders of diplomas of secondary education (except workers); |
| Qualification | ISCED 8, ISCED 7, ISCED 6, ISCED 5 are asked separately, Qualification ISCED 4 and below is reported together |
| Age | year of birth |
| Citizenship | yes |
3.4.2.3. FTE calculation
FTE is calculated by taking 100% of personnel working full-time in R&D organisations and 50% of personnel working part-time in R&D organisations.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| not available | ||
3.5. Statistical unit
The statistical units are those included the in the National Accounts plus PNP sector and some profit institutions which are financed by the government for the most part
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
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 Government 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.
| 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 | The target population are research institutes of federal, Länder (federal states) and local governments, the national research centres, the Max-Planck and the Fraunhofer societies, institutions of the Leibniz association, scientific museums and libraries, private non profit organisations working in science, research and development as long as they receive more than EUR 160,000 from the government in the reporting year. Some institutes carrying out R&D to a minor degree could be missing because R&D not their main task. So the target population includes a) government units (federal, regional, state level) b) Non-market non-profit institutions controlled by goverment c) The PNP-sector d) Some institutions which are allocated to BES or PNP according SNA, but which are are mainly financed and controlled by government (Max-Planck, Fraunhofer, Leibniz Association) |
|
| Estimation of the target population size | Approx. 1000 |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | The frame population covers the PNP-sector and some profit institutions if they are financed by the government for the most part. |
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Information on the R&D activities is taken from the databases on public funding and beneficiaries from the research ministries of federal and Länder level. Some institutes are checked by online research about their activities. |
| Inclusion of units that primary don`t belong to the frame population | Since 1991, the Government sector includes private non-profit institutes, which are not permanently financed by business enterprises, as far as data are available from the annual survey in the government |
| Systematic exclusion of units from the process of updating the target population | |
| Estimation of the frame population | Information not available, because there is no official register. We assume, that we cover the target population almost fully with our method to define the target population. |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested.
3.9. Base period
Not requested.
expenditures: Euro ( thousand)
personnel: HC
expenditures: a calendar year
personnel: point in time (30. June)
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
| Legal acts / agreements |
Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021. |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | yes |
| Legal acts | Gesetz über die Statistiken der öffentlichen Finanzen und des Personals im öffentlichen Dienst in der Fassung der Bekanntmachung vom 22. Februar 2006 (BGBl. I S. 438), zuletzt durch Artikel 1 des Gesetzes vom 3. Juni 2021 (BGBl. I S. 1401) geändert |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | yes |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | yes |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | yes |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | no |
| Planned changes of legislation | no |
6.1.3. Standards and manuals
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
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: Federal Statistics Act (BStatG)
b) Confidentiality commitments of survey staff: Is ensured by oath of office
7.2. Confidentiality - data treatment
Confidential data/cells are delivered to Eurostat with the relevant remark.
8.1. Release calendar
Date of final release of provisional national data: T+10 month
Date of final release of final national data: T+14 month
8.2. Release calendar access
no official release calendar available
8.3. Release policy - user access
Publications/data releases are usually accompanied by a press release (accessible to the public).
yearly
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Y | Regular press release in first quarter every year. Link for press release: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Forschung-Entwicklung/_inhalt.html |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Mean of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | Printed annual series on request (online publication). |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Destatis database "GENESIS Online": https://www-genesis.destatis.de/genesis/online
Database of the Ministry for education and research: "Datenportal BMBF" - Link: http://www.datenportal.bmbf.de/portal/de/K1.html
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | GOV/PNP/HES: no microdata access |
| Access cost policy | No costs |
| Micro-data anonymisation rules | No dissemination of micro data |
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 resultsavailable on the nationalstatistical authority’s website |
Y | Some key breakdowns available, detailed online publication available for download | |
| Data prepared forindividual ad hoc requests | Y | with rules of confidentiality for detailed breakdowns | |
| Other | Y | Data prepared for regular publications of other authorities, for example state statistical offices and federal and state ministries for education and research |
1) Y – Yes, N - No
10.6. Documentation on methodology
National quality report: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Forschung-Entwicklung/_inhalt.html#sprg410790
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Quality Report: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Forschung-Entwicklung/_inhalt.html#sprg410790 |
| Request on further clarification, most problematic issues | ain feedback of users consists in asking for additional breakdowns or combination of variables. As far as possible the requested data are produced. |
| Measure to increase clarity | no |
| Impression of users on the clarity of the accompanying information to the data | good |
11.1. Quality assurance
Broad Quality Management within the Statistical Offices of Germany and the federal states (Länder.)
