1.1. Contact organisation
Statistisches Bundesamt
1.2. Contact organisation unit
Unit H24 - Research, Culture
1.3. Contact name
Restricted from publication1.4. Contact person function
Restricted from publication1.5. Contact mail address
Martin Szibalski
Gustav-Stresemann-Ring 11
D-65180 Wiesbaden
Germany
1.6. Contact email address
Restricted from publication1.7. Contact phone number
Restricted from publication1.8. Contact fax number
Restricted from publication2.1. Metadata last certified
31 October 20232.2. Metadata last posted
31 October 20232.3. Metadata last update
31 October 20233.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- 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).
3.2.1. Additional classifications
| Additional classification used | Description |
| We do not use additional classifications | |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D |
Definition according FM: 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. Use of R&D coefficients. |
| Fields of Research and Development (FORD) |
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 by NABS) |
No data available |
For the Government sector, every fourth year a detailed breakdown is asked.
3.3.2. Sector institutional coverage
| Higher education sector | No deviation of the FM2015. Included are Universities, technical universities, university hospitals, teacher training colleges, comprehensive institutions of higher education (Gesamthochschulen), colleges of arts and music, specialised colleges with focus on applied sciences (Fachhochschulen). |
| Tertiary education institution | Yes, when main focus is academic, not professional. |
| University and colleges: core of the sector | Yes |
| University hospitals and clinics | Yes, total inclusion |
| HE Borderline institutions | Research institutions not administered by the university and independent from university budget (AN-Institute) are not included. They are included in GOV. |
| Inclusion of units that primary don`t belong to HES | NO |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Academic personnel which belongs to the administrative staff and administrative personnel of the teaching and research units is included. In the calculation process non-teaching and non-R&D activities of all personnel are eliminated. |
| External R&D personnel | In general all personnel with work contract with HES institutions are included (internal personnel). In addition, in HES post-graduate students receiving funding are included. The data are based on a special survey which includes post-graduate students who are engaged in R&D (in general working on a doctoral or post-doctoral thesis ("Habilitation")) and receive grants. The full amount of grants awarded to the post-graduate students is included in HERD. |
| 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 | Not available |
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 | Intramural and extramural expenditures are separately surveyed in the questionnaire |
| 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 higher education sector, the breakdown by sources of funds is only available at total sector level, not at fields-of-science level.
Until 1981, national estimate.
No data on internal/external funds and transfer/exchange funds are collected. Use of R&D coefficients to allocated R&D expenditures, transferability of the information on revenues to expenditures is problematic |
| Type of R&D | No breakdown as from 1994. 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. |
| Type of costs | No breakdown is available for the additional funds from the German Research Association (see details under Source of funds). |
| Defence R&D - method for obtaining data on R&D expenditure | We have no data available for HES in terms of R&D defence. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Fixed date |
| Function | Researchers = Academic (and creative arts) personnel = professors, university assistants, other academic personnel, teachers for special tasks and post-graduate students who receive scholarships. Technicians = Technical or library personnel. Others = Administrative personnel, workers, service personnel at university clinics, etc. |
| Qualification | University graduates = university and other post-secondary graduates. Other post secondary = included with university graduates. Secondary = holders of diplomas of secondary education. Other = all others. |
| Age | There are no data available. |
| Citizenship | There are no data available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Fixed date |
| Function | Researchers = Academic (and creative arts) personnel = professors, university assistants, other academic personnel, teachers for special tasks and post-graduate students who receive scholarships. Technicians = Technical or library personnel. Others = Administrative personnel, workers, service personnel at university clinics, etc. |
| Qualification | University graduates = university and other post-secondary graduates. Other post secondary = included with university graduates. Secondary = holders of diplomas of secondary education. Other = all others. |
| Age | There are no data available. |
| Citizenship | There are no data available. |
3.4.2.3. FTE calculation
FTE is calculated by taking 100% of personnel working full-time and 50% (main occupation) respectively 20% (second job "nebenberuflich") of personnel working part-time.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| We do not apply cross-classification for function and qualification | ||
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
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 of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional 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 consists of all institutions of the higher education sector, including university hospitals and all research institutes, centres, experimental stations and clincs that have their R&D activities under the direct control of, or administered by, tertiary institutions. . Borderline institutes located at universities but independent in accounting are included in GOV. | |
| Estimation of the target population size | Approx. 470 |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
- Questionnaire expenditures: In Euro.
