1.1. Contact organisation
Ministry for Higher Education, Research and Space (MESRE)
1.2. Contact organisation unit
SIES - Sub-Directorate for Information Systems and Statistical Studies
Department of statistical studies on research
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Ministère de l’enseignement supérieur, de la recherche et de l'espace
DGESIP/DGRI – SIES – Département des études statistiques de la recherche
1 rue Descartes, 75231 Paris Cedex 05
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
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 Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
All resident enterprises are included regardless of the residence of their shareholders. All NACE codes from A to U are covered excepted the 10 following divisions : 33: Repair and installation ofmachinery and equipment |
|---|---|
| Hospitals and clinics | Private hospitals and clinics are included. |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | Some private high schools are included in the HES sector. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | There is no deviation from the Frascati Manual |
|---|---|
| External R&D personnel | External R&D personnels are taken into account in the total of the expenditures of R&D. |
| Clinical trials: compliance with the recommendations in FM §2.61. | Not precised in the questionnaire, thus all the stages of the trials are covered. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | The following categories can be distinguished : - receipts from EU - from international organisations - from foreign enterprise of the same enterprise group - from foreign enterprises not in the same group - foreign national organisations. |
|---|---|
| Payments to rest of the world by sector - availability | The following categories can be distinguished : - Payments to foreign enterprise of the same enterprise group into the EU and outside the EU - to foreign enterprises not in the same group, into the EU and outside the EU - international organisations - foreign national organisations. |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | The foreign-controlled enterprises are not excluded from the R&D totals. No specific question. Foreign-controlled enterprises are taken into account in the aggregate category "foreign enterprise of the same enterprise group". |
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 | In the questionnaire, intramural R&D and extramural R&D are collected separately. |
| Difficulties to distinguish intramural from extramural R&D expenditure | No difficulties |
3.4. Statistical concepts and definitions
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
3.4.1. R&D expenditure
| Coverage of years | Fiscal year (usually calendar year) |
|---|---|
| Source of funds | Sources of funds are compliant with FM §4.104-4.108, Table 4.3. |
| Type of R&D | Compliant with section 2.5 of the Frascati Manual All 3 types of R&D are collected. |
| Type of costs | Compliant with section 4.2 the Frascati manual. Labour costs, other current costs (icld costs for external R&D personnel), capital expenditures (breakdown by lands and buildings, instruments and equipments, capitalised computer softwares, capitalised personnel costs). |
| Economic activity of the unit | Main activity of the entreprise from Business register. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | No specific treatment to attribute an other industrie for enterprises in Nace 72. |
| Product field | Note applicable |
| Defence R&D - method for obtaining data on R&D expenditure | Question about the percentage of R&D expenditures that goes to defence. Due to specific defense confidentiality, we cannot be sure of exhaustivity in this field. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | 31st December of the reference year |
|---|---|
| Function | Compliant with Frascati manual. The R&D personnel of the business enterprise sector is classified by occupation. The researchers category corresponds to the specialists who work with the design or the creation of knowledge, products, processes, methods and new systems. Also included are executives and administrators who plan and manage the researchers work. The technicians category includes engineers whose work is not regarded as R&D per se as well as the other persons performing technical tasks linked with R&D projects. The support personnel consists of workers, qualified or not, and office personnel helping with R&D projects or who are directly associated with the implementation of such projects. |
| Qualification | Compliant with the ISCED-2011 classification. Researchers data are broken down by higher degree of qualification. |
| Age | Compliant with Frascati Manual. Data are even collected at more granular level : Less than 25, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, more than 65. |
| Citizenship | Compliant with Frascati Manual. Data are even collected at more granular level : France, EU except FR, UK, Europe except EU, USA, Americas except USA, Asia, Africa, Oceania, unknown. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
|---|---|
| Function | Compliant with Frascati manual. The R&D personnel of the business enterprise sector is classified by occupation. The researchers category corresponds to the specialists who work with the design or the creation of knowledge, products, processes, methods and new systems. Also included are executives and administrators who plan and manage the researchers work. The technicians category includes engineers whose work is not regarded as R&D per se as well as the other persons performing technical tasks linked with R&D projects. The support personnel consists of workers, qualified or not, and office personnel helping with R&D projects or who are directly associated with the implementation of such projects. |