Rules are described for example in: "Qualitätshandbuch der Statistischen Ämter des Bundes und der Länder": https://stanet-web.stba.testa-de.net/DE/Statistikuebergreifend/Qualitaetsmanagement/Qualitaetshandbuch.pdf
Within the Statistical Office: Quality Reports for each statistic, for example R&D: https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Forschung-Entwicklung/_inhalt.html#sprg410790
Regular review of the implementation of quality guidelines (Qualitätsrichtlinien (QRL))
11.2. Quality management - assessment
The overall assessment of the GOV R&D methodology is good especially because of the mandatory character. Some weakness appears while not asking for R&D expenditure and personnel but instead working with R&D coefficients.
Sometimes the identification of a research institute causes some problems as there is no register which can be used.
Because of the mandatory character there is a very high response rate. The respondents receive several reminders within the survey period.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1 | Eurostat; European Commission | Data tabulation and publication, building EU aggregates; research policy assessment |
| 1 | Ministries of Education and Research | Research policy making and assessment, analysis and publications |
| 1 | OECD, UNESCO | Data tabulation, analysis and publication |
| 4 | Mainly economists | Analysis, policy assessment |
| 3 | Specialized media and media for the general public | Reporting on research and policy issues |
| 5 | Consulting agencies that do marketing for special locations for businesses | Market analysis |
| 6 | - | - |
1) Users' class codification
1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, Other Ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes.)
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | No user satisfaction survey has been conducted. |
| User satisfaction survey specific for R&D statistics | not applicable |
| Short description of the feedback received | Possibility to comment on questionnaire. Some feedback we receive concerns the confidentiality when many detailed breakdowns are requested. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
As it is a mandatory data collection the completeness is nearly 100%
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 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 5 |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons formissing cells | |
|---|---|---|---|---|---|---|
| Preliminary variables | x | |||||
| Obligatory data on R&D expenditure | x | |||||
| Optional data on R&D expenditure | x | no legal basis for collecting special indicators | ||||
| Obligatory data on R&D personnel | x | |||||
| Optional data on R&D personnel | x | no legal basis for collecting special indicators | ||||
| Regional data on R&D expenditure and R&D personnel | x |
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%.
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Y-1981 | Geographical coverage | 1991 | From 1991, the data are for unified Germany. Until 1990, data in the STI/EAS databases cover West Germany only | ||
| Type of R&D | Y-1981 | Every fourth year | Geographical coverage | 1991 | From 1991, the data are for unified Germany. Until 1990, data in the STI/EAS databases cover West Germany only | |
| Type of costs | Y-1981 | Geographical coverage | 1991 | From 1991, the data are for unified Germany. Until 1990, data in the STI/EAS databases cover West Germany only | ||
| Socioeconomic objective | Y-1996 | Every fourth year | ||||
| Region | Y-1981 | Geographical coverage | 1991 | From 1991, the data are for unified Germany. Until 1990, data in the STI/EAS databases cover West Germany only | ||
| FORD | Y-1993 | Geographical coverage Changes in national classification |
1991 2005 |
From 1991, the data are for unified Germany. Until 1990, data in the STI/EAS databases cover West Germany only. Adaption to Higher Education sector |
||
| Type of institution | Y-1981 | Geographical coverage | 1991 | From 1991, the data are for unified Germany. Until 1990, data in the STI/EAS databases cover West Germany only |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y-1995 | |||||
| Function | Y-1995 | Detailed categories of personnel are asked and allocated to the occupations, except personnel of federal institutions and institutions of the Länder | 2014 | Change of national statistical legislation | ||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-1995 | |||||
| FORD | Y-1995 | Changes in national classification | 2015 | Adaption to Higher Education sector | ||
| Type of institution | N |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y-1995 | |||||
| Function | Y-1995 | Detailled categories of personnel are asked and allocated to the occupations, except personnel of federal institutions and institutions of the Länder. | 2014 | |||
| Qualification | Y-1995 | |||||
| Age | Y-1995 | |||||
| Citizenship | Y-1995 | |||||
| Region | Y-1995 | |||||
| FORD | Y-1995 | changes in national classification | 2015 | Adaption to Higher Education sector | ||
| Type of institution | Y-1995 |
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 |
| Researchers/university graduates - Sex | Y-1995 | ||||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 995/2012 (neither as 'optional').
