1.1. Contact organisation
Ministry of Higher Education, Research (MESR in french)
1.2. Contact organisation unit
SIES - Sub-Directorate for Information Systems and Statistical Studies
Department of statistical studies on research and development
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Ministère de l’enseignement supérieur, de la recherche et de l'innovation
DGESIP/DGRI – SIES – Département des études statistiques de la recherche
1 rue Descartes, 75231 Paris Cedex 05
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
23 February 2024
2.2. Metadata last posted
15 June 2023
2.3. Metadata last update
23 February 2024
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.
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. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
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).
3.2.1. Additional classifications
| Additional classification used | Description |
| We only use the FM 2015 as reference. | Not concerned. |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Same as defined in the FM 2015. |
| Fields of Research and Development (FORD) | We use twelve (12) fields. Eleven (11) of them are covered by the six (6) fields recommanded in the FM 2015 and the last one is "The R&D management". |
| Socioeconomic objective (SEO by NABS) | All socioeconomic objectives are covered. There is no breakdown of R&D indicators by SEO. |
3.3.2. Sector institutional coverage
| Higher education sector | See below |
| Tertiary education institution | Public and private educational establishments 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. |
| HES Borderline institutions | - The CNRS (National Center for Scientific Research) is included in the HES but is usually considerded as belonging to the Government sector. - There are also some private schools that could be considered as enterprises. |
| Inclusion of units that primarily do not belong to HES | The CNRS (National Center for Scientific Research) is included in the HES but is usually considerded as belonging to the Government sector. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | There is no deviation from the 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 | 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 : - EU - International organisations - Foreign enterprises - Foreign higher education institutions. |
| Payments to rest of the world by sector - availability | Payments to: - International organisations - Foreign enterprises - Foreign higher education institutions. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) 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 to be 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 2022. |
| Defence R&D - method for obtaining data on R&D expenditure | We interviewed 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)
We don't have data on qualification level.
| Coverage of years | Total number of person employed and still working the 31st December of the reference year |
| Function | We do a distinction between Researchers and support 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 | Distinction between Researchers, technicians, Phd Students and support personnel |
| Qualification | Not asked |
| Age | Not asked |
| Citizenship | Not asked |
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.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| We don't collect data on qualification. | Not available | Not available. |
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain. Precisely, establishment.
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 sources. |
| Estimation of the target population size | Approximatively 332 statistical units. | We don't use another data sources. |
3.7. Reference area
France and its overseas departments and territories.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. 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 expenditures are given in Keuros (1.000 euros).
R&D personnel is given in headcounts and in FTE (with two decimal place).
- For R&D personnel (HC): 31 st December, 2021
- For R&D personnel (FTE) and expenditures as well as ressources: 2021 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 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. 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. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | mandatory |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Yes |
| Legal acts | |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Our survey is not compulsory, so the unit could refuse to respond. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | bospe-mesri-2-1424603-pdf-16940.pdf (enseignementsup-recherche.gouv.fr) Go to page 17 and 18. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | - Law no. 51-711 of June 7, 1951 on the obligation, coordination and secrecy of statistics. Article 6 - Loi n° 51-711 du 7 juin 1951 sur l'obligation, la coordination et le secret en matière de statistiques. - Légifrance (legifrance.gouv.fr)
- Article 26 of the law no. 83-634 of July 13, 1983 on the rights and obligations of civil servants. Also known as the Le Pors Act. Loi n° 83-634 du 13 juillet 1983 portant droits et obligations des fonctionnaires. Loi dite loi Le Pors. - Légifrance (legifrance.gouv.fr)
- Chapter 6 of the 1st part of the law no. 78-17 of January 6, 1978 on data processing, data files and individual liberties. For Collection, recording and storage of personal information. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | The Law no. 78-753 of July 17 introduced a citizen's right of access to administrative documents. This means that anyone can obtain access to documents held by an administration in the course of its public service mission, whatever their form or medium. |
| Planned changes of legislation | No, as far as we know |
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
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
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.
a) Confidentiality protection required by law:
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:
Every individual staff member is obliged by internal rules and by the European Statistics Code of Practice to a strict confidential treatment of information.
