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
29 September 2025
2.2. Metadata last posted
29 October 2025
2.3. Metadata last update
29 October 2025
3.1. Data description
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
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
| Business enterprise sector (BES) |
This sector only covers private enterprises. The selection of the economic branches covered by the survey R-D Priv 2004 was done starting from the general Nomenclature of the economic activities 2002 (NOGA 2002). |
|---|---|
| Hospitals and clinics | Private hospitals and clinics are included in the business enterprise sector. |
| Inclusion of units that primarily do not belong to BES 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 |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Yes, 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 enterprise) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | 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 | Biennial (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) |
| Economic activity of the unit | In accordance with FM (2015) |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | In accordance with FM (2015) |
| Product field | 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 business enterprise 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 | Biennial (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 | Biennial (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 business 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 for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993
3.6. Statistical population
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
3.6.1. National target population
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | The national target population (R&D survey target population) is all the active private business enterprises performing R&D. It is obtained thanks to a screening in the national frame population. | |
| Estimation of the target population size | Around 22000 enterprises in which 4000 R&D performer | |
| Size cut-off point | Only the companies employing 10 persons and more are selected in order to eliminate the small companies which have little or no means to conduct R-D. The exception to this rule is the R&D branch (NACE 72), which is, of course, recognized as R&D intensive. As it contains many small companies, the cut-off of 10 employees is not taken into consideration and all companies in the R&D branch are maintained within the frame population. | |
| Size classes covered (and if different for some industries/services) | Only the companies employing 10 persons and more are selected in order to eliminate the small companies which have little or no means to conduct R-D. The exception to this rule is the R&D branch (NACE 72), which is, of course, recognized as R&D intensive. As it contains many small companies, the cut-off of 10 employees is not taken into consideration and all companies in the R&D branch are maintained within the frame population. | |
| NACE/ISIC classes covered | Enterprises are classified according to their main economic activity and data are adjusted in accordance with the OECD classification derived from the International Standard Industrial Classification (ISIC rev4 and NACE Rev2) adapted to R&D statistics. The Swiss industrial classification is the “General nomenclature of economic activities” (NOGA). The NOGA 2008 corresponds in all aspects to the NACE Rev2 up to the 4th level. The characteristics, particular to Switzerland, are at the 5th level only. For reasons of confidentiality and data quality it’s impossible to publish data for all economic activities listed in Regulation No 995/2012. Moreover there are economic activities which do not exist in Switzerland or do not carry out R&D. This is the reason why not all NACE-activities mentioned in the Regulation No. 995/2012 are covered in the 2015 R&D survey. Branches excluded in the Swiss population frame: 33, 43, 45-46 (except 465), 47, 49-52, 55-56, 63 (except 631), 64-66, 68, 74, 77, 82, 84-94, 95 (except 951), 96-99 |
3.6.2. Frame population – Description
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
| Method used to define the frame population | The national frame population is all the active enterprises listed in the Official Business Register. The National Business Register is maintained by the Federal Statistical Office |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Two-Step Survey Since 2008, the R&D survey in private business enterprises has been carried out in two stages. The first step, screening, is to identify companies performing R&D in Switzerland. During this phase, companies receive a questionnaire containing only one question: "Has your company carried out or plans to carry out R&D expenditures in the current year"? In the second stage, the survey itself, only companies that answered "yes" to the screening are asked. Target population (2 stages) Companies that are the subject of other R&D surveys, such as public administration or higher education institutions, are the first excluded from the target population. Of the remaining entrprises, most of them, those registered in industries that are recognized as not very active in terms of R&D, for example, hotels and transport, are automatically eliminated. Then, in the remaining branches, only companies employing 10 persons or more are retained. The only exception to this rule is the "Research and Development" industry, which is recognized as intensive in R&D, and is fully questioned. The target population is subdivided into strata constructed on the basis of two criteria: the size (number of persons employed) and the industry (NACE) of the enterprises. The R&D screening questionnaire is addressed to this first "R&D SCREENING target population". The companies, which at the screening stage declare themselves to be active in R&D, form the "R&D SURVEY target population". |
| Inclusion of units that primarily do not belong to the frame population | No |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | No |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | Not applicable |
| Systematic exclusion of units from the process of updating the target population | We exclude systematically companies in specific industries and companies with less than 10 persons employed, (except in R&D Industry 72). Industries systematically excluded from the target population are: 01 02 33, 43, 45-46 (except 465), 47, 49-52, 55-56, 63 (except 631), 64-66, 68, 74, 77-82, 84-94, 95 (except 951), 96-99. |
| Estimation of the frame population | Around 4000 R&D performer |
1) i.e. enterprises previously not known or not supposed to perform R&D
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 the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
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: | Not mandatory |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
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. No confidential data is delivered
8.1. Release calendar
The calendars of statistical publications are publicly available.
