1.1. Contact organisation
Ministry of Higher Education, Research and Space (MESRE in French)
1.2. Contact organisation unit
SIES - Sub-Directorate for Information Systems and Statistical Studies
Department of statistical studies on research
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Ministère de l’enseignement supérieur, de la recherche et de l'espace
DGESIP/DGRI – SIES – Département des études statistiques de la recherche
1 rue Descartes, 75231 Paris Cedex 05
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
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 higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
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 unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011.
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise the 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
| Tertiary education institution | Public and private educational establishments either attached to a ministry or not. |
|---|---|
| University and colleges: core of the sector | Include universities and colleges attached or not to a ministry. |
| University hospitals and clinics | Include university hospitals and cancer research centers. |
| Inclusion of units that primarily do not belong to HES and the borderline cases |
|
3.3.3. R&D variable coverage
| R&D administration and other support activities | There is no deviation from the Frascati Manual (FM) 2015. |
|---|---|
| External R&D personnel | Doctoral students who have received a grant to prepare a thesis (thesis paid by the Ministry of Education or other ministries, the university or other organizations) are considered as external personnels. We also consider as external personnel, whoever do or contribute to R&D but is allocated/paid by another unit. |
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | Clinical trials are mainly carried out in university hospitals and cancer research centers. The company concerned must supply the drugs to the researchers, and therefore to the hospital, free of charge. This results in a "physical" flow, but not a financial one. The cost of the drugs is therefore included in the pharmaceutical industry's intramural R&D expenditure, leading to an increase in the share of other current R&D costs, even though the place where the R&D is carried out is not in the pharmaceutical industry but mainly in hospitals. In addition, although companies are billed for the additional direct costs of trials, they are not billed for time spent or the use of technical facilities. The cost of clinical trials to the pharmaceutical industry corresponds to a marginal cost rather than a full cost, which may explain the relatively low amounts observed. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Receipts from :
|
|---|---|
| Payments to rest of the world by sector - availability | Payments to:
|
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 | We consider subcontracting and collaboration in R&D tasks as external R&D expenditure. These are non-taxable expenses relating to complete or partial R&D programs carried out by a third party on behalf of an establishment, excluding orders for supplies or simple services linked to R&D work carried out by this establishment and included in domestic expenses. |
| Difficulties to distinguish intramural from extramural R&D expenditure | No difficulties. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
|---|---|
| Source of funds | In addition to the sources of funds presented in the FM 2015, in our annual surveys, we ask universities and private higher education establishments to provide information on public grants included in the State budget or provided by local authorities. |
| Type of R&D | We are using the 3 types of R&D proposed by the FM 2015. |
| Type of costs | We ask for current R&D expenditure excluding depreciation (personnel (including social security charges and taxes) and operating expenditure), R&D capital expenditure before depreciation (R&D-specific equipment, R&D-specific property transactions) and depreciation of R&D capital expenditure in the year of interest. |
| Defence R&D - method for obtaining data on R&D expenditure | We interview higher education schools performing R&D attached to the Ministry in charge of the defence. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons employed and still working the 31st December of the reference year |
|---|---|
| Function | We have the basic breakdown by researcher/support personnel. However, we also ask a detailled breakdown by type of researcher (researchers, R&D engineers, Phd students) and type of support personnel (technicians, administrative personnel). |
| Qualification | Not available. |
| Age | Less than 25, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-62,63-64,65-67 more than 67. |
| Citizenship | France, Other European Union countries (EU 28), Other European countries, North America, South and Central America, Asia, Africa. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Total number of persons employed prorated to time spent doing or supporting R&D during the calendar year |
|---|---|
| Function | We have the basic breakdown by researcher/support personnel. However, we also ask a detailled breakdown by type of researcher (researchers, R&D engineers, Phd students) and type of support personnel (technicians, administrative personnel). |
| Qualification | Not available. |
| Age | Not available. |
| Citizenship | Not available. |
3.4.2.3. FTE calculation
This involves counting all personnel directly assigned to R&D, as well as those providing services associated with R&D work, such as management, administrative and service staff. These numbers include all paid staff. Full-time equivalent research (FTE) is calculated pro rata to the time devoted to R&D activities. For example, 4 full-time employees devoting 50% of their working time to R&D for 3 months : 4 x 0.50 x 3/12 -> 0.5 FTE.