2) Y-start year
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors | 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 | - | 2 | 3 | 1 | 4 | - | +/- |
| Total R&D personnel in FTE | - | 2 | 3 | 1 | 4 | - | +/- |
| Researchers in FTE | - | 2 | 3 | 1 | 4 | - | +/- |
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.
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 | 5 | ||||
| Total R&D personnel in FTE | 5 | ||||
| Researchers in FTE | 5 |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = 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.
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
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)
13.2.1.1. Variance Estimation Method
no sample
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | n.a |
| Government | n.a |
| Higher education | n.a |
| Private non-profit | n.a |
| Rest of the world | n.a |
| Total | n.a |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Occupation | Researchers | n.a |
| Technicians | n.a | |
| Other support staff | n.a | |
| Qualification | ISCED 8 | n.a |
| ISCED 5-7 | n.a | |
| ISCED 4 and below | n.a |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors :
Since there is no official register, there might be signle institutions which are no included.
Data of government-funded institutions, which receive less than 160.000 Euros in the reported year, is not questioned and therefore not included.
The omitted amount can not be numbered but is expected to be small.
b) Measures taken to reduce their effect:
Yearly assessment.
Information on the R&D activities is taken from the databases on public funding and beneficiaries from the research ministries of federal and Länder level.
Some institutes are checked by online research about their activities.
c) Share of PNP (if PNP is included in GOV): approx. 15%
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors:
Measurement errors can occur while using R&D coefficients and by assuming the regional allocation of R&D personnel also for R&D expenditure. The measurement error caused by institutional coverage is small.
b) Measures taken to reduce their effect:
The R&D coefficients are checked with information from other sources (ministries, publications etc.). The coverage is permanently improved by using different sources of information about new or existing research institutes. The questionnaire and the validity checks are improved continuously.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
-Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
-Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
| 1038 | 1054 | 0.0152 |
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 variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| No information available | No information available | No information available |
13.3.3.3. Measures to increase response rate
Mandatory survey: when no response -> regulatory offence procedure; reminders
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 | Automatic checks for plausibility and consistency during data collection. In addition: Possible errors are checked by comparing the results with previous answers and by calling back the respondents. |
| Estimates of data entry errors | Data from institutions are rejected if not correct. Contact and questions to the institution until correct data is provided. |
| Variables for which coding was performed | See above. |
| Estimates of coding errors | See above. |
| Editing process and method | See above. |
| Procedure used to correct errors | Re-contact with information provider or imputation on the basis of data of the previous year. There is also the possibility to impute with data of annual reports. This is done very rarely. |
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: 31.12.2021
b) Date of first release of national data: submission to Eurostat: 13.10.2022
c) Lag (days): 286
14.1.2. Time lag - final result
a) End of reference period: 31.12.2021
b) Date of first release of national data: 08.03.2022
c) Lag (days): 432
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
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
As from 1993, R&D expenditure in the government sector, and consequently total GERD, includes R&D performed in German research institutions located abroad. This amounts to between 0.6 and 0.7% of R&D expenditure in the government sector and less than 0.1% of GERD.