- Questionnaire: Number of persons.
- Expenditures: calendar year.
- Personnel: point in time (1. Dec.).
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 Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. 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. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | The obligations arises from European legislation only. There is no national legal framework for this. |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Yes |
| Legal acts | Gesetz über die Statistik für das Hochschulwesen (Hochschulstatistikgesetz -HStatG) vom 2. November 1990 (BGBl. I S. 2414), das durch Art. 1 des Gesetzes vom 2. März 2016 (BGBl. I S. 342) novelliert wurde |
| 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
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development.
- European Business Statistics Methodological Manual on R&D Statistics.
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.
- Confidentiality protection required by law: Federal Statistics Act (BStatG).
- Confidentiality commitments of survey staff: Is ensured by oath of office.
7.2. Confidentiality - data treatment
Cell suppression, 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+16 month.
8.2. Release calendar access
No official release calendar for this statistic.
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 | Yes | Regular press release in autumn once a year, when the online publication is published |
| Ad-hoc releases | No |
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) |
Yes |
|
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
No |
1) Y – Yes, N - No
10.3. Dissemination format - online database
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 micro-data access to outside users |
| Access cost policy | |
| Micro-data anonymisation rules |
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 | Yes | The main figures are available in tables and graphs on the internet, detailed online publication available for download, GENESIS database (see above) | |
| Data prepared for individual ad hoc requests | Yes | With rules of privacy; time necessary is calculated for the customers, if the data preparation needs more than half an hour, the customer has to pay a fee | |
| Other | No | Data prepared for regular publications of other authorities, for example state statistical offices and federal and state ministries for education and research (see above) |
1) Y – Yes, N - No
10.6. Documentation on methodology
Quality report for data collection "personnel HES" ("Qualitätsbericht - Personal an Hochschulen").
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Information and clarity
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Quality report, methodological explanations in each publication, methodological articles, when methods are changed. |
| Request on further clarification, most problematic issues | Main 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. No mentionable complaints. |
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 - Statistisches Bundesamt
- Quality report for data collection "personnel HES" ("Qualitätsbericht - Personal an Hochschulen").
- Quality report for data collection "expenditures HES" ("Qualitätsbericht - Hochschulfinanzstatistik").
- Regular review of the implementation of quality guidelines (Qualitätsrichtlinien (QRL)).
11.2. Quality management - assessment
The overall assessment of the HES 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.
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 |
Users' class codification
- 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.
- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
- 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.
- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data).
- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services).
- 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 available |
| Short description of the feedback received | Not available |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
As R&D figures are based on mandatory surveys (HES expenditures and personnel) 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. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing 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 | N | |||||
| Type of R&D | N | |||||
| Type of costs | Y-1981 | |||||
| Socioeconomic objective | ||||||
| Region | ||||||
| FORD | ||||||
| Type of institution |
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 | |||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-1995 | |||||
| FORD | Y-1995 | Changes in national classification | 2015 | Adoption to national classification | ||
| 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 | |||||
| Qualification | Y-1995 | |||||
| Age | Y-1995 | |||||
| Citizenship | Y-1995 | |||||
| Region | Y-1995 | |||||
| FORD | Y-1995 | Changes in national classification | 2015 | Adoption to national classification | ||
| 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 | Y-1995 | ||||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
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 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 | Not relevant | 2 | 4 | 1 | 3 | +/- | |
| Total R&D personnel in FTE | Not relevant | 2 | 4 | 1 | 3 | +/- | |
| Researchers in FTE | Not relevant | 2 | 4 | 1 | 3 | +/- | |
- 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 ‘-‘.
