1.1. Contact organisation
Swiss Federal Statistical Office (FSO)
1.2. Contact organisation unit
Division WI (Economy),
Section WSA (Economic structure and analysis)
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Office fédéral de la Statistique (OFS)
Espace de l'Europe 10
2010 Neuchâtel
SWITZERLAND
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
No fax.
31 October 2025
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
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.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The 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).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
The definition of R&D complies with the Frascati Manual.
3.3.2. Sector institutional coverage
| Government sector | This sector comprises all top-level federal government agencies, listed as follows:
This sector does not yet include government agencies at the cantonal and municipal level. |
|---|---|
| Hospitals and clinics | Not included yet, as they belong to the cantonal level |
| Inclusion of units that primarily do not belong to GOV and the borderline cases. |
No |
3.3.3. R&D variable coverage
| R&D administration and other support activities | R&D administration and other support activities are part of R&D. |
|---|---|
| External R&D personnel | We do not ask for external R&D personnel. |
| Clinical trials: compliance with the recommendations in FM §2.61. | Yes, we use the Frascati Manual criteria in Switzerland. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Yes, available |
|---|---|
| Payments to rest of the world by sector - availability | Yes, available |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), 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 | They are separated in 2 different items in the survey |
| Difficulties to distinguish intramural from extramural R&D expenditure | No |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year (starting from reference year 2021 before it was only odd years) |
|---|---|
| Source of funds | Data are collected for each source of fund, in accordance with FM (2015) |
| Type of R&D | In accordance with FM (2015) |
| Type of costs | In accordance with FM (2015) |
| Defence R&D - method for obtaining data on R&D expenditure | We have a question on SEO NABS "Defence" in the government sector. R&D for defence purposes is part of the socioeconomic objectives (NABS14). Socioeconomic objectives is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS). |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year (starting from reference year 2021 before it was only odd years) |
|---|---|
| Function | In accordance with FM (2015) |
| Qualification | In accordance with FM (2015). We use the ISCED-2011 classification but have less detailed breakdowns than those recommanded by the FM.
|
| Age | Not available |
| Citizenship | In accordance with FM (2015), we collect the breakdown Swiss/Foreigner |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year (starting from reference year 2021 before it was only odd years) |
|---|---|
| Function | In accordance with FM (2015) |
| Qualification | In accordance with FM (2015). We use the ISCED-2011 classification but have less detailed breakdowns than those recommanded by the FM.
|
| Age | Not available |
| Citizenship | Not available |
3.4.2.3. FTE calculation
For the government sector, the calculation method is given in the annex of the questionnaire:
"One full-time equivalence on R&D is the equivalent of one R&D employee working full-time for one year. Full-time equivalence on R&D is calculated by taking the type of workweek (full-time or part-time %), the duration of employment, and the portion of time devoted to R&D and multiplying these figures together".
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
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 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 national target population covers all federal government agencies active in R&D. | |
| Estimation of the target population size | 45 units |
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 all the federal government agencies. All the federal government agencies have to fill in the information system ARAMIS with their R&D projects (intramural or extramural R&D) The information system ARAMIS is collecting all the R&D projects of all federal government agencies. The Swiss R&D data in the Government sector are extracted from this information system ARAMIS. It does not match exactly with the FM2015. We do not include all the institutional units classified by the national accounts (ESA) as included in the general government (S13), (with the exclusion of those units included in the Higher Education sector (HES)). ARAMIS = Administration Research Actions Management Information System. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | The information system ARAMIS is collecting all the R&D projects of all federal government agencies. The information system ARAMIS is managed by the State Secretariat for Education, Research and Innovation SERI (in the Federal Department of Economic Affairs, Education and Research) The Swiss R&D data in the Government sector are extracted from this information system ARAMIS. |
| Inclusion of units that primarily do not belong to the frame population | No |
| Systematic exclusion of units from the process of updating the target population | No |
| Estimation of the frame population | It is a census, no estimation |
3.7. Reference area
Not requested
3.8. Coverage - Time
Not requested.
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
2023
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on 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. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
Switzerland delivers R&D data on a volountary basis
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Yes
|
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
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
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
a) Confidentiality protection required by law:
Federal Statistics Act (FStatA) of 9 October 1992 (RS 431.01)
b) Confidentiality commitments of survey staff:
Federal Statistics Ordinance of 30 April 2025 (OFS)
7.2. Confidentiality - data treatment
microdata are not published. Nevertheless, aggregated results are published by department (non anonymised).
8.1. Release calendar
The calendars of statistical publications are publicly available.
