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, de la recherche et de l'espace
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
Not required.
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 Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit 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.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and 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) 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 distribution of principal economic activity and by product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- 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 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
| Private non-profit sector | Compliant with FM2015, non-profit associations, foundations and public interest group |
|---|---|
| Inclusion of units that primarily do not belong to PNP and the borderline cases | A few units belonging to the education sector but with pnp status. |
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 Frascati Manual §2.61. | Compliant |
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.The following categories for foreign receipts can be distinguished :
|
|---|---|
| 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 | For PNP, the sources of funds by R&D branch are collected on the basis of total expenditure (intramural + extramural R&D expenditure). Compliant with FM (see FM §4.104-4.108, Table 4.3.). |
| Type of R&D | No divergence from FM (FM section 2.5). |
| Type of costs | Labour costs, other current costs (included 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 only for non-defence sector except for the three main PNP. No specific information is collected for intramural expenditures. |
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 PNP 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 | A census survey is used for collection of raw data. Target population consists of all PNP with NACE 72 and legal categories 92 Associations, 93 Foundations and 7410 Public Interest Groups in the business register (which includes PNP), expanded with PNP eligible to R&D tax credit, and with the population from the previous year minus those having stated they do not perform R&D. | Does not apply. |
| Estimation of the target population size | 539 (dont 3 enquêtés avec les organismes nationaux de recherche) | Does not apply. |
3.7. Reference area
France, including overseas departments and territories.
3.8. Coverage - Time
Not requested. See concept 12.3.2. (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.
Reference 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 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. 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.
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 Cnis - Enquête sur les moyens consacrés à la recherche et au développement expérimental (R&D) dans les associations et les GIP - 2025A703RE |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | No |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
a) Confidentiality protection required by law:
National legal law that guarantee the protection of statistical confidentiality : Loi n° 51-711 du 7 juin 1951 sur l'obligation, la coordination et le secret en matière de statistiques. - Légifrance
The information collected as part of the survey is protected by statistical confidentiality and intended for the Sub-Directorate for Information Systems and Statistical Studies (Sies) of the Ministry of Higher Education, Research and Space. At all times, their use and access are strictly controlled and limited to the development of statistics or to scientific or historical research works.
According to national law, data may only be published in a way that no conclusions on individual units can be drawn. Data for aggregates where less than 3 units contribute to the figures are not published. Data for aggregates where 1 unit contributes to more than 85% to the figures are not published.
b) 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.
- Every individual staff member is obliged by internal rules and by the European Statistics Code of Practice to a strict confidential treatment of information. 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), cells containing information from less than 3 establishments or 1 establishment contributing to more than 85% cannot be disclosed. In order to prevent identifcation of these cells by simple substractions from total, at least one additional category must be suppressed.
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 |
| 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 | Micro-data access for researchers will be possible in the future through the CASD, with legal constraints. For now the individual data are not shared. |
|---|---|
| Access cost policy | Not concerned |
| Micro-data anonymisation rules | Not concerned |
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 : Les publications statistiques sur l'enseignement supérieur, la recherche et l'innovation |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | Specific requests from government bodies (inspections, Cour des comptes), statistical teams from other ministries and Insee |
| Other | N | Not concerned | Not concerned |
1) Y – Yes, N - No
10.6. Documentation on methodology
Enquête R&D auprès des administrations
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 linked to the questionnaire. |
|---|---|
| Requests on further clarification, most problematic issues | Not available. |
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 3 to 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.
In order to increase the response rate we do follow-up calls and e-mails. We try to maximise the response rate in each reweighting stratum. We also check the consistency of the responses received and call back the respondent if something is wrong or not clear. We offer assistance by videoconferences if needed.
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 |
| 3 | Medias | Published statistics |
| 4 | Researchers and students | 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. Data availability
Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D):
12.3.2.1. Incorporation of PNP sector in another sector
| Incorporation of PNP in another sector | No |
|---|---|
| Reasons for not producing separate R&D statistics for the PNP sector | Not relevant. |
| Share of PNP expenditure in the total expenditure of the other sector | Not relevant. |
| Share of PNP R&D Personnel in the respective figure of the other sector | Not relevant. |
12.3.2.2. Non-collection of R&D data for the PNP sector
| Reasons for not compiling R&D statistics for the PNP sector | Not relevant. Statistics are produced. |
|---|---|
| PNP R&D expenditure/ GERD*100) | 4.8 |
| Share of PNP R&D Personnel in the respective figure of the total national economy | 0.0 |
12.3.2.3. Data availability on more detail level
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons |
|---|---|---|---|---|---|---|
| Source of funds | 2010- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
| Type of R&D | 2010- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
| Type of costs | 2010- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
| Socioeconomic objective | N | Not applicable | Not applicable | |||
| Region | N | Not applicable | Not applicable | |||
| FORD | 2019- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
| Type of institution | 2010- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
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
12.3.2.4. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons |
|---|---|---|---|---|---|---|
| Sex | 2015- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
| Function | 2015- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
| Qualification | N | Not applicable | ||||
| Employment status | 2015- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
| Age | N | Not applicable | ||||
| Citizenship | N | Not applicable | ||||
| Region | N | Not applicable | ||||
| FORD | 2019- | Yearly | N | Methodological changes on sampling / statistical adjustment | 2020-2022 | Improving the results |
| Type of institution | N |
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.
13.2. 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
Confidence interval for Total R&D expenditure: Does not apply, census survey
Confidence interval for Total R&D personnel (FTE): Does not apply, census survey
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.