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 995/2012 or Frascati manual 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 paragraph 5.2). | YES | Applying R&D coefficients to derive R&D personnel only internal personnel |
| Researcher | FM2015, § 5.35-5.39. | NO | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | YES | Only data for internal personnel available. |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | NO | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Target population | FM2015, § 8.63 (in combination with the Eurostat's harmonised Methodological Guidelines). | YES | Some institutes carrying out R&D to a minor degree are missing because R&D not their main task. |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with the Eurostat's harmonised Methodological Guidelines). | YES | Some institutes carrying out R&D to a minor degree are missing because R&D is not their main task. Some institutions mainly funded by government are classified as GOV, not by sectors of the national accounts. This is in line with the FM2015 (§ 3.20, 3.37, 8.14, 8.15) |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | NO | |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Fields of research & development coverage and breakdown | Reg. 995/2012: Annex 1, section 1, § 7.3. | YES | Engineering and technology generally includes computer and information science. Sports science is included in humanities, pharmacy is included in natural sciences. |
| Socioeconomic objectives coverage and breakdown | Reg. 995/2012: Annex 1, section 1, § 7.8. | YES | Data available every fourth year. |
| Reference period | Reg. 995/2012: Annex 1, section 1, § 4-6. | NO |
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, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | NO | Annual census survey |
| Survey questionnaire / data collection form | NO | |
| Cooperation with respondents | NO | |
| Data processing methods | NO | |
| Treatment of non-response | NO | |
| Variance estimation | NOT APPLICABLE | |
| Data compilation of final and preliminary data | NO |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See 12.3.3. and 15.2.2
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
| R&D personnel (HC) | 1992, 1991, 1985, 1983, 1981 | 1992:-the restructuring that took place after unification created over 100 non-university research units in the government sector; -the methodology of the surveys on resources devoted to R&D in the government sector was altered. |
|
| Function | 2014 | Since 2014 detailed categories of personnel are asked and allocated to the occupations, except personnel of federal institutions and institutions of the Länder | |
| Qualification | |||
| R&D personnel (FTE) | 1992, 1991, 1985, 1983, 1981 | 1992:-the restructuring that took place after unification created over 100 non-university research units in the government sector; -the methodology of the surveys on resources devoted to R&D in the government sector was altered. |
|
| Function | 2014 | Since 2014 detailed categories of personnel are asked and allocated to the occupations, except personnel of federal institutions and institutions of the Länder | |
| Qualification | |||
| R&D expenditure | 1992, 1991, 1985, 1983, 1981 | 1992:-the restructuring that took place after unification created over 100 non-university research units in the government sector; -the methodology of the surveys on resources devoted to R&D in the government sector was altered. |
|
| Source of funds | 1981, 1983 | 1983 and 1981: downwards adjustment of R&D performed by the government sector and of government funding to higher education. | |
| Type of costs | |||
| Type of R&D | |||
| 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
Enhanced and detailed categories for personnel, especially regarding researchers.
The coverage is not fully in accordance with the national accounts, we question some institutions which are classified in BES by the national accounts, science they are mainly funded by government. We referto FM Chapter 3.20, thus, funding may also be a factor for classification.
PNP is included in GOV.
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.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Enhanced and detailed categories for personnel, especially regarding researchers.
The coverage is not fully in accordance with the national accounts, we question some institutions which are classified in BES by the national accounts, science they are mainly funded by government. We refer to FM Chapter 3.20, thus, funding may also be a factor for classification.
PNP is included in GOV.
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 |
| - | - | - | - | - | - |
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | 17047649 |
116900 | 62700 |
| Final data (deliveredT+18) |
16761071 | 119268 | 63701 |
| Difference (of final data) | -286578 | 2368 | 1001 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | FTE: 119268; Labour Costs: 8850982 (in Thousands) |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | - |
(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) | % sub-contracted1) | |
| Staff costs | Not available | |
| Data collection costs | Not available | |
| Other costs | Not available | |
| Total costs | Not available | |
| Comments on costs | ||
| We have not yet calculate the costs. | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | Approx. 1000 | |
| Average Time required to complete the questionnaire in hours (T)1 | Not available | |
| Average hourly cost (in national currency) of a respondent (C) | Not available | |
| Total cost | Not available |
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. 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.