The data are available in June
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
For Switzerland this is: Agenda | Federal Statistical Office - FSO
8.3. Release policy - user access
Statistical information shall be disseminated in such a way that all users can access it simultaneously. All users have access to statistical publications at the same time and under the same conditions, and any privileged pre-release access granted to an external user is limited, controlled and made public. Some authorities may receive advance information under embargo in order to prepare for possible questions. The policy on consultations and advance information regulates the modalities.
Source: LSF 18.1, Charte Principes fondamentaux 9 et 10, CoP 10 ind. 6
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
The frequency of R&D data dissemination in Switzerland fot the GOV sector is every year (since 2021, before it was every two years). Switzerland do not produce provisional data
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | 10% rise in Confederation's research and development expenditure in 2023 - Confederation R&D expenditure and personnel in 2023 | Communiqué de presse |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | Y | Recherche et développement en Suisse 2023 - Finances et personnel | Publikation |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
There is an indicator on R&D expenditure in the GOV sector and related data tables on our website.
Système d'indicateurs Science et Technologie | Office fédéral de la statistique (admin.ch)
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to the micro-data |
Not available |
|---|---|
| Access cost policy | Not applicable |
| Micro-data anonymisation rules | No 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 results available on the national statistical authority’s website | Y | Aggregate figures | Science and Technologie indicator |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | Data prepared for individual ad hoc request |
| Other | Y | Aggregate figures | Unregular paper publication |
1) Y – Yes, N - No
10.6. Documentation on methodology
see below
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.) | Graphs and analytical comments |
|---|---|
| Requests on further clarification, most problematic issues | Explanation on methodology |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
At FSO level, the quality is insured by our methodological services (ongoing process)
11.2. Quality management - assessment
As it is an exhaustive data collection, mandatory for all the federal government agencies, the data quality is good.
In order to better follow the FM2015 recommendations, we plan to adapt the R&D questionnaire: changes in the formulation of the question, new breakdown, new questions. We plan to improve the information accompanying the questions
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1-Institutions | OECD and ESTAT | All R-D statistics |
| 1-Institutions | State Secretariat for Education, Research and Innovation (SERI). The SERI within the Federal Department of Home Affairs is the federal government's specialised agency for national and international matters concerning general and university education, research and space. | All the R-D and STI statistics needed for the redaction of the “Message relating to the encouragement of the formation, research and innovation” and for the strategic controlling of the formation, research and the technology objectives. |
| 1-Institutions | State Secretariat for Economic Affairs (SECO). The SECO is the Confederation's competence centre for all core issues relating to economic policy. | All kind of R-D and STI statistics. |
| 2-Social actors | Economisuisse. Federation of the Swiss companies. | All kind of R-D and STI statistics |
| 3-Media | Media in general and in particular:“economic life, the review of economic policy”. Published under the auspices of the Secretariat for Economic Affairs SECO, this review: “economic life, the review of economic policy” analyzes every month the economic evolution of the country. Moreover, it regularly publishes statistical data of which R-D statistics. | Main R-D statistics |
| 4- Researchers and students | Universities in general and in particular: the Swiss Institute for Business Cycle Research (KOF) within the Swiss Federal Institute of Technology of Zurich, (ETHZ). The KOF within the Swiss Federal Institute of Technology of Zurich supplies information in the range of the economic and market research. |
R-D statistics for the validation of the Innovation survey. |
| 4- Researchers and students | Universities in general and in particular: the Chair of Economics and Management of Innovation (CEMI) within the Swiss Federal Institute of Technology of Lausanne, (EPFL). CEMI - the Chair of Economics and Management of Innovation - is a research laboratory established at EPFL to develop teaching and research in the area of economics and managementm of innovation, knowledge and technology. |
All kind of R-D and STI statistics. |
| 4- Researchers and students | Researchers and students. | All kind of R-D and STI statistics |
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 | The FSO conducts non-regular surveys on user satisfaction. |
|---|---|
| User satisfaction survey specific for R&D statistics | We do not conduct user satisfaction survey specific for R&D statistics. |
| Short description of the feedback received | We are in frequent contact with our various partners involved in the GOV statistics. We receive good feedbacks |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
see below
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.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Switzerland do not produce preliminary variables |
| Obligatory data on R&D expenditure | Switzerland provides data on a voluntary basis (Variation coefficient too high or poor quality due to a too small number of R&D performers) |
| Optional data on R&D expenditure | Switzerland provides data on a voluntary basis (No measure of this item, variation coefficient too high or poor quality due to a too small number of R&D performers) |
| Obligatory data on R&D personnel | Switzerland provides data on a voluntary basis (Variation coefficient too high or poor quality due to a too small number of R&D performers) |
| Optional data on R&D personnel | Switzerland provides data on a voluntary basis (No measure of this item, variation coefficient too high or poor quality due to a too small number of R&D performers) |