- 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 |
- '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.
- 'Good' = In the event that at least one out of the three criteria above described would not be fully met.
- 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
- '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.
- '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. Coefficient of variation for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | Not applicable |
| Government | Not applicable |
| Higher education | Not applicable |
| Private non-profit | Not applicable |
| Rest of the world | Not applicable |
| Total | Not applicable |
13.2.1.3. Coefficient of variation for R&D expenditure by function and qualification
| R&D personnel (FTE) | ||
| Occupation | Researchers | Not applicable |
| Technicians | Not applicable | |
| Other support staff | Not applicable | |
| Qualification | ISCED 8 | Not applicable |
| ISCED 5-7 | Not applicable | |
| ISCED 4 and below | Not applicable |
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.
- Description/assessment of coverage errors:
All institutions of the higher education sector, including university hospitals and all research institutes, centres, experimental stations and clincs that have their R&D activities under the direct control of, or administered by, tertiary institutions are included in HES. Borderline institutes located at universities but independent in accounting are included in GOV.
Very good coverage.
- Measures taken to reduce their effect:
Yearly assessment, official registers.
13.3.1.1. Over-coverage - rate
Not applicable.
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.
- Description/assessment of measurement errors:
Measurement errors can occur while using R&D coefficients.
- Measures taken to reduce their effect:
Regular recalculation of the R&D coefficients based on current personnel 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 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) |
| 468 | 468 | 0 (mandatory survey) |
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. | Mandatory survey | No information available. |
13.3.3.3. Measures to increase response rate
Not relevant.
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 | Possible errors are checked by comparing the results with results from previous surveys and calling back the respondent units. |
| Estimates of data entry errors | Data from institutions are rejected if not correct. The information provider has to deliver new data. |
| 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 | Data from institutions are rejected if not correct. Contact and questions to the institution until correct data is provided. |
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).
- End of reference period: 31 December 2021.
- Date of first release of national data: submission to Eurostat: 21 November 2022.
- Lag (days): 286.
14.1.2. Time lag - final result
- End of reference period: 31 December 2021.
- Date of first release of national data: 30 Juin 2021.
- Lag (days): 545.
- End of reference period: 31 December 2021.
- Date of first release of national data: Submission to Eurostat: 30 Juin 2021.
- Lag (days): 545.
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 | No delays | No delays |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
2016 time use survey to assess the assumptions for R&D coefficient calculation -> adjustment of R&D coefficients.
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 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 paragraph 5.2). | NO | internal personnel and graduates (=external) |
| 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 Full-time equivalence (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 | NO | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | NO | |
| Statistical unit | FM2015 §3.70 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Target population | FM2015 §9.6 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | |
| Borderline research institutions | FM2015 §9.18-9.27 (in combination with the Eurostat's harmonised Methodological Guidelines). | NO | Coherent with FM2015 § 9.24 |
| Major fields of science and technology coverage and breakdown | Reg. 995/2012: Annex 1, section 1, § 7.3. | Yes | 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. |
| 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 | Questionaire |
| Survey questionnaire / data collection form | No | Online form |
| Cooperation with respondents | No | Deadline extension is possible |
| Coverage of external funds | No | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | Yes | No distiction is made, definition in Germany differs ("Grundmittel") |
| Data processing methods | No | Administrative data in combination with R&D coefficients |
| Treatment of non-response | No | Imputation |
| Variance estimation | No | Not required due to full survey |
| Method of deriving R&D coefficients | No | R&D coefficients on basis of time use survey 2016/2017, recalculation every 2 years on basis of personnel data |
| Quality of R&D coefficients | No | last update R&D coefficients: 2020 |
| Data compilation of final and preliminary data | No | We compilate final and preliminary data |
15.2. Comparability - over time
See below.
