1.1. Contact organisation
Ministry of higher education, research and space (France)
1.2. Contact organisation unit
Department of statistical studies on research
SIES - Sub-Directorate for Information Systems and Statistical Studies
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 et de la recherche
SIES - A2.2
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
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
3.1. Data description
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of territorial units for statistics
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Government sector | Compliant with FM2015, though one unit does not perform R&D but heavily finances it |
|---|---|
| Hospitals and clinics | Not included, university hospital centers (CHU) are included in HES |
| Inclusion of units that primarily do not belong to GOV and the borderline cases. |
None. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Compliant. |
|---|---|
| External R&D personnel | Compliant. |
| Clinical trials: compliance with the recommendations in FM §2.61. | Not relevant. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Our survey includes detail about foreign financement, broken down by sectors. |
|---|---|
| Payments to rest of the world by sector - availability | Our survey includes detail about foreign R&D subcontracting / partnership, broken down by sectors. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes. |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | The survey has two main parts for R&D expenditure, one for intramural R&D expenditure, one for extramural R&D expenditure. Explanatory notes given alongside the survey give enough detail to ensure intramural R&D figures do not include extramural R&D expenditure. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Some bordeline activities concurring to R&D such as biologicial analyses or compound building need further explanation sometimes because they can be spontaneously declared as extramural R&D expenditure although they should rather be included in other current expenditures. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
|---|---|
| Source of funds | The sources of funds by R&D branch are collected on the basis of total expenditure (intramural + extramural R&D expenditure). Compliant with FM 4.104 |
| Type of R&D | No |
| Type of costs | Labour costs, other current costs (icld costs for external R&D personnel), capital expenditures (breakdown by lands and buildings, instruments and equipments). |
| Defence R&D - method for obtaining data on R&D expenditure | Extramural expenditures are collected on defence sector from the defence agency in charge of the innovation part through the same survey. There si also a distinction between military and civil extramural expenditures in the survey. |
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 | The R&D personnel 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. Researchers data are broken down only by seniority. Doctoral student are collected. |
| Age | Less than 25, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, more than 65. |
| Citizenship | Not available |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
|---|---|
| Function | The R&D personnel 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. Researchers data are broken down only by seniority. Doctoral student are collected. |
| Age | Not available. |
| Citizenship | Not available. |
3.4.2.3. FTE calculation
Full-time equivalents consist of average figures for the year that take account of departures and arrivals during the year, of the part time contracts and also of the time devoted to research in cases where the activity does not consist solely of R&D.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | The target population is composed of all state-owned companies of commercial and industrial nature, public institutions with a scientific and technical vocation, public administrative institutions located in France (including overseas departments and territories) who perform R&D activity. | |
| Estimation of the target population size | 41 |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | Units must perform intramural R&D and have a governmental status. One unit with governmental status only finances R&D but heavily so and therefore is necessary for the complete calculations of R&D. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Units already included in the census survey which declare intramural R&D, plus public establishments which get research tax credit, units can be added also because they are mentioned in some answers (extramural or financed R&D part). |
| Inclusion of units that primarily do not belong to the frame population | The GOV questionnaire is addressed to 3 big PNP units (Pasteur, Curie, INRS) BUT these 3 units are not included in the GOV sector (they are included in the PNP sector, with all other smaller PNP units, which receive a lighter questionnaire). |
| Systematic exclusion of units from the process of updating the target population | No. |
| Estimation of the frame population | 52 (PNP excluded) |
3.7. Reference area
France.
3.8. Coverage - Time
Not requested, see concept 12.3.3 (Data availability).
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.
Calendar year 2023.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
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: |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law:
- Chapter 6 of the 1st part of the law no. 78-17 of January 6, 1978 on data processing, data files and individual liberties. For Collection, recording and storage of personal information. Loi n° 78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés
- Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance) Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data
- Confidentiality commitments of survey staff: Article L121-6 Of the General Code of Public Service : L'agent public est tenu au secret professionnel dans le respect des articles 226-13 et 226-14 du code pénal.
7.2. Confidentiality - data treatment
Data on government agencies or bodies are not subject to statistical confidentiality.
Nevertheless, statistics concerning less than 5 headcount per body may not be disseminated.