| Qualification | Not available |
| Age | Not available |
| Citizenship | Not available |
3.4.2.3. FTE calculation
Examples are given to the respondents, taking into account pat-time and part-time on R&D projects :
3 researchers working part-time during the year and part-time on R&D projects: 3 × 0.5 × 0.5 = 0.75 FTE
2 technicians working part-time during the year and full-time on R&D projects: 2 × 0.5 × 1 = 1 FTE
3 people working part-time during the year and one-third of their time on R&D projects: 3 × 0.5 × 1/3 = 0.5 FTE in total
It is also necessary to take into account the actual presence during the year. For example, from January to October = 10/12 of the annual time, therefore:
3 people working part-time from January to October and one-third of their time on R&D projects: 3 × 0.5 × 10/12 × 1/3 = 0.42 FTE in total
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 target population is made of all companies located in France (including overseas departments and territories) likely to carry out R&D activity, regardless of their size and sector of activity. | Not applicable |
| Estimation of the target population size | For 2023, 30 000 legal units, 25 000 enterprises | Not applicable |
| Size cut-off point | Cut-off at 80% of the total of the BERD in an enterprise. | Not applicable |
| Size classes covered (and if different for some industries/services) | All size classes covered, no threshold is used. | Not applicable |
| NACE/ISIC classes covered | All NACE codes from A to U are covered excepted the 10 following divisions : 33: Repair and installation of machinery and equipment |
Not applicable |
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 | All the enterprises that conducted R&D project the previous year and enterprises detected through administrative sources such as R&D credit tax. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | The frame population is updated every year thanks to the following administrative files or statistical surveys : - Companies benefiting from the research tax credit, tax incentive for research (source : MESRI), - Companies receiving support from business incubators (source : MESRI), - Companies benefiting from the young innovative companies scheme (source : Central Agency of Social Security Associations - Acoss), - CIS (Community innovation survey) (source : The French National Institute of Statistics and Economic Studies - Insee). |
| 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 | Annual |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | Around 3500 new units, around 9% of the total population in legal unit |
| Systematic exclusion of units from the process of updating the target population | Every units that are off field of R&D are systematically excluded from the survey. Some of them can integrate the survey again a few years later if they are detected through administrative sources. As far as possible, the holdings are excluded from the process of updating the frame population. |
| Estimation of the frame population | 28 000 legal units over 30 000 at the begining of the data collection. |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
France (continental and overseas territories)
3.8. Coverage - Time
NAF rev 2 series are available from 2014
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.
Last period available is reference year 2023, calendar year.
Available every calendar year.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Business entreprise survey is compulsory for respondants. Survey is granted by the CNIS with a visa number (2023A067RE) and is listed in a decree of official surveys validated by the Ministry of economics and finance. Respondents may be fined if they dot not answer. |
|---|---|
| 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
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: National legal law that guarantee the protection of statistical confidentiality :
- Confidentiality commitments of survey staff: All the agents in charge of the survey have to sign an agreement to respect confidentiality.
7.2. Confidentiality - data treatment
In accordance with the national law mentionned in a), it is strictly forbidden to disclose statistical data.
For business statistics, every service that produces statistical data should respect 2 rules :
- at least 3 respondents in every cells of the table
- no more than 85% for the contribution of the variable of the table from the top contributor
In data aggregates, cells that don't respect the criterias are under primary secret (not visible), and secondary secret is used to prevent indirect estimation of the data.
8.1. Release calendar
Preliminary results : April N+2
Final results : July N+2 and November N+2 (more detailed results)
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
On the website of the ministry : Calendrier 2025 des publications statistiques du SIES | enseignementsup-recherche.gouv.fr
8.3. Release policy - user access
Data release are publicly and freely available on the website of the Ministry. All users have access to the information at the same time.
Individual data are available for researchers from the CASD if they are granted the access by the national secret committee : Enquête annuelle sur les moyens consacrés à la recherche et au développement dans les entreprises – Le CASD – Centre d'accès Sécurisé aux Données
At Eurostat level and National level, the frequency of R&D data dissemination is yearly for provisional and final data.