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) | 2016-2021 | 2016, 1995, 1991 | 2016: Revised methodology for R&D coefficients on basis of time use survey in 2016/2017 -> increase of R&D coefficients 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. 1991:graduate students conducting research and receiving grants for that purpose were included for the first time among higher education researchers (close to 10000 FTE). The relevant grants paid to them in 1991 were included in higher education R&D expenditure (HERD), so total grants paid to higher education in 1991 were well above the earlier figures, because only grants paid directly by government had been included up till then; the outcome was an increase in total government funding for R&D performed by higher education in that year. 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. |
| Function | 2016-2021 | Breakdown: Researchers, Technicans, other supporting staff. (According to the employment status or professional position of the higher education institution at which persons are employed.) |
|
| Qualification | Not available (optional) | ||
| R&D personnel (FTE) | 2016-2021 | 2016, 2006, 1995, 1991 | 2016: Revised methodology for R&D coefficients on basis of time use survey in 2016/2017 -> increase of R&D coefficients. 2006: Modification of the calculation method for R&D personnel (FTE). 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. 1991:graduate students conducting research and receiving grants for that purpose were included for the first time among higher education researchers (close to 10000 FTE). The relevant grants paid to them in 1991 were included in higher education R&D expenditure (HERD), so total grants paid to higher education in 1991 were well above the earlier figures, because only grants paid directly by government had been included up till then; the outcome was an increase in total government funding for R&D performed by higher education in that year. 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. |
| Function | 2016-2021 | Breakdown: Researchers, Technicans, other supporting staff. (According to the employment status or professional position of the higher education institution at which persons are employed.) |
|
| Qualification | Not available (optional) | ||
| R&D expenditure | 2016-2021 | 2016, 1995, 1991 | 2016: Revised methodology for R&D coefficients on basis of time use survey in 2016/2017 -> increase of R&D coefficients. 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. 1991:graduate students conducting research and receiving grants for that purpose were included for the first time among higher education researchers (close to 10000 FTE). The relevant grants paid to them in 1991 were included in higher education R&D expenditure (HERD), so total grants paid to higher education in 1991 were well above the earlier figures, because only grants paid directly by government had been included up till then; the outcome was an increase in total government funding for R&D performed by higher education in that year. 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. |
| Source of funds | 1995-2021 | Downwards adjustment of government funding to higher education. | |
| Type of costs | 1995-2021 | Breakdown: Current Costs (Labour Costs, other current costs), Capital Expenditures. | |
| Type of R&D | 1995-2021 | Breakdown: Basic research, Applied research, Experimental development for reference year 2016 only. Not available as a time series. (Optional for Higher Education Sector.) | |
| Other |
- 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
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
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. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
No HES sector in 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 |
| unknown | unknown | unknown | unknown | unknown | unknown |
15.3.4. Coherence – Education statistics
No information.
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 – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | 20612814 | 155600 | 120500 |
| Final data (delivered T+18) | 20661351 | 156543 | 120901 |
| Difference (of final data) | 48537 | 943 | 401 |
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: 156543; Labour Costs (in Mill.): 12461243 (in Thousand) |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | We have no information on the costs of external R&D personel in HES |
- 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.).
- 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 known | There are no sub-contracted |
| Data collection costs | Not known | There are no sub-contracted |
| Other costs | Not known | There are no sub-contracted |
| Total costs | Not known | There are no sub-contracted |
| Comments on costs | ||
- 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) | not known | not known |
| Average Time required to complete the questionnaire in hours (T)1 | not known | not known |
| Average hourly cost (in national currency) of a respondent (C) | not known | not known |
| Total cost | not known | not known |
- 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 | Hochschulpersonal- und Hochschulfinanzstatistik |
| Type of survey | Administrative statistics (Hochschupersonal- und Hochschulfinanzstatistik) |
| 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 | Total expenditure and personnel. R&D coefficients were used (last update: 2020). Data on postgraduate students receiving funding and on scholarships and fellowships for postgraduates are collected seperately by the Federal Statistical Office. |
| Survey timetable-most recent implementation | last survey 2022 (personnel data 2022, expenditure data 2021) |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | No sample survey | ||
| 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 | Hochschulpersonal- und Hochschulfinanzstatistik, decentral statistics via Statistical Offices of the Länder |
| Description of collected data / statistics | HES personnel and expenditures, R&D coefficients were seperately calculated and updated regularly (see Quality report) |
| Reference period, in relation to the variables the survey contributes to | 2021 (No deviation) |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Microdata of every institution is requested |
| Description of collected information | All mandatory variables are collected. |
| Data collection method | The administrative and questioned data is collected online by statistical offices of the federal states.The questionnaire is improved regularly. The federal states deliver their collected data to the federal statistical office. |
| Time-use surveys for the calculation of R&D coefficients | Last survey: 2016 |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Online |
| Incentives used for increasing response | No incentives, mandatory survey |
| 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
| FAnnex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | |
| R&D national questionnaire and explanatory notes in the national language: |
|
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
Data validation processes via federal states (Länder) at micro level.