8.1. Release calendar
June N+2 for Eurostat dissemination
Summer N+2 for national dissemination
Final results : July N+2 and November N+2
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
For France : 2025 Calendar of Statistical Publications of SIES
8.3. Release policy - user access
Official calendar
Publications
Press releases
Users are treated according to the national statistical system rules, i.e. all users have access to the information at the same time.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
The frequency of R&D data dissemination at national level is yearly.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | N | Not concerned |
| Ad-hoc releases | Y | press release on the ministry website when the publication is disseminated, article on the social networks of the ministry, free of charge |
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 | Note flash du SIES - La dépense de recherche et développement expérimental en 2023 Note d'information du SIES - Dépenses de recherche et développement expérimental en France - Résultats détaillés pour 2023 et premières estimations pour 2024 (à paraître) |
| Specific paper publication (e.g. sectoral provided to enterprises) | N | L'État de l'Enseignement supérieur, de la Recherche et de l'Innovation en France 2025 |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Not available.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to the micro-data |
Individual data are pusblished in excel files accessible on the ministry website and soon in the open data of the ministry. They are available only for the Public Scientific and Technical Research Establishments (EPST) and Public Establishments of an Industrial and Commercial nature (EPIC). |
|---|---|
| Access cost policy | The access is free |
| Micro-data anonymisation rules | Data from a short list of government agencies or bodies are published individually : Public Scientific and Technical Research Establishments (EPST) and Public Establishments of an Industrial and Commercial nature (EPIC). The rest is published as an aggregate only. Regarding the statistics in headcount, a case shall not be published if the headcount is less than 5. |
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 | Data from a short list of government agencies or bodies are published individually. The rest is published as an aggregate only. | Ministry website : Ministère de l'Enseignement Supérieur, de la Recherche et de l'Espace, Statistiques et analyses |
| Data prepared for individual ad hoc requests | Y | Data from a short list of government agencies or bodies are published individually. The rest is published as an aggregate only. | Specific requests from some government department (inspections, Cour des comptes), researchers and Insee |
| Other | N | Not concerned | Not concerned |
1) Y – Yes, N - No
10.6. Documentation on methodology
We don't have an official methodology file, but we have internal documents describing the statistical processes. We publish the notices that explain the concepts used in the survey. They are available on the survey page on the Ministry website Enquête R&D auprès des administrations - Collecte - Application DoRAd.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Explanatory notes included in each document. Either graphics notes or notes defining the main regrouping sectors, describing the domain covered and the statistical breaks. |
|---|---|
| Requests on further clarification, most problematic issues | Not on gov specifically, but sometimes further explanations requested on the exact content behind the matrix of fluxes between BES, GOV-HES-PNP and Foreign sector. |
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 National level, we apply the French Public Statistical System quality guidelines regarding data editing and imputation process (La politique et la stratégie qualité du SSP).Some consistencies check are done on the web platform. A personnel assistance is given to the respondents if needed. In addition, throughout the investigation period, completed questionnaires are reviewed with complete consistencies check to detect any additional problems and questions are sent to the concerned establishment in order to explain or correct the figures.