Every two years for additional information on R&D personnel.
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 | Data Release on Main R&D Indicators |
| Ad-hoc releases | Y | press release on the ministry website when the publication is disseminated |
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 | Data Release on Main R&D Indicators Recherche et developpement experimental en France-Resultats Detailles pour 2022 et 20203 Enseignement superieur de la recherche et de l'innovation en France-2025 |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y, online and paper for other institutions |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Open data datasets :
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 | Micro-data are available for researchers from the CASD if they are granted the access by the national confidentiality committee. |
|---|---|
| Access cost policy | Monthly price have to be paid (see Price List of Statistical Products) |
| Micro-data anonymisation rules | No anonymisation, due to the confidentiality rules applied to researchers (see see Secure Processing of Sensitive 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 | Ministry website : Publication.enseignement superior recherche |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | Specific requests from government bodies (inspections, Cour des comptes) and Insee |
| Other | Y | Micro data | For researchers only : Secure Processing of Sensitive Data |
1) Y – Yes, N - No
10.6. Documentation on methodology
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.) | Methodological documents are provided when individual data are released (to researchers). These include questionnaires, explanatory notes and a methodological guide presenting the survey population and sample, as well as the adjustments made, the results and variables description. |
|---|---|
| Requests on further clarification, most problematic issues | Clarifications requested between R&D branches and activity codes. |
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).
The R&D survey has obtained the Label of general interest and compliance to the rules of public statistic
11.2. Quality management - assessment
Every 5 years, a board of experts from the National Council of statistical information examines the quality of the survey and asks for clarifications and improvements which are assessed at the next session.
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 | Eurostat EBS regulation | aggregates |
| 1 | OCDE - MSTI | database |
| 1 | Insee - national accounts and CIS survey | detailed data |
| 1 | Ministries : R&D, Agriculture, Industry, Environement | national aggregates, breakdown by activity |
| 1 | Regional bodies | Regional data |
| 3 | Media | Disseminated data |
| 4 | Researchers | Micro-data |
| 6 | Cour des comptes, inspections générales de l'administration, des finances ou de l'éducation nationale | Specific questions |
1) Users' class codification
1- Institutions:
- European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
- in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
- International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes. )
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | No user satisfaction survey. Each year, a users committee meeting is organized with the main users (ministries and other administrations, trade unions, researchers). |
|---|---|
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | Each year, the companies surveyed receive a link to the Ministry's website with a summary of the main results of the survey. |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
100% all mandatory data sets transmitted
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | No missing cells. |
| Obligatory data on R&D expenditure | No missing cells. |
| Optional data on R&D expenditure | No missing cells. |
| Obligatory data on R&D personnel | No missing cells. |
| Optional data on R&D personnel | No missing cells. |
| Regional data on R&D expenditure and R&D personnel | No missing cells. |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y | yearly from 1970 | N | |||
| Type of R&D | Y | yearly from 1970 | N | |||
| Type of costs | Y | yearly from 1970 | N | |||
| Socioeconomic objective | N | |||||
| Region | Y | yearly from 1970 | N | |||
| FORD | N | |||||
| Type of institution | Y | yearly from 1970 |
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 | every two years | ||||
| Function | Y | every two years | ||||
| Qualification | Y | every two years | ||||
| Age | Y | every two years | ||||
| Citizenship | Y | every two years | ||||
| Region | Y | yearly | ||||
| FORD | N | |||||
| Type of institution | N | |||||
| Economic activity | Y | yearly | ||||
| Product field | N | |||||
| Employment size class | Y | yearly |
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 | yearly | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y | yearly | ||||
| FORD | N | |||||
| Type of institution | N | |||||
| Economic activity | Y | yearly | ||||
| Product field | N | |||||
| Employment size class | Y | yearly |
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 | from 1970 | yearly | - domestic R&D organisations - domestic companies from the same group / from another group - foreign enterprise of the same enterprise group into the EU and outside the EU - to foreign enterprises not in the same group, into the EU and outside the EU - international organisations - foreign national organisations. |
||
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 |
|---|---|---|
| FTE and HC | FTE and HC | |
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non- response errors | ||||
| Total intramural R&D expenditure | 4 | 1 | 3 | 5 | 2 | 6 | - |
| Total R&D personnel in FTE | 4 | 1 | 3 | 5 | 2 | 6 | - |
| Researchers in FTE | 4 | 1 | 3 | 5 | 2 | 6 | - |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
The variance estimation is not available.