The data are available in December
8.2. Release calendar access
At Eurostat level 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
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
The frequency of R&D data dissemination in Switzerland fot the BES sector is every two year (odd years). Switzerland do not produce provisional data
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.5 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | CHF 18 billion invested in R&D in Switzerland in 2023 - Research and Development (R&D) in the Business Enterprise Sector 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) | Y | Hausse des dépenses de R-D des entreprises - | Publication |
1) Y – Yes, N - No
10.3. Dissemination format - online database
There is an indicator on R&D expenditure in the BES sector and related data tables on our website.
Système d'indicateurs Science et Technologie | Office fédéral de la statistique - OFS
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | Yes, under specific conditions |
|---|---|
| Access cost policy | see «Ordonnance sur les émoluments et indemnités perçus pour les prestations de services statistiques des unités administratives de la Confédération » of 25 June 2003. |
| Micro-data anonymisation rules | Only anonymised 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 | 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
Please see the sub-concept 10.7.1 in the full metadata view.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | 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
Generally the data quality achieved with the above-described methodology can be considered as being good.
12.1. Relevance - User Needs
Please see the sub-concept 12.1.1 in the full metadata view.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1-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 innovation. |
All R-D and STI statistics needed for the the drafting of the message on the promotion of education, research and innovation” and for the strategic controlling of education, research and innovation |
| 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 | Economiesuisse: federation of the swiss companies | All the R-D statistics. |
| 2-Social actors | Enterprises | 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. | All kind of R-D and STI 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 strudents | 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
| principal 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 receive good feedback from our partners |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
Relatively low due to the fact that a split of the data by 2-digit NACE is not possible for swiss results (variation coefficient too high)
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | 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 or 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 or variation coefficient too high or poor quality due to a too small number of R&D performers) |
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 - 1989 | every two years | Between 1989 and 2012 : every leap year. | Changes in the list of sources of funds over time | ||
| Type of R&D | Y - 1996 | every two years | Between 1996 and 2012 : every leap year. | |||
| Type of costs | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. | Between 1992 and 2004 add of Other expenditure (depreciation) Since 2004 Depreciation is replaced by Capital expenditure |
||
| Socioeconomic objective | Y -1992 | every two years | Between 1992 and 2012 : every leap year. | Breakdown by socio-economic objective available at chapter level. Not all SEO are available. 1. Health, 2. Agriculture, 3. Environment, 4. Energy, 5. Industrial production and technology, 6. Defense, 7. Other objectives. |
||
| Region | Y - 2008 | every two years | Between 2008 and 2012 : every leap year. | |||
| FORD | N | |||||
| Type of institution | Y - 2004 | every two years | Between 2004 and 2012 : every leap year. |
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 - 1996 | every two years | Between 1996 and 2012 : every leap year. | |||
| Function | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. | |||