11.2. Quality management - assessment
We surveyed 55 establishments in 2023. The response rate (81% on 2023) is increased by follow-up calls and e-mails. We also check the internal and time consistency of the responses received and send detailed data editing reports to the establishments requiring them to answer either with an explanation or a correction. If needed (method or perimeter change) we require them to send the n-1 survey with figures in line with the n survey. Furthermore we are particularly careful if the individual answer has a significant weight in the disseminated aggregates.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | Eurostat; OCDE; FAO; MESR; Cour des comptes; inspections générales de l'administration, des finances ou de l'éducation nationale | Metadata; aggregates; Micro-data |
| 3 | Media | Published statistics |
| 4 | Researchers and students | Micro-data, aggregates. |
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 specific for R&D statistics | No |
| Short description of the feedback received | Not available |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
100%. No missing cells in the data delivered to Eurostat.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | 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-1992 | Yearly | We have no missing data. We have global figures from 1992, but more detailed data only from 2002, and figures accessible on our website from 2015 | No changes | Not applicable | Not applicable |
| Type of R&D | Y-1992 | Yearly | We have no missing data. We have global figures from 1992, but more detailed data only from 2002, and figures accessible on our website from 2015 | No changes | Not applicable | Not applicable |
| Type of costs | Y-1992 | Yearly | We have no missing data. We have global figures from 1992, but more detailed data only from 2002, and figures accessible on our website from 2015 | No changes | Not applicable | Not applicable |
| Socioeconomic objective | N | Not applicable (see GBARD for that) | Not applicable | Not applicable | Not applicable | Not applicable |
| Region | Y-1992 | Yearly | We have no missing data. We have global figures from 1992, but more detailed data only from 2002, and figures accessible on our website from 2015 | No changes | Not applicable | Not applicable |
| FORD | Y-1992 | Yearly | We have no missing data. We have global figures from 1992, but more detailed data only from 2002, and figures accessible on our website from 2015 | No changes | Not applicable | Not applicable |
| Type of institution | Y-1992 | Yearly | We have no missing data. We have global figures from 1992, but more detailed data only from 2002, and figures accessible on our website from 2015 | No changes | Not applicable | Not applicable |
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-1992 | Yearly | No gap year | No changes | Not applicable | Not applicable |
| Function | Y-1992 | Yearly | No gap year | No changes | Not applicable | Not applicable |
| Qualification | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Age | Y-1992 | Yearly | No gap year | No changes | Not applicable | Not applicable |
| Citizenship | Y-1992 | Yearly | No gap year | No changes | Not applicable | Not applicable |
| Region | N | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| FORD | Y-1992 | Yearly | No gap year | No changes | Not applicable | Not applicable |
| Type of institution | Y-1992 | Yearly | No gap year | No changes | Not applicable | Not applicable |
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 | 2020-2010 | Yearly | Before 2010 and after 2020 | No changes | No changes | No changes |
| Function | Y-1992 | Yearly | No missing data | No changes | No changes | No changes |
| Qualification | N | N | N | N | No changes | No changes |
| Age | N | N | N | N | No changes | No changes |
| Citizenship | N | N | N | N | No changes | No changes |
| Region | Y-1992 | Yearly | No missing data | No changes | No changes | No changes |
| FORD | N | N | N | N | No changes | No changes |
| Type of institution | Y-1992 | Yearly | No missing data | No changes | No changes | No changes |
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 |
|---|---|---|---|---|---|
| Extra-mural R&D expenditure | Y-1992 | Yearly | By R&D perfomer sector (government, association, enterprises, higher education facilities,foreign) | Breakdown by sector (enterprises, foreign, association, HES, Government) the expenditures were used in. | Statistical unit |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not available | Not available | Not available |
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 | Not applicable | 5 | 1 | 4 | 2 | 3 | +/- |
| Total R&D personnel in FTE | Not applicable | 5 | 1 | 4 | 2 | 3 | +/- |
| Researchers in FTE | Not applicable | 5 | 1 | 4 | 2 | 3 | +/- |
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. 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
Doesn't apply because we conduct a census survey.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not applicable. |
| Government | Not applicable. |
| Higher education | Not applicable. |
| Private non-profit | Not applicable. |
| Rest of the world | Not applicable. |
| Total | Not applicable. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not applicable. |
| Technicians | Not applicable. | |
| other support staff | Not applicable. | |
| Qualification | ISCED 8 | Not applicable. |
| ISCED 5-7 | Not applicable. | |
| ISCED 4 and below | Not applicable. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors : We know some small gov units are missing, but their weight is negligible when compared to the main gov R&D actors.
- Measures taken to reduce their effect: We have tried to include some smal units performing R&D under the responsbility of ministries other than the Ministry of higher education, research and space. With no great luck so far.
- Share of PNP (if PNP is included in GOV): PNP have the same survey as the GOV ones but they are not included in the GOV results.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
- Description/assessment of measurement errors: Miscomprehension of the concept sometimes, change in perimeter over time, change in the SI RH on which the answer is based.