The sampling method is a stratified sampling with two variables : region and R&D branch.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | Not available | Not available | Not available |
| R&D personnel (FTE) | Not available | Not available | Not available |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | Not available | Not available | Not available | Not available | Not available |
| R&D personnel (FTE) | Not available | Not available | Not available | Not available | Not available |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
Description/assessment of coverage errors: New legal units detected through adminsitrative sources, with main activity in (33,(45,46,47,55,56,68,97,98,99) are not included in the survey beacause of the small amount of R&D realised in those activities.
Measures taken to reduce their effect: Estimation of the R&D missing, to make sure the total of R&D missing is still insignificant.
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) | Does not apply. Legal units are classified into a size class and an industry after the survey. The preliminary classification at the beginning of the survey can be different, but is irrelevant as not used for data dissemination. | ||||
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | Does not apply. Legal units are classified into a size class and an industry after the survey. The preliminary classification at the beginning of the survey can be different, but is irrelevant as not used for data dissemination. | ||||
| Misclassification rate | Does not apply. Legal units are classified into a size class and an industry after the survey. The preliminary classification at the beginning of the survey can be different, but is irrelevant as not used for data dissemination. | ||||
| 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) | Does not apply. Legal units are classified into a size class and an industry after the survey. The preliminary classification at the beginning of the survey can be different, but is irrelevant as not used for data dissemination. | ||||
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | Does not apply. Legal units are classified into a size class and an industry after the survey. The preliminary classification at the beginning of the survey can be different, but is irrelevant as not used for data dissemination. | ||||
| Misclassification rate | Does not apply. Legal units are classified into a size class and an industry after the survey. The preliminary classification at the beginning of the survey can be different, but is irrelevant as not used for data dissemination. |
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
- Description/assessment of measurement errors: No error identified
- Measures taken to reduce their effect: No measure necessary.
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 | 1121 | 1545 | 1292 | 1312 | 5270 |
| Total number of units in the sample | 3103 | 2966 | 1911 | 1901 | 9881 |
| Unit Non-response rate (un-weighted) | 36.12633 | 52.09036 | 67.60858 | 69.01631 | 53.33468 |
| Unit Non-response rate (weighted) | 62.72950 | 66.18915 | 72.57529 | 64.44270 | 65.60069 |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 1943 | 3327 | 5270 |
| Total number of units in the sample | 3453 | 6428 | 9881 |
| Unit Non-response rate (un-weighted) | 56.26991 | 51.75793 | 53.33468 |
| Unit Non-response rate (weighted) | 69.39505 | 64.12450 | 65.60069 |
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
After the launch mail, one additional letter is sent by ordinary mail, and 2 other letters are sent with return receipt. Emails are sent at the same moment as the mails.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No |
|---|---|
| Selection of the sample of non-respondents | Does not apply. |
| Data collection method employed | Does not apply. |
| Response rate of this type of survey | Does not apply. |
| The main reasons of non-response identified | Heavy response burden and confidentiality of the topic. |
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) (%) | |||
| Imputation (Y/N) | Y | Y | Y |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used | Depending on the company's profile: N-1 answer or stratified hotdeck with crossover between employment and research branch | Depending on the company's profile: N-1 answer or stratified hotdeck with crossover between employment and research branch | Depending on the company's profile: N-1 answer or stratified hotdeck with crossover between employment and research branch |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | Not available |
| Total R&D personnel in FTE | Not available |
| Researchers in FTE | Not available |
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | Insee website dedicated to business surveys collection |
|---|---|
| Estimates of data entry errors | |
| Variables for which coding was performed | Department |
| Estimates of coding errors | none |
| Editing process and method | |
| Procedure used to correct errors | Direct questions to companies, additional sources of informations, estimations |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: 30 April 2025
- Lag (days): 485
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 31 July 2025
- Lag (days): 575
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No issues.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| 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 deviation | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | not calculated |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation |
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) | 2004, 2001, 1998, 1992 | 2004: In 2007, the sampling method in the BE sector was modified and the 2004 data revised according to the new methodology. 2001: Coverage of the BE sector was expanded, and the data communicated by the Ministry of Defense now cover research that was not considered as R&D in earlier years. 1998: The new estimates for 1998 also resulted in 1997 corrections to R&D personnel with an increase of 2257 FTE in the business enterprise sector. |
|
| Function | |||
| Qualification | |||
| R&D personnel (FTE) | 2004, 2001, 1998, 1992 | 2004: In 2007, the sampling method in the BE sector was modified and the 2004 data revised according to the new methodology. 2001: Coverage of the BE sector was expanded, and the data communicated by the Ministry of Defense now cover research that was not considered as R&D in earlier years. 1998: The new estimates for 1998 also resulted in 1997 corrections to R&D personnel with an increase of 2257 FTE in the business enterprise sector. |
|
| Function | |||
| Qualification | |||
| R&D expenditure | 2001, 1997, 1992, 1981 | 2004: In 2007, the sampling method in the BE sector was modified and the 2004 data revised according to the new methodology. 2001:Coverage of the BE sector was expanded, and the data communicated by the Ministry of Defense now cover research that was not considered as R&D in earlier years. 1997: Modifications to the boundary between R&D and other activities conducted by some very large enterprises resulted in a FRF 2.6 billion adjustment to domestic expenditure by business enterprises. BERD increased to FRF 113854 million from 111278 before revision. The new estimates for 1998 also resulted in 1997 corrections to R&D personnel with an increase of 2257 FTE in the business enterprise sector 1992:The data related to the business enterprise and government sectors are not comparable with the corresponding data for 1991 due to the transfer of agencies (France Télécom and GIAT Industries) from the government sector to the business enterprise sector, in accordance with the change in their legal status.- account has been taken of the repayment of reimbursable aid in the distribution of R&D expenditure by source of funding in the data supplied to the OECD. - the method used for the survey of the private non-profit sector has been modified 1981:The evaluation of R&D expenditure was modified to take account of: - a reassessment of the proportion of time devoted to research by lecturers. The Ministry of Education currently estimates this share to amount to 50% on average, whereas the coefficients previously supplied by the Ministry and applied until 1980 (natural sciences 65%, medicine 30% and social sciences 10%) amounted on average to approximately 35%; - the cost of research and development work by the Ministry of Defence in connection with the FOST (Strategic Ocean Force), which previously was not included under R&D; - the impact of levying VAT on public research bodies. |
|
| Source of funds | |||
| Type of costs | 1992 | Account has been taken of the repayment of reimbursable aid in the distribution of R&D expenditure by source of funding. | |
| 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
In the even years, data are collected in the same way as in the odd years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Survey results are the input for national accounts, there is no other source for R&D.
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 available | |||||
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) | 40516876 | 303204 | 211776 |
| Final data (delivered T+18) | 40629523 | 311701 | 222409 |
| Difference (of final data) | 112467 | 8497 | 10633 |
Comments :
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not available | We do not distinguish R&D labour costs by internal or external personnel |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available | We do not distinguish R&D labour costs by internal or external personnel |
(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 | 583 244 euros | 6,6 persons in FTE |
| Data collection costs | ||
| Other costs | 149 490 euros | |
| Total costs | 732 734 euros | 6,6 persons in FTE |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs :
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 9420 legal units | |
| Average Time required to complete the questionnaire in hours (T)1 | 2h17 | |
| Average hourly cost (in national currency) of a respondent (C) | Not available | |
| Total cost |
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
| Survey name | Survey on resources dedicated to research and experimental development in companies |
|---|---|
| Type of survey | Sample survey and census survey. The target population is made up of two sub-populations:
|
| Combination of sample survey and census data | Yes |
| Combination of dedicated R&D and other survey(s) | Survey on resources dedicated to research and experimental development in companies - Survey on R&D researchers and engineers |
| Sub-population A (covered by sampling) | 22,800 legal units are covered by the sampling |
| Sub-population B (covered by census) | 7,200 legal units are in the census |
| Variables the survey contributes to | The survey contributes to R&D expenditures, fundings, and personnel broken down by R&D orientation, by category of enterprise (SME, ISE, LE). |