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 HES Sector should consist of all R&D performing institutional 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 universities, colleges, other higher education establishements and healthcare facilities located in France (including overseas departments and territories) who perform R&D activity. | We don't use another data source. |
| Estimation of the target population size | Approximatively 330 statistical units. | We don't use another data source. |
3.7. Reference area
France and its overseas departments and territories.
3.8. Coverage - Time
Calendar year 2023.
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2020. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR); 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) and in head counts (HC).
- For R&D personnel (HC): 31 December, 2023
- For R&D personnel (FTE) and expenditures as well as ressources: 2023 calendar year
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. 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: | Our survey is not compulsory, so the unit could refuse to respond. |
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: 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
- According to national law, for private schools, 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.
- Confidentiality commitments of survey staff: 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
Data on private higher education establishments are subject to statistical confidentiality. 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.
Data on public higher education establishments are not subject to statistical confidentiality.
8.1. Release calendar
Final results : July N+2 and November N+2 (more detailed results)
8.2. Release calendar access
- For Eurostat this is: Release calendar - Eurostat (europa.eu).
- For national release this is : Calendrier 2025 des publications statistiques du SIES | enseignementsup-recherche.gouv.fr.
8.3. Release policy - user access
Data release are publicly and freely available on the website of the Ministry. All users have access to the information at the same time.
The frequency of dissemination is yearly.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Y | La dépense de recherche et développement expérimental en 2023 | enseignementsup-recherche.gouv.fr |
| Ad-hoc releases | Y | L'État de l'Enseignement supérieur, de la Recherche et de l'Innovation en France 2025 | enseignementsup-recherche.gouv.fr |
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 | La dépense de recherche et développement expérimental en 2023 | enseignementsup-recherche.gouv.fr |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y | L'État de l'Enseignement supérieur, de la Recherche et de l'Innovation en France 2025 | enseignementsup-recherche.gouv.fr |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Not yet available. It will be soon.
10.3.1. Data tables - consultations
Not yet available. It will be soon.
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 | Apart for universities under the exclusive authority of the MESR, the individual data are not shared. |
|---|---|
| Access cost policy | The access to micro-data published (universities under the exclusive authority of the MESR) is free. |
| Micro-data anonymisation rules | Apart from universities under the exclusive authority of the MESR, no establishment name is revealed. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not available.
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 | Aggregated figures | Ministry website : Statistiques et analyses | enseignementsup-recherche.gouv.fr |
| Data prepared for individual ad hoc requests | Y | Aggregated figures | Specific requests from some government department (inspections, Cour des comptes), researchers and Insee |
| Other | N | Not applicable. | Not applicable. |
1) Y – Yes, N - No
10.6. Documentation on methodology
Enquête R&D auprès des administrations | enseignementsup-recherche.gouv.fr
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 |
|---|---|
| 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) iscomposed of the European Statistics Code of Practice, the Quality Assurance Framework of theESS, and the general quality management principles (such as continuous interaction with users,continuous improvement, integration, and harmonisation).
At National level, the exhaustivity of surveyed establishement is checked using the national directory of research structures (Répertoire national des structures de recherche). 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 frequently reviewed to detect any additional problems and a feedback is sent to the concerned establishment.
11.2. Quality management - assessment
We survey approximately 330 higher education and research establishments each year (CNRS included). In 2023 the response rate is 89%. It is increased by follow-up calls and e-mails. We also check the consistency of the responses received and call back the respondent if something is wrong or not clear.
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, statistics |
1) Users' class codification
1- Institutions:
- European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
- in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
- International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes.)
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | We don't have a satifcation survey. However, when discussing with the respondent, we do ask them a quick feedback. There is also comment section in the questionnaire where the respondent can give us feedback. |
|---|---|
| User satisfaction survey specific for R&D statistics | Not available. |
| 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. 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 | 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 | Y-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 | : | 5 | 1 | 4 | 2 | 3 | +/- |
| Total R&D personnel in FTE | : | 5 | 1 | 4 | 2 | 3 | + |
| Researchers in FTE | : | 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 be 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
Not applicable.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not applicable. |
| Government | Not applicable. |
| Higher education | Not applicable. |
| Private non-profit | Not applicable. |
| Rest of the world | Not applicable. |
| Total | Not applicable. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not applicable. |
| Technicians | Not applicable. | |
| Other support staff | Not applicable. | |
| Qualification | ISCED 8 | Not applicable. |
| ISCED 5-7 | Not applicable. | |
| ISCED 4 and below | Not applicable. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors: Data are collected through a census survey. The percentage of units not covered is very low.