| Regional data on R&D expenditure and R&D personnel | Switzerland provides data on a voluntary basis (No measure of this item) |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y - 1986 | every year | Before 2021 every two years | |||
| Type of R&D | Y - 1996 | every year | Before 2021 every two years | |||
| Type of costs | Y - 1996 | every year | Before 2021 every two years | |||
| Socioeconomic objective | Y - 1986 | every year | Before 2021 every two years | |||
| Region | N | |||||
| FORD | N | |||||
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y - 1986 | every year | Before 2021 every two years | |||
| Function | Y - 1986 | every year | Before 2021 every two years | |||
| Qualification | Y - 1986 | every year | Before 2021 every two years | Switzerland doesn't have the same group category as used in international comparison for ISCED-11 | ||
| Age | N |
|
||||
| Citizenship | Y - 1986 | every year | Before 2021 every two years | |||
| Region | N |
|||||
| FORD | N | |||||
| 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 | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | N | |||||
| Function | Y - 1986 | every year | Before 2021 every two years | |||
| Qualification | Y - 1986 | every year | Before 2021 every two years | Switzerland doesn't have the same group category as used in international comparison for ISCED-11 | ||
| Age | N | |||||
| Citizenship | N | |||||
| Region | N | |||||
| FORD | N | |||||
| Type of institution | N |
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 |
|---|---|---|---|---|---|
| Extramural R&D expenditure (mandates) by nature of expenditure | Y - 2000 | ||||
| Extramural R&D expenditure by beneficiaries | Y - 2000 |
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
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| No |
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 errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | Not applicable (census) | ||||||
| Total R&D personnel in FTE | Not applicable (census) | ||||||
| Researchers in FTE | Not applicable (census) | ||||||
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | 5 | ||||
| Total R&D personnel in FTE | 4 | ||||
| Researchers in FTE | 4 |
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' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
There is no sampling, but a census. Each unit has to provide the information (mandatory). --> no non-response.
13.2.1.2. Confidence interval 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. Confidence interval for R&D personnel by occupation 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.
a) Description/assessment of coverage errors :
No coverage errors (census)
b) Measures taken to reduce their effect:
Not applicable
c) Share of PNP (if PNP is included in GOV):
Not included
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:
No measurement errors it is a census
b) Measures taken to reduce their effect:
Not applicable
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)] * 100
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) |
|---|---|---|
| 37 | 37 | 0 |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item Non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible, for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 0 | 0 | 0 |
| Comments | mandatory census | mandatory census | mandatory census |
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 and manual entry |
|---|---|
| Estimates of data entry errors | No error estimates |
| Variables for which coding was performed | No coding was done |
| Estimates of coding errors | Not applicable |
| Editing process and method | Consistency of data is automatically controlled when entering the data into the plateform |
| Procedure used to correct errors | Not applicable |
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: Decembre 31st of the reference year
b) Date of first release of national data: July (Y+1)
c) Lag (days): 6 month
NB: we have only final results (no provisional results)
14.1.2. Time lag - final result
a) End of reference period: Decembre 31st of the reference year
b) Date of first release of national data: July (Y+1)
c) Lag (days): 6 month
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) | We do not have provisional data | 18 |
| Delay (days) | Not applicable |
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
Not applicable.
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, Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No (only internal personal) | |
| Researcher | FM2015, § 5.35-5.39. | No (only internal personal) | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No (only internal personal) | |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No (only internal personal) | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No (only internal personal) | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | Yes | Hospital and clinic are not included in the government data collection. There are no government (federal) hospitals. |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | There is no breakdown into major fields of R&D in the GOVERD survey |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | 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 (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No | |
| Survey questionnaire / data collection form | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No | |
| Cooperation with respondents | FM2015 Chapter (mainly sub-chapter 8.5). | No | |
| Data processing methods | FM2015 Chapter 8 (mainly sub-chapters 8.5-8.6). | No | |
| Treatment of non-response | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.7). | - | As it is an exhaustive data collection, there is no estimation method. |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | Not applicable | No 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) | 1983-2017 | Several modifications in the coverage of the GOVERD survey. Public enterprises have moved into the private sector; public bodies disappear or change their assignment | |
| Function | |||
| Qualification | |||
| R&D personnel (FTE) | 1983-2017 | Several modifications in the coverage of the GOVERD survey. Public enterprises have moved into the private sector; public bodies disappear or change their assignmen | |
| Function | |||
| Qualification | |||
| R&D expenditure | 1983-2017 | Several modifications in the coverage of the GOVERD survey. Public enterprises have moved into the private sector; public bodies disappear or change their assignmen | |
| Source of funds | |||
| 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
No
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
Coherence with the national accounts sectorisation is insured.