| Survey timetable-most recent implementation | The survey is mandatory. Launched in March of year T+1, data collection completes in December T+1, first results in June T+2, complete results in December T+2. |
Sensitivity: CU Quota consumption: 2.25%
18.1.2. Sample/census survey information
| Sampling unit | Enterprises |
|---|---|
| Stratification variables (if any - for sample surveys only) | R&D orientation brokendown by 32 positions X turnover brokendown by 7 positions, stratification on NUTS2 region |
| Stratification variable classes | stratification on NUTS2 region |
| Population size | |
| Planned sample size | |
| Sample selection mechanism (for sample surveys only) | |
| Survey frame | The sampling frame of the R&D survey consists of enterprises that reported R&D expenditures in the previous year in addition from administrative data to point out the new enterprises that perform R&D activities. |
| Sample design | The sub-population A (sampled stratum) is made up of 2 strata : - the first stratum (stratum A.1) is made of all the units of the target population which are in a group, - the second stratum (stratum A.2) is made of all the units of the target population which are independant - the entreprises are sampled up to 80% of the BERD inside each group, and the independant LU are sampled to complete a threshold to reach a certain precision. |
| Sample size | 11 973 legal units, around 9 000 enterprises |
| Survey frame quality | |
| Variables the survey contributes to | 14 duplicate records detected in 2023 |
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) | 12 000 legal units, 9 000 enterprises |
|---|---|
| Mode of data collection | Web questionnaire |
| Incentives used for increasing response | Enterprises from exhaustive stratum (census) can have a fine for non response |
| Follow-up of non-respondents | Email, phone |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | For the most important non respondant legal units, they are imputed by the data from the previous year. For the non-exhaustive stratum the non respondant legal units by reweighted. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 79% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | 21%. There is mostly no non-response for biggest companies. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Y- if asked |
| R&D national questionnaire and explanatory notes in the national language: | Y- National Survey on R&D |
| Other relevant documentation of national methodology in English: | Notice that describes the terms in the questionnaire |
| Other relevant documentation of national methodology in the national language: | Publications on Resources for R&D |
18.4. Data validation
Macro-controling for the most influent value in each strata.
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) | Not available | Not available | Not available | Not available |
| 10-49 employees and self-employed persons | Not available | Not available | Not available | Not available |
| 50-249 employees and self-employed persons | Not available | Not available | Not available | Not available |
| 250-and more employees and self-employed persons | Not available | Not available | Not available | Not available |
| TOTAL | Not available | Not available | Not available | Not available |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | Not available | Not available | Not available | Not available |
| Services2) | Not available | Not available | Not available | Not available |
| TOTAL | Not available | Not available | Not available | Not available |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
| Data compilation method - Final data | |
|---|---|
| Data compilation method - Preliminary data | First estimation even if every enterprises have not responded yet. For non respondents : we use the response from the previous year with a raise of the salary that is the same in the entire economy, or use the forecast of R&D expenditures from last year quetionnaire. |
18.5.3. Measurement issues
| Method of derivation of regional data | NUTS3 data is asked in the quetsionnaire, so NUTS1 or NUTS2 data is calculated by agregation |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT excluded |
18.5.4. Weighting and estimation methods
| Weight calculation method | For each stratum, the weight is equal to the ratio of the frame size to the sample size. Treatment of total non-response: Total non-response is corrected by reweighting: the response of responding firms, which already have an initial weight established during the draw, is reweighted. The assumption is that non-responding firms behave in the same way as responding firms. The responses of companies with GERD greater than 600 k€ are not reweighted. For these strata, the response to the survey of year N-1 can be renewed for year N. |
|---|---|
| Data source used for deriving population totals (universe description) | Survey frame and business register. |
| Variables used for weighting | |
| Calibration method and the software used | R |
| Estimation |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
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.
31 October 2025
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
France (continental and overseas territories)
Last period available is reference year 2023, calendar year.
Available every calendar year.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
At Eurostat level and National level, the frequency of R&D data dissemination is yearly for provisional and final data.
Every two years for additional information on R&D personnel.
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.