7.2. Confidentiality - data treatment
Data on private higher education establishments are subject to statistical confidentiality. Categories 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
June N+2 for national dissemination
8.2. Release calendar access
Calendrier 2023 des publications statistiques du SIES | enseignementsup-recherche.gouv.fr
8.3. Release policy - user access
Users are treated according to the national statistical system rules, i.e. all users have access to the information at the same time.
Yearly
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | N | Not concerned |
| Ad-hoc releases | Y | press released on the ministry website when the publication is disseminated |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | The results are published through: Notes d'information , Notes flash, Repères et références statistiques, l'état de l'enseignement supérieur et de la recherche en France , Repères et Références Statistiques, publication du MEN, Vers l'égalité femmes-hommes ? See https://www.enseignementsup-recherche.gouv.fr/fr/statistiques-et-analyses-50213 |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N | Not concerned. |
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
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | https://www.enseignementsup-recherche.gouv.fr/fr/statistiques-et-analyses-50213 |
| Access cost policy | The access is free |
| Micro-data anonymisation rules | No establishment name is revealed. |
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 : https://www.enseignementsup-recherche.gouv.fr/fr/statistiques-et-analyses-50213 |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | Specific requests from some government department (inspections, Cour des comptes), researchers and Insee |
| Other | N | Not concerned | Not concerned |
1) Y – Yes, N - No
10.6. Documentation on methodology
We don't have an official methodology file, but everyone in charge of the survey must write a file describing everything they've done, such as the survey objective, the population, the way they conducted the survey, the statistical process. We publish the notice that explain the concepts used in the survey.
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.) | Questionnaires, explanatory notes |
| Request on further clarification, most problematic issues | No |
| Measure to increase clarity | No |
| Impression of users on the clarity of the accompanying information to the data | This information is not available as we don't specifically ask feedback. As we have never received complains or clarifications requests on the results published, we can say it is overall good. |
11.1. Quality assurance
The exhaustivity of surveyed establishement is checked using the national directory of research structures (https://appliweb.dgri.education.fr/rnsr/). The staff in charge of the survey are qualified statisticians and the plateform where the data are collected have many error or incoherences checks and warn the respondent when he do something wrong. If needed a time extension is given to the respondents and a personnal assitance as well if needed.
11.2. Quality management - assessment
We survey approximately 332 higher education and research establishments each year (CNRS included). In 2021 the response rate is 87%. 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 STI regulation | Metadata |
| 1 | OCDE - MSTI | National aggregates |
| 1 | MESR | National aggregates |
| 3 | Medias | Disseminated data |
| 6 | Cour des comptes, inspections générales de l'adminisatration, des finances ou de l'éducation nationale | Specific questions |
| 4 | Researchers or students | Specific questions |
1) Users' class codification
1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes.)
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | We don't do that |
| User satisfaction survey specific for R&D statistics | We don't do that |
| Short description of the feedback received | Not available |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Not available.
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.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | X | |||||
| Obligatory data on R&D expenditure | X | |||||
| Optional data on R&D expenditure | X | |||||
| Obligatory data on R&D personnel | X | |||||
| Optional data on R&D personnel | X | |||||
| Regional data on R&D expenditure and R&D personnel | X |
Criteria:
A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.