| Qualification | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. | |||
| Age | N | |||||
| Citizenship | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. | |||
| Region | Y - 2008 | every two years | Between 2008 and 2012 : every leap year. | |||
| FORD | N | Starting frome the reference year 2008, the FORD is not asked. Previously it was asked in the survey for R&D personnel with tertiary level qualification | ||||
| Type of institution | Y - 2004 | every two years | Between 2004 and 2012 : every leap year. | |||
| Economic activity | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. | Because of data quality and data confidentiality, we cannot publish the data at NACE level 2.digit | ||
| Product field | N | |||||
| Employment size class | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. |
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 - 1992 | every two years | Between 1992 and 2012 : every leap year. | |||
| Qualification | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. | |||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y - 2008 | every two years | Between 2008 and 2012 : every leap year. | |||
| FORD | N | Starting frome the reference year 2008, the FORD is not asked. Previously it was asked in the survey for R&D personnel with tertiary level qualification | ||||
| Type of institution | Y - 2004 | Between 2004 and 2012 : every leap year. | ||||
| Economic activity | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. | Because of data quality and data confidentiality, we cannot publish the data at NACE level 2.digit | ||
| Product field | N | |||||
| Employment size class | Y - 1992 | every two years | Between 1992 and 2012 : every leap year. |
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 |
|---|---|---|---|---|---|
| Intramural R&D expenditure by type of technologie | Y - 2000 | Between 2000 and 2012 : every leap year. | Nanotechnologie since 2004 and Software since 2008 | ||
| Extramural R&D expenditure by nature of expenditure | Y - 2008 | Between 2008 and 2012 : every leap year. | |||
| Extramural R&D expenditure by beneficiaires | Y - 2015 | every two years | |||
| R&D personnel by nationality | Y - 1992 | Between 1992 and 2012 : every leap year. | Swiss-Foreigners |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| 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 | 2 | : | 2 | : | 1 | 2 | - |
| Total R&D personnel in FTE | 2 | : | 2 | : | 1 | 2 | - |
| Researchers in FTE | 2 | : | 2 | : | 1 | 2 | - |
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 | 4 | ||||
| Total R&D personnel in FTE | 3 | ||||
| Researchers in FTE | 3 |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
An approximation of the variance estimator was used by applying the ‘proc surveymeans’ (Taylor linearization) including the sample design (strata, finite population correction) and the final weights (inverse of selection probability adapted for unit non response).
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | 1.80% | 5.88% | 2.46% |
| R&D personnel (FTE) | 3.40% | 4.99% | 2.80% |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | 12.96% | 5.23% | 7.23% | 2.57% | 2.46% |
| R&D personnel (FTE) | 6.91% | 4.16% | 4.71% | 4.67% | 2.80% |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors:
Not applicable
b) Measures taken to reduce their effect:
Not applicable
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Frame misclassification rate
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
| By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
|---|---|---|---|---|---|
| Number or surveyed enterprises in the stratum (according to frame) | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Misclassification rate | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
| Number or surveyed enterprises in the stratum (according to frame) | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Misclassification rate | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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:
Not applicable
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 satisfying by computing the weighted and un-weighted response rate.