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) | Not applicable | Not applicable | Not applicable |
| Final data (delivered T+18) | Not applicable | Not applicable | Not applicable |
| Difference (of final data) | Not applicable | Not applicable | Not applicable |
Comments:
We do not have preliminary data
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanations of consistency issues, if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not available | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use/person/day | |
|---|---|---|
| Staff costs | Not available | Not available |
| Data collection costs | Not available | Not available |
| Other costs | Not available | Not available |
| Total costs | Not available | Not available |
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
Not available
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 37 | |
| 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. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
The R&D survey in the Government sector is an exhaustive survey (census) conducted every year in all gouvernment agencies. This survey is mandatory
18.1.2. Sample/census survey information
| Sampling unit | The statistical unit is the government office or agency |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | 37 |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | This survey is included in a centralised information system (ARAMIS) designed to provide interested parties with information on research work funded or performed by central government, improve co-ordination and ensure greater transparency. The system integrates all research and development projects funded or performed by central government. Statistics on Federal services and offices are drawn from this databank. The integrated data are comparable to those that have been collected hitherto by questionnaire. |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Not applicable |
| Variables the survey contributes to | R&D expenditure and R&D personnel. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Not available |
|---|---|
| Description of collected data / statistics | Not available |
| Reference period, in relation to the variables the administrative source contributes to | Not available |
| Variables the administrative source contributes to | Not available |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | All the federal governement agencies |
|---|---|
| Description of collected information | The information collected is R&D expenditure and R&D personnel. All the variables requested by EU Regulation No 995/2012 except for Fields of R&D. |
| Data collection method | This survey is included in a centralised information system (ARAMIS) designed to provide interested parties with information on research work funded or performed by central government, improve co-ordination and ensure greater transparency. The system integrates all projects funded or performed by federal government. Those projects are divided into two groups: Projects with R&D and projects without R&D. In the projects with R&D, it is said the part (%) of R&D they contain. Statistics on federal government agencies are drawn from this information system ARAMIS. The integrated data are comparable to those that have been collected hitherto by questionnaire. ARAMIS = Administration Research Actions Management Information System. R&D statistics are extracted from ARAMIS. Information on R&D personnel is missing in ARAMIS. The R&D information from ARAMIS is completed with information collected via an R&D questionnaire integrated inside ARAMIS. |
| Time-use surveys for the calculation of R&D coefficients | Not applicable |
| Realised sample size (per stratum) | Census about 45 offices and government agencies |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Data collection in a databank: Information system ARAMIS |
| Incentives used for increasing response | Mandatory for the federal government agencies |
| Follow-up of non-respondents | Not applicable. Every office or agency respond. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 100% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Not applicable |
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: | RD_GOVSI_A_CH_2023_0000_an_R&D_GOV_Survey_FR.pdf RD_GOVSI_A_CH_2023_0000_an_R&D_GOV_Survey_DE.pdf |
| Other relevant documentation of national methodology in English: |
|
| Other relevant documentation of national methodology in the national language: | RD_GOVSI_A_CH_2023_0000_an_R&D_GOV_Meth_FR.pdf RD_GOVSI_A_CH_2023_0000_an_R&D_GOV_Meth_DE.pdf |
Annexes:
R&D_GOV_Survey_FR
R&D_GOV_Survey_DE
R&D_GOV_Meth_FR
R&D_GOV_meth_DE
18.4. Data validation
- Outlier detection (early in the process);
- Checking the population coverage;
- Benchmark the responses (of a same enterprise) with the responses of the previous survey with;
- Investigating inconsistencies in the statistics; performing micro and macro data editing;
- Verifying the statistics against expectations and budgets.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
| Data compilation method - Final data | ARAMIS records all the R&D projects. Information on R&D personnel is missing in ARAMIS. These data are automatically recorded in the R&D questionnaire. |
|---|---|
| Data compilation method - Preliminary data | No preliminary statistics |
18.5.3. Measurement issues
| Method of derivation of regional data | Not applicable |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Not applicable |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not applicable |
|---|---|
| Description of the estimation method | Not applicable |
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.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
31 October 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested
2023
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
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.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
The frequency of R&D data dissemination in Switzerland fot the GOV sector is every year (since 2021, before it was every two years). Switzerland do not produce provisional data
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.