b) Measures taken to reduce their effect: Two years ago, we discovered that some higher education establishment performing R&D were missing. Now it is fixed.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors: Respondents often revise their answers for previous years. Mesurement errors are due to misunderstanding of concepts, inappropriate information system or staff turnover (the questionnaire is not always filled by the same person). We usually detect such problem by comparing answers evolution over year.
b) Measures taken to reduce their effect: The respondents have the option to send us an email if they have any questions. We carry out micro and macro checks. Evolutions over years are particularly monitored. Respondents must provide explanations if variations are too high or too low. Checks are performed a short time after receiving the filled questionnaire.
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) |
|---|---|---|
| 294 | 332 | 11% |
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) (%) | 17 | 20 | 20 |
| Comments |
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 | A web questionnaire |
|---|---|
| Estimates of data entry errors | We don't have a measurement of percentage of errors recorded. |
| Variables for which coding was performed | No coding was performed |
| Estimates of coding errors | Not applicable |
| Editing process and method | During the data collection and cleaning, if there is an error (wrong unit for example), the person in charge of the survey can correct the wrong value directly on the online questionnaire of the respondent. |
| Procedure used to correct errors | We contact the respondents for clarifications if we detect errors or inconsistencies. |
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: 2023
- Date of first release of national data: the 10th of December 2024
- Lag (days): 345
14.1.2. Time lag - final result
- End of reference period: 2023
- Date of first release of national data: the 30th of July 2025
- Lag (days): 575
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
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.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | 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 Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | Some establishments have difficulties to compute the FTE. So, they usually give us an estimation using ratios. | 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 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | No comments |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | No comments |
| 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 9 (mainly sub-chapter 9.5). | No deviation | Census survey |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Online questionnaire and the responses are hosted in a database. |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | 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 witg their answers. |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviation | We collect data on external funds and verify the consitency of the responses from the sender and the receiver institution. |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviation | As recommenced by FM 2015, we collect data on GUF and don't include in the intern funds. |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | After data collection and follow-up to correct some errors, we clean the data and do imputation for the non respondents (see the following row for more details). |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Values are imputed using the following methods in this order : |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | Not applicable, we conduct a census. | No comment |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | Not applicablewe, conduct a census. | No comment |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | Not applicable, we conduct a census. | No comment |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | Not applicable, we only provide final data | No comment |
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) | 2001-2008; 2009-2013; 2014; 2015; 2016-2019; 2020-2021; 2022; 2023 | 2009; 2014; 2015; 2020; 2022; 2023 | 2009: exclusion of the defense sector. 2014: better accounting for R&D personnel at university hospitals and university hospitals with teaching facilities. 2015: university R&D staff numbers are measured directly from the R&D survey. 2020: addition of non-RCE(with expanded responsibilities and competencies) staff at universities. 2022: methodological improvement in the treatment of missing values and expansion of the scope covered. 2023: methodological improvements and expansion of the scope covered |
| Function | 2001-2023 | No breaks | No comments |
| Qualification | Not concern, we don't collect data on qualification | Not concerned, we don't collect data on qualification | No comments |
| R&D personnel (FTE) | 2001-2008; 2009-2013; 2014; 2015; 2016-2019; 2020-2021; 2022; 2023 | 2009; 2014; 2015; 2020; 2022; 2023 | 2009: exclusion of the defense sector, for 6,100 FTEs. 2014: better accounting for R&D personnel at university hospitals and university hospitals with teaching facilities. 2015: university R&D staff numbers are measured directly from the R&D survey. 2020: addition of non-RCE(with expanded responsibilities and competencies) staff at universities. 2022: methodological improvement in the treatment of missing values and expansion of the scope covered. 2023: methodological improvements and expansion of the scope covered. |
| R&D expenditure | 2001-2003; 2004-2008; 2009-2013; 2014; 2015; 2016-2019; 2020-2021; 2022; 2023 | 2004; 2009; 2014; 2015; 2022; 2023 | 2004: In 2007, a new methodology was introduced to correct for some double-counting in source of funds for universities, and the Higher Education R&D expenditure data revised for 2004., 2009: the cost of research and development work by the Ministry of Defence in connection with the FOST (Strategic Ocean Force), which previously was not included under R&D; 2014: better consideration given to university hospital staff conducting R&D work within these institutions (+8,500 FTEs compared to 2013). As a result, R&D expenditure increased. 2023: methodological improvements and expansion of the scope covered. |