Definition:
Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100
13.3.3.1.1. Unit non-response rates by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| Number of units with a response in the realised sample | 818 | 7388 | 1868 | 448 | 10522 |
| Total number of units in the sample | 1323 | 9581 | 2670 | 622 | 14196 |
| Unit Non-response rate (un-weighted) | 38.17% | 22.89% | 30.04% | 27.97% | 25.88% |
| Unit Non-response rate (weighted) | 23.22% | 14.03% | 16.47% | 16.35% | 15.54% |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 5100 | 5422 | 10522 |
| Total number of units in the sample | 6886 | 7310 | 14196 |
| Unit Non-response rate (un-weighted) | 25.94% | 25.83% | 25.88% |
| Unit Non-response rate (weighted) | 15.55% | 15.50% | 15.54% |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.3.3.1.3. Recalls/Reminders description
Two reminders are sent. For some enterprises we do a telephone call in addition
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No non-response survey was done |
|---|---|
| Selection of the sample of non-respondents | Not applicable |
| Data collection method employed | Not applicable |
| Response rate of this type of survey | Not applicable |
| The main reasons of non-response identified | Not applicable |
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) (%) | 0.87% | 5.56% | 9.42% |
| Imputation (Y/N) | Y | Y | Y |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used | Ratio imputation | Ratio imputation | Ratio imputation |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | 0.67% |
| Total R&D personnel in FTE | 5.56% |
| Researchers in FTE | 9.42% |
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 | Manual control of each questionnaire by the survey managers. Consistency of data is controlled when entering the data with plausibilisations. |
|---|---|
| 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 | Balance edits were used to check the totals and subtotals and also the totals of two questions |
| Procedure used to correct errors | When the information was not consistent or not clear, the information provider was contacted again by telephone or email. |
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: December 31th of the reference year
b) Date of first release of national data: December (T+1)
c) Lag (days): 12 month
NB: we have only final results (no provisional results)
14.1.2. Time lag - final result
a) End of reference period: December 31th of the reference year
b) Date of first release of national data: December (T+1)
c) Lag (days): 12 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
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
None, the business enterprise sector only includes private enterprises.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No | |
| Researcher | FM2015, §5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics). | No | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | No differenciation between internal and external R&D personnal |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | Our data cannot be published by NACE 2 digits branches. Our R&D branches are groups formed by NACE 2 branches. Differences in the classification with international standard |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | Not all class sizes are surveyed. branch 72 is the only branches where enterprises with less than 10 employees are included in the survey |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period for all data | 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 preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | A screening has been made before the R&D survey to discover all enterprises performing R&D |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | |
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | Two written reminders and a contact by phone to all non-respondents. |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No | |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No preliminary data | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No |
15.2. Comparability - over time
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | 2000 | ||
| Function | 2000 | ||
| Qualification | 2000 | ||
| R&D personnel (FTE) | 2000 | ||
| Function | 2000 | ||
| Qualification | 2000 | ||
| R&D expenditure | 1996 | 1977, 1979, 1981, 1983, 1986, 1988, 1989, 1996 | 1996: introduction of the NOGA classification 1989, 1988, 1986, 1983: several modifications in the field covered by data and classifications 1981, 1979: the watch industry was included in the “Electrical sub-total” 1977: the watch industry was included in the “Machinery sub-total” |
| Source of funds | 2000 | ||
| Type of costs | 1996 | 1996 | 2004: calculation of R&D expenditure changed: capital expenses are included and depreciation is excluded 2000, 1996: data revised according to changes introduced in 2004 |
| Type of R&D | 2000 | ||
| Other | 2000 |
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 collection of data
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Some small adjustements are made on the sectorisation of some specific units to insure a perfect coherence between R&D data and SNA.
These adjustements are made during the R&D capitalisation process (R&D satellite account).