Data validation processes at federal statistical office at macro level (e.g. comparing pervious years).
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) | There is no estimation. |
| Data compilation method - Preliminary data | Data is estimated on the basis of previous cycles. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | The coefficients are based on a empirical and normative design. It includes data and assumptions about the activities of personnel in the Higher education sector. Since repoting year 1995 the Federal Statistical Office distinguishes between "research funded by basic resources" and "research funded by external resources" in regard to R&D expenditure and R&D personnel. External funds and personnel financed by external funds are entirely classified as R&D. R&D expenditures and R&D personnel funded by basic resources are estimated by appling R&D coefficients. The R&D coefficients and the basic assumptions of the method for deriving the coefficients have been reviewed in years 2016 and 2017. There are different R&D coefficients and methods for the different types of universities and collegues within the higher education sector. Universities, collegues of education and collegues of theology: Form reporting year 2016, the R&D coefficients are subjected directly to time use for R&D. The Federal Statistical Office surveyed the scientific staff for its time use for R&D in the winter semester 2016/2017. The survey was voluntary and the data were wighted by the official figures of personnel in HES to ensure representativeness. R&D coefficients for universities are adjusted regularly on basis of Personnel Statistcs of Higher Education Sector. |
| Revision policy for the coefficients | The coefficients are revised every two years with the updated structure of scientific personnel. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | The last update of the coefficients was for the reported year 2018.The aggregation level for other higher education institutions than university is very general. This could affect the quality of the data. |
18.5.4. Measurement issues
| Method of derivation of regional data | Information of local units from student statistics (Studierendenstatistik) were used to deviate expenditures |
| Coefficients used for estimation of the R&D share of more general expenditure items | R&D coefficients are used. The coefficients are based on a empirical and normative design. It includes data and assumptions about the activities of personnel in the Higher education sector. Since repoting year 1995 the Federal Statistical Office distinguishes between "research funded by basic resources" and "research funded by external resources" in regard to R&D expenditure and R&D personnel. External funds and personnel financed by external funds are entirely classified as R&D. R&D expenditures and R&D prsonnel funded by basic resources are estimated by appling R&D coefficients. The R&D coefficients and the basic assumptions of the method for deriving the coefficients have been reviewed in years 2016 and 2017. There are different R&D coefficients and methods for the different types of universities and collegues within the higher education sector. Universities, collegues of education and collegues of theology: Form reporting year 2016, the R&D coefficients are subjected directly to time use for R&D. The Federal Statistical Office surveyed the scientific staff for its time use for R&D in the winter semester 2016/2017. The survey was voluntary and the data were wighted by the official figures of personnel in HES to ensure representativeness. R&D coefficients for universities are adjusted regularly on basis of Personnel Statistcs of Higher Education Sector. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT excl. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | Not possible. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | See above |
18.5.5. Weighting and estimation methods
| Description of weighting method | No weighting |
| Description of the estimation method | Nonprobabilistic methods: secondary data, previous year, industry averages |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comment
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
See below.
Not requested.
- Expenditures: calendar year.
- Personnel: point in time (1. Dec.).
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.
- Questionnaire expenditures: In Euro.
- Questionnaire: Number of persons.
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.