B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Y-1992 | Every year | 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 modifications | No modifications | No modifications |
| Type of R&D | Y-1992 | Every year | 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 modifications | No modifications | No modifications |
| Type of costs | Y-1992 | Every year | 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 modifications | No modifications | No modifications |
| Socioeconomic objective | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| Region | Y-1992 | Every year | 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 modifications | No modifications | No modifications |
| FORD | Y-1992 | Every year | 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 modifications | No modifications | No modifications |
| Type of institution | Y-1992 | Every year | 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 modifications | No modifications | No modifications |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y-1992 | Every year | No gap year | No modifications | No modifications | No modifications |
| Function | Y-1992 | Every year | No gap year | No modifications | No modifications | No modifications |
| Qualification | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| Age | Y-1992 | Every year | No gap year | No modifications | No modifications | No modifications |
| Citizenship | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| Region | N |
Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| FORD | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| Type of institution | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| Function | Y-1992 | Every year | No gap year | No modifications | No modifications | No modifications |
| Qualification | N | Not concerned |
Not concerned | Not concerned | Not concerned | Not concerned |
| Age | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| Citizenship | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| Region | Y-1992 | Every year | No gap year | No modifications | No modifications | No modifications |
| FORD | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
| Type of institution | N | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
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 | Every Year | By R&D perfomer sector (government, enterprises, higher education faciloties, foreign institution) | 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
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors | 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 | 3 | - | 1 | - | +/ - |
| Total R&D personnel in FTE | - | 5 | 3 | - | 1 | - | +/- |
| Researchers in FTE | - | 5 | 3 | - | 1 | - | +/ - |
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 (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.
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' = In the event that at least one out of the three criteria above described would not be fully met.
3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would 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
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
13.2.1.1. Variance Estimation Method
Doesn't apply because we conduct a census survey.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | Not concerned |
| Government | Not concerned |
| Higher education | Not concerned |
| Private non-profit | Not concerned |
| Rest of the world | Not concerned |
| Total | Not concerned |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | Not concerned |
| Technicians | Not concerned | |
| Other support staff | Not concerned | |
| Qualification | ISCED 8 | Not concerned |
| ISCED 5-7 | Not concerned | |
| ISCED 4 and below | Not concerned |
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: In 2023, during discussion with the respondents/ analysis of their extramural R&D expenditures, we have discovered that there are some higher education establishment performing R&D who were not surveyed the previous years. But the number of cases is really low, 2 establishments. There also some of HES private establishments who were considered as belonging to the enterprises sector but shouldn't have.
b) Measures taken to reduce their effect: Find additionnal sources to identify the establishments and exploit deeply the higher education establishments our surveyed respondents said to work with. Coordonnate with the department in charge of R&D in enterprises to identify the private schools that we considered as enterprises.
13.3.1.1. Over-coverage - rate
Not concerned beuase we run a census survey.
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:
Controls on unit used by the respondent and the consistency with the rest of the recorded information.
b) Measures taken to reduce their effect:
There are micro and macro controls on the survey platform and we also proposed to the respondents to call or send us a mail if they have questions.
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)
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) |
| 288 | 332 | 13% |
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 variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| R&D Personnel (both FTE and PP) | Approximatively 25% for FTE and 25 for PP | No comments |
| R&D Expenditure (i.e. HERD in the present case) | Approximatively 23% | No comments |
| R&D HES Sources of funding (to cover research expenditure of the sector) | Approximatively 25% | No comments |
13.3.3.3. Measures to increase response rate
We do follow-up by email and phone calls and possible deadline extension.
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | An online questionnaire |
| Estimates of data entry errors | 0% of non valid values. 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 | No coding was performed |
| 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 | Imputation, re-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)
a) End of reference period:
b) Date of first release of national data:
c) Lag (days):
14.1.2. Time lag - final result
a) End of reference period: 31 December, 2021
b) Date of first release of national data: July 2023
c) Lag (days): Roughly 19 months
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | We don't transmit provisional data, only final. | 19 |
| Delay (days) | Not concerned | 30 |
| Reasoning for delay | Not concerned | For reasons of programming the automation of SDMX file production |
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 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 paragraph 5.2). | N | Not concerned |
| Researcher | FM2015, § 5.35-5.39. | N | Not concerned |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat'EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| 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). | N | Not concerned