| Source of funds | 2001-2003; 2004-2008; 2009-2013; 2014; 2015; 2016-2019; 2020-2021; 2022; 2023 | 2004; 2009; 2014; 2015; 2022; 2023 | 2004: In 2007, a new methodology was introduced to correct for some double-counting in source of funds for universities, and the Higher Education R&D expenditure data revised for 2004., 2009: the cost of research and development work by the Ministry of Defence in connection with the FOST (Strategic Ocean Force), which previously was not included under R&D; 2014: better consideration given to university hospital staff conducting R&D work within these institutions (+8,500 FTEs compared to 2013). As a result, R&D expenditure increased. 2023: methodological improvements and expansion of the scope covered. |
| Type of costs | 2001-2023 | No breaks | No comments |
| Type of R&D | 2001-2021; 2022; 2023 | 2022 | Change of imputation of missing values method. |
| Other | No | No | 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. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual (FM) regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Survey results are the input for national accounts, there is no other source for R&D.
15.3.3. Coherence – Education statistics
The sub-directorate of Information Systems and Statistical Studies is the only entity who conducts national survey on R&D in HES in France. So there is no reference to compare with.
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 – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 12940049 | 139690 | 95861 |
| Final data (delivered T+18) | 12712457 | 139976 | 96248 |
| Difference (of final data) | -227592 | +286 | +387 |
Comments:
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 102480 | 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 | 129874 in euros | 1,5 persons in FTE |
| Data collection costs | ||
| Other costs | 32481 in euros | |
| Total costs | 162355 in euros | 1,5 persons in FTE |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 294 | Sum of all the surveyed units that partially or entirely answered the survey. |
| Average Time required to complete the questionnaire in hours (T)1) | Not available | Not available |
| 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 | 332 |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | Our database is updated with informations provided by the units and an online platform collecting information on all the HES units in France. |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Very good |
| Variables the survey contributes to | Not applicable |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Direction générale des ressources humaines (DGRH) - Human resources general directorate for the Ministry |
|---|---|
| Description of collected data / statistics | R&D personnel (in head counts) for public Universities under the direct and exclusive authority of the Ministry in charge of of Higher Education and Research. Data breakdown by FORD and gender. |
| Reference period, in relation to the variables the administrative source contributes to | 2023 |
| Variables the administrative source contributes to | Breakdown for R&D personnel (in head counts) by FORD and gender for the whole population are obtained by applying the structure of data collected from the DGRH to the total number of R&D personnel. |
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, citizenship, their function, the type of contract, their employer, 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 have partially completed questionnaires. |
| Time-use surveys for the calculation of R&D coefficients | Not applicable |
| Realised sample size (per stratum) | Not applicable |
| 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, calls and explanations about the importance of the survey and the results. |
| Follow-up of non-respondents | Re-contact by emails (twice) and mail (once), follow-up by phone. |
| 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) | 89% |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Values are imputed using the following methods in this order : |
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 | enseignementsup-recherche.gouv.fr |
| 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).
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)
Intra expenditures : 22%
HC : 28%
FTE : 32%
Ressources : 23%
18.5.2. Data compilation methods
| Data compilation method - Final data | Annual survey |
|---|---|
| Data compilation method - Preliminary data | Annual survey (preliminary results and imputations) |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | Not applicable |
|---|---|
| Revision policy for the coefficients | Not applicable |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not applicable |
18.5.4. 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. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT are excluded from R&D expenditures. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | The GUF earmarked for R&D corresponds to the entire budget allocation for R&D at universities, whether this involves university research credits recorded in the civil R&D budget or in the higher education budget. |
18.5.5. 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 higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
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 and its overseas departments and territories.
- For R&D personnel (HC): 31 December, 2023
- For R&D personnel (FTE) and expenditures as well as ressources: 2023 calendar year
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR); 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) and in head counts (HC).
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 dissemination 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.