18.1.1. Data source – general information
| Survey name | Ausgaben, Einnahmen und Personal der öffentlichen und öffentlich geförderten Einrichtungen für Wissenschaft, Forschung und Entwicklung |
| Type of survey | Census Survey, annual Since 2014: online survey. 2003 - 2014: Mixed mode (postal and online). Survey by the Federal Statistical Office combined with relevant data taken from financial and manpower statistics for Federal, Länder and local governments. |
| Combination of sample survey and census data | - |
| Combination of dedicated R&D and other survey(s) | - |
| Sub-population A (covered by sampling) | - |
| Sub-population B (covered by census) | - |
| Variables the survey contributes to | Personnel (HC, FTE) by sex, personnel categories, FORD, region Expenditures by source of funds, FORD, type of costs, socioeconomic objective (every fourth year), type of R&D (every fourth year), region |
| Survey timetable-most recent implementation |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | No sampling but complete count based on household budgets and Internet search | ||
| Stratification variables (if any - for sample surveys only) | |||
| Stratification variable classes | |||
| Population size | |||
| Planned sample size | |||
| Sample selection mechanism (for sample surveys only) | |||
| Survey frame | |||
| Sample design | |||
| Sample size | |||
| Survey frame quality |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | We do not collect administrative data or produce pre-compiled statistics |
| Description of collected data / statistics | |
| Reference period, in relation to the variables the survey contributes to |
18.2. Frequency of data collection
yearly
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Information is collected within a survey asking every single research institution -> micro level |
| Description of collected information | In the survey information about expenditure, revenues/funds and personnel is collected from every research institution using different questionnaires for financial and personnel data. |
| Data collection method | Online survey. The survey is regularly adjusted for better comprehensibility. There are detailed documents and support for the respondents. Beyond that there is a team which can be contacted with queries. |
| Time-use surveys for the calculation of R&D coefficients | - |
| Realised sample size (per stratum) | - |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | online |
| Incentives used for increasing response | - |
| 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) | see 13.3.3 |
| Non-response analysis (if applicable -- also see section 18.5. 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: |
|
| R&D national questionnaire and explanatory notes in the national language: | Expenditures: https://erhebungsportal.estatistik.de/Erhebungsportal/#kBFi78T5sQOW3BEb/unterstuetzte-statistiken/bildung/hochschulen/wissenschaft-forschung-ausgaben-und-einnahmen Personal: https://erhebungsportal.estatistik.de/Erhebungsportal/#TB2AeXEFvbf36rIN/unterstuetzte-statistiken/bildung/hochschulen/wissenschaft-forschung-entwicklung-beschaeftigte |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
different data validation processes:
- comparing the statistics with previous cycles;
- calculation FTE/labour costs;
- comparing personnel and expenditures at micro level;
- investiganting inconsistencies in the statistics;
contact to respondents if inconsistencies or large changes occur.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Generally no imputations necessary (mandatory variables).
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | The survey is conducted annually. |
| Data compilation method - Preliminary data |
Current data from survey already available are used for some institutions. If not available: Final data from the previous survey are adjusted with rates of change from other sources e.g. from public budget statistics or from annual business reports of the research institutions themselves. The adjustment is done separately for different types of institutions. The data for Institutions with no detailed annual report are estimated on the basis of the trends of the past five years |
18.5.3. Measurement issues
| Method of derivation of regional data | Information of local units from personnel questionnaire were used to deviate expenditures |
| Coefficients used for estimation of the R&D share of more general expenditure items | Since 1992 when the questionnaire was revised, the respondents have been asked to report the specific R&D coefficients of the statistical units and the distribution of the R&D expenditure to the relevant scientific fields. These R&D coefficients are applied in estimating the R&D expenditure and personnel |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT excl. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | - |
18.5.4. Weighting and estimation methods
| Description of weighting method | no weighting |
| Description of the estimation method | Non probabilistic methods: secondary data, previous year, industry averages |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government 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.
Statistics on science, technology and innovation were collected based on the Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.
31 October 2023
See below.
The statistical units are those included the in the National Accounts plus PNP sector and some profit institutions which are financed by the government for the most part
See below.
Not requested.
expenditures: a calendar year
personnel: point in time (30. June)
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.
expenditures: Euro ( thousand)
personnel: HC
See below.
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.
yearly
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