- Extent of non-sampling errors:
- Measures taken to reduce the extent of non-sampling errors:
- Methods used in order to correct/adjust for such errors:
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: It is rather difficult to identify the PNPs who perform R&D. We use four criteria in an attempt to detect them at best : having answered they perform R&D the year before, having asked for tax credit aimed at encouraging R&D, being classified in a NACE R&D, being cited as partners by GOV R&D performers.
- Measures taken to reduce their effect: We have a filter question :
Do you perform R&D in 2023 ?
Did you perform R&D during the three previous years ?
Will you perform R&D starting from 2024 ?
We use the answers to better delineate the target population. On top of that we coordinate ourselves with the team that conducts the survey adressed to the firms.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Not requested.
13.3.3. Non response error
Not requested.
13.3.3.1. Unit non-response - rate
Not requested.
13.3.3.2. Item non-response - rate
Not requested.
13.3.4. Processing error
Not requested.
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
Compliant with FM.
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) paragraphs and the EBS Methodological Manual on R&D Statistics 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 |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | 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 |
| Intramural R&D expenditure | FM2015,Chapter 4 (mainly sub-chapter 4.2). | No deviation | No comments |
| Statistical unit | FM2015, § 10.40-10.42 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Target population | FM2015, § 10.40-10.42 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Sector coverage | FM2015, § 10.2-10.8 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Reference period for the main data | Reg. 2020/1197: Annex 1, Table 18 | No deviation | No comments |
| Reference period for all data | 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, 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 10 (mainly sub-chapter 10.6). | No deviation | No comments |
| Survey questionnaire / data collection form | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | No comments |
| Cooperation with respondents | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | No comments |
| Data processing methods | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | No comments |
| Treatment of non-response | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | 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 | 2022, 2020, 1997, 1992 | 2022 : Change in the adjustment part 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). |
| Function | From 1978 | 2022, 2020, 1997, 1992 | 2022 : Change in the adjustment part 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). |
| Qualification | Not concerned, we don't collect data on qualification. | ||
| R&D personnel (FTE) | From 1978 | 2022, 2020, 1997, 1992 | 2022 : Change in the adjustment part 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). |
| Function | From 1978 | 2022, 2020, 1997, 1992 | 2022 : Change in the adjustment part 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). |
| Qualification | Not concerned, we don't collect data on qualification | ||
| R&D expenditure | From 1978 | 2022, 2020, 1997, 1992, 1981 | 2022 : Change in the adjustment part 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). 1992:The survey method for the private non-profit sector changed. 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%; |
| Source of funds | From 1978 | 2022, 2020, 1992 | 2022 : Change in the adjustment part 2020 : Methodological innovations on the sampled part. 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 | 2022, 2020 | 2022 : Change in the adjustment part 2020 : Methodological innovations on the sampled part. |
| Type of R&D | From 1978 | 2022, 2020 | 2022 : Change in the adjustment part 2020 : Methodological innovations on the sampled part. |
| 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
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
Yes it is.
15.3. Coherence - cross domain
See below.
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 PNP R&D expenditure (in 1000 of national currency) | Total PNP R&D personnel (in FTEs) | Total number of PNP researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 1 223 484,2 | 10 645,0 | 6 601,0 |
| Final data (delivered T+18) | 1 001 050,0 | 9 699,0 | 5 853,0 |
| Difference (of final data) | -222 434,2 | -946,0 | -748,0 |
Comments:
The adjustment method has been improved in 2023.
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) | 61 084 € | 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 | 93 962 |
Not available |
| Data collection costs | Not available | |
| Other costs | 25 299 | Not available |
| Total costs | 119 261 | 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) | 196 | Sum of all R&D-performing government institutions that responded entirely or partially to the survey. |
| Average Time required to complete the questionnaire in hours (T)1) | 4 | 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 | 539 |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | We mix four sources : NACE 72, tax credit for research, previous year respondents, partners of GOV respondants |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Reasonably 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 and SIREN ==> both in order to select the units in the survey |
| Reference period, in relation to the variables the administrative source contributes to | 2021 - 2023 for RTC ; 2024 for SIREN API |
| Variables the administrative source contributes to | Selection in the survey frame and addresses |
18.2. Frequency of data collection
See 12.3.2.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Individual staff members of the units. Usually the ones dealing with the finance and HR parts or firms which helped the units with their tax credit for research files. |
|---|---|
| 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. |
| 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) | 37% |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | We use the filter question "Did you performed R&D in year N" to apply coefficients in order to estimate the non-response part and we do imputation for the non-respondents based on their previous year answer if available. Then we increase the weight of the respondants to make them represent the non-respondants, by origin of selection in the survey frame. |
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 |
| 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 consistency of the answers (personnel expenditure and FTE for example).
Final results are discussed with our colleagues responsible for HESSI and the sub-director for Information Systems and Statistical Studies.
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)
% of units (not weighted) : 9 % (Denominator = total number of units in the frame minus those which should not have been in the frame = 539 - 40, that is to say 499)
% of GERD : 8%
% of R&D personnel FTE : 11 %
18.5.2. Data compilation methods
| Data compilation method - Final data | Annual survey |
|---|---|
| Data compilation method - Preliminary data | Forecasts asked in the Annual survey 2022, imputation and adjustment included. |
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 coefficient 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 | Every respondent unit has a weight equal to one since it is a census. |
|---|---|
| Description of the estimation method | We first do imputation for non-response by imputing the previous survey's response if available. Then we calculate a share by criteria of selection into the population of the units who really perform research. Theses calculations give coefficients applied to the summary results of the population of respondents. The criteria are :
|
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit 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.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and 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) 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, including overseas departments and territories.
Reference 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.