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 |
|---|---|---|---|---|---|
| Not applicable |
15.4. Coherence - internal
Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 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) | Explanation 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 |
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) | 1717 | counted (Only companies with R&D expenditure) |
| Average Time required to complete the questionnaire in hours (T)1 | 2.3 | Question integrated into questionnaire: weighted mean |
| Average hourly cost (in national currency) of a respondent (C) | Not applicable | Not applicable |
| Total cost | Not applicable | Not applicable |
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 private business enterprise sector is a two-phase survey conducted every two year (odd years). Respondents take part in the survey on a voluntary basis
- Phase 1 (screening): Exhaustive survey of the frame population. Identification of enterprises performing R&D
- Phase 2: Exhausitve survey of the enterprises identified during phase 1 (screening). Collection of more detailed information on R&D expenditure and R&D personnel
18.1.2. Sample/census survey information
| Sampling unit | The statistical unit is the Swiss private enterprise, namely an enterprise located in Switzerland, regardless of whether or not its head offices are located in Switzerland. | ||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable | ||||||||||||||||||||||||||||||||||||
| Stratification variable classes | Not applicable | ||||||||||||||||||||||||||||||||||||
| Population size | ... | ||||||||||||||||||||||||||||||||||||
| Planned sample size | Not applicable | ||||||||||||||||||||||||||||||||||||
| Sample selection mechanism (for sample surveys only) | Not applicable | ||||||||||||||||||||||||||||||||||||
| Survey frame | The target population is defined by successive selections from the 546'308 enterprises registered in the REE at the time of the survey | ||||||||||||||||||||||||||||||||||||
| Sample design | Two phase sampling of enterprises stratified by industry and size. Phase 1 (screening): exhaustive survey of the target screening population. Identification of enterprises performing R&D. Strates: cross between industries (economic activities) and size classes
|
||||||||||||||||||||||||||||||||||||
| Sample size | |||||||||||||||||||||||||||||||||||||
| Survey frame quality | |||||||||||||||||||||||||||||||||||||
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Not applicable |
|---|---|
| Description of collected data / statistics | Not applicable |
| Reference period, in relation to the variables the administrative source contributes to | Not applicable |
| Variables the administrative source contributes to | Not applicable |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
| Realised sample size (per stratum) | |
|---|---|
| Mode of data collection | Postal and eSurvey. Enterprises could also ask for an Excel questionnaire sent by email. |
| Incentives used for increasing response | Two reminders are sent. For some enterprises we do a telephone call in addition. |
| Follow-up of non-respondents | Two reminders are sent. For some enterprises we do a telephone call in addition. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Not available |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Questionnaire:
Explanatory notes:
|
| R&D national questionnaire and explanatory notes in the national language: | Questionnaire:
Explanatory notes:
|
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
Annexes:
R&D_BES_Survey_EN
R&D_BES_Appendix1_EN
R&D_BES_Appendix2_EN
R&D_BES_Appendix3_EN
R&D_BES_Appendix4_EN
R&D_BES_Survey_DE
R&D_BES_Appendix1_DE
R&D_BES_Appendix2_DE
R&D_BES_Appendix3_DE
R&D_BES_Appendix4_DE
R&D_BES_Survey_FR
R&D_BES_Appendix1_FR
R&D_BES_Appendix2_FR
R&D_BES_Appendix3_FR
R&D_BES_Appendix4_FR
R&D_BES_Survey_IT
R&D_BES_Appendix1_IT
R&D_BES_Appendix2_IT
R&D_BES_Appendix3_IT
R&D_BES_Appendix4_IT
18.4. Data validation
- Outlier detection (early in the process);
- Checking the population coverage and response rates;
- 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 domain intelligence.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)
18.5.1.1. Imputation rate by Size class
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | 0.000% | 0.000% | 1.598% | 1.548% |
| 10-49 employees and self-employed persons | 0.527% | 0.268% | 7.639% | 9.192% |
| 50-249 employees and self-employed persons | 0.298% | 0.170% | 3.566% | 2.853% |
| 250-and more employees and self-employed persons | 1.063% | 0.961% | 6.170% | 5.096% |
| TOTAL | 0.872% | 0.673% | 5.559% | 4.942% |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | 1.234% | 1.041% | 5.559% | 5.627% |
| Services2) | 0.101% | 0.066% | 6.237% | 3.862% |
| TOTAL | 0.872% | 0.673% | 4.302% | 4.942% |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
| Data compilation method - Final data | Until now no method is used to gather data for in-between years. Surveys are carried out every two year. |
|---|---|
| Data compilation method - Preliminary data | No preliminary data |
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
| Weight calculation method | The survey is actually a census.The inclusion probabilities, and hence the weights of selections, take the value 1. |
|---|---|
| Data source used for deriving population totals (universe description) | National Business Register |
| Variables used for weighting | Strata and non response rate |
| Calibration method and the software used | No calibration was done. |
| Estimation |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
29 October 2025
See below.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
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.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
The frequency of R&D data dissemination in Switzerland fot the BES sector is every two year (odd 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.
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