- Measures taken to reduce their effect: Consistencies check and feedback from the establishments allow us to correct the value.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 42 | 52 | 19 % |
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) (%) | 19 % | 23 % | 23 % |
| Comments | The non respondants have lower weight. | The non respondants have lower weight. | The non respondants have lower weight. |
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 | An online questionnaire |
|---|---|
| Estimates of data entry errors | DIRDA : 17 % ; FTE : 17 % (percentage of respondants who gave a figure later corrected) |
| Variables for which coding was performed | No coding was performed |
| Estimates of coding errors | No coding was performed |
| Editing process and method | During the data collection and cleaning, data editing programs including internal and time consistencies check and thorough human examination allow us to send detailed control reports to the units which are asked to explain or correct the figures. |
| Procedure used to correct errors | Re-contact the respondents for clarifications and corrections if we detect presumable errors or inconsistencies. Then we correct the figures to be changed for the unit's survey on the online collecting platform, both for year N and N-1 if needed. |
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: Provisional FTE data are released only for Eurostat, only total GOV+HES+PNP DIRD 2023 was published on 10 December 2024
- Lag (days): 345
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 30 July 2025
- Lag (days): 577
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
The National Centre for Scientific Research (CNRS) is included in the higher education sector, although in some countries, such as Italy, this type of organisation is classified in the government sector; this affects the distribution of R&D effort by sector of performance. The National Centre for Scientific Research (CNRS) is included in the GOV survey and treated alongside the other government units.
In the national french statistics CNRS is included in GOV. It is counted in the Eurostat GOV response rate and in the error measurement rates as well.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197, Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | No comments |
| Researcher | FM2015, § 5.35-5.39. | No deviation | No comments |
| 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 | No comments |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | No comments |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No deviation | No comments |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | Small deviation. | We don't fully apply 8.22 since university hospitals are mostly funded by the state but we include them in HES because the doctors are teachers-researchers. Is is the same logic as for CNRS. Non university hospital are not included in the GOV census. |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | Small deviation | The classification in the survey has one item which is not in FORD : "R&D managment : managing and overseeing R&D activities". Of course we pay attention this particular item is chosen only when senior researchers really oversee a variety of FORD domain. |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | Not concerned | See GBARD |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | No comments |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | Census survey |
| Survey questionnaire / data collection form | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | Online questionnaire and the responses are hosted in a database. |
| Cooperation with respondents | FM2015 Chapter (mainly sub-chapter 8.5). | No deviation | Statistical units receive login details for the survey by ordinary mail so they can use the web questionnaire. E-mail addresses and telephone numbers of persons responsible at SIES are provided for assistance. Online explanatory notices provide definitions. Respondents are offered at extra time if they cannot provide the data within the set deadline provided they contact us and explain why.They can call us if they have questions or problem. We do a follow-up to remind them the deadline and call the back if there is something wrong or not clear with their answers. |
| Data processing methods | FM2015 Chapter 8 (mainly sub-chapters 8.5-8.6). | No deviation | We use data editing programs with internal and time consistency checks. We analyse each survey answer thoroughly and call back for explanations and if need be corrections. We use external sources like annual reports. When it is more effective we have visio meeting to clarify some points. |
| Treatment of non-response | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | For this population we have very little missing values at the end. For the few remaining we impute the value of the previous survey, if not available, for each non-respondent, we use auxiliary information in annual reports, and if it is not possible we affect a group of units who it looks the most like based on the information we have and we affect the mean of the group answer to the non-respondent missing values (GERD by type basic research/applied research/experimental development for example). |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.7). | Not applicable, we conduct a census. | No comments |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | No comments |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1) | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | From 1978 | 2023, 2001, 1998, 1997, 1992 | 2023: methodological improvements and expansion of the scope covered 2001:The data communicated by the Ministry of Defense now cover research that was not considered as R&D in earlier years. |
| Function | From 1978 | ||
| Qualification | Not concerned | ||
| R&D personnel (FTE) | From 1978 | 2023, 2001, 1998, 1997, 1992 | 2023: methodological improvements and expansion of the scope covered 2001:The data communicated by the Ministry of Defense now cover research that was not considered as R&D in earlier years. |
| Function | From 1978 | ||
| Qualification | Not concerned | ||
| R&D expenditure | From 1978 | 2023, 2010, 2001, 1997, 1992, 1981 | 2023: methodological improvements and expansion of the scope covered 2010: The methode to compute the GOVERD was changed in order to separate the financing activity from the GOVERD. This led to a shift of minus 1 billion € for GOVERD. 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. 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 |
| Source of funds | From 1978 | 1992 | Account has been taken of the repayment of reimbursable aid in the distribution of R&D expenditure by source of funding. |
| Type of costs | From 1978 | ||
| Type of R&D | From 1978 | ||
| Other | No |
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
Data are produced in the same way in the odd and even 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.
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.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers(in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 7 045 148,6 | 51 835 | 31 753 |
| Final data (delivered T+18) | 7 138 935,9 | 51 854 | 31 868 |
| Difference (of final data) | + 93 787,3 (1,9 %) | +19 (0,03 %) | + 115 (0,4 %) |
Comments: The difference is small because due to a change in survey calendar (earlier than the years before), in october 2024 we had already a very good response rate so we could mostly use answers of the survey on 2023.
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanations of consistency issues, if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 77 287 € | No comment |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available | No comment |
(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 | 145 277 | Not available |
| Data collection costs | Not available | |
| Other costs | 67 685 | Not available |
| Total costs | 212 962 | Not available |
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 42 | Sum of all R&D-performing government institutions that responded entirely to the survey. |
| Average Time required to complete the questionnaire in hours (T)1) | 17 | Mean of the time spent reported by the respondents. |
| Average hourly cost (in national currency) of a respondent (C) | Not available | Not available |
| Total cost | Not available | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
All data are collected through a census survey.
18.1.2. Sample/census survey information
| Sampling unit | Not applicable |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | 52 |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | Usual units in the survey plus the new gov units (by fusion or creation) plus units who did not performe R&D earlier minus the disappeared ones |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Very good |
| Variables the survey contributes to | All the variables requested by the European regulation |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | DgFip ; Insee |
|---|---|
| Description of collected data / statistics | Individual data on the Research Tax Credit ; Extract from Insee Firms Register SIRUS, CIS survey ==> both in order to select the new units in the survey |
| Reference period, in relation to the variables the administrative source contributes to | 2021 - 2023 for RTC |
| Variables the administrative source contributes to | Not applicable |
18.2. Frequency of data collection
Annual collection
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Individual staff members of the units. Usually the finance directors, research managers, Human Resources department. |
|---|---|
| Description of collected information | We collect information on the nature and use of intramural and extramural R&D expenditures, the regions where they are used, the resources and their origins. We also collect information on the R&D staff and the administrative personnel who support the R&D (HC and FTE). For the personnal, we collect information on their age, gender, their function, the type of contract they are on, who pay them, their work place. |
| Data collection method | All the units receive an email to inform them about the survey, the deadlines and the link to the online questionnaire with their identifiers. We have access to their questionnaire whether it is completed or not. That means, we can use partially completed questionnaires. |
| Time-use surveys for the calculation of R&D coefficients | Not asked. We ask for FTE. |
| Realised sample size (per stratum) | No sample, it is census. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Online survey. The units have access to the questionnaire and just have to fill it. |
| Incentives used for increasing response | Follow-up and calls and explanation of the use of the data collected and as a last ressort, a letter of the sub-director for Information Systems and Statistical Studies |
| Follow-up of non-respondents | By email, by phone call for the most influent ones. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 81 % |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | We do minor imputation for the non-respondents based on their previous year answer and/or auxiliary information in annual reports and/or the units they look the most like. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | Enquête R&D auprès des administrations - Collecte - Application DoRAd |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | Not available |
18.4. Data validation
Emails and phone follow-up to increase the response rate, consistency checks with the last survey answers and overall consistcency of the answers (personnel expenditure and FTE for example).
Use of the GBARD survey and annual reports to complete the consistency checks.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
| Data compilation method - Final data | Annual survey |
|---|---|
| Data compilation method - Preliminary data | Mix of Annual survey 2023 and forecasts asked in the Annual survey 2022, depending on the quality of the most recent answer. |
18.5.3. Measurement issues
| Method of derivation of regional data | The interviewed units are asked to give the information (expenditures and personnal FTE) on the regions where they do R&D. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | If not given by the respondents, we rely on the answer given at the previous survey; or the share observed in other units of the same type. So, the coeffcient is not fixed and depends on the type of units. Nethertheless some units mention they use such coefficients to answer. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT are excluded from R&D expenditures. |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not applicable |
|---|---|
| Description of the estimation method | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
31 October 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
France.
Calendar year 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:
- 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.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
The frequency of R&D data dissemination at national level is yearly.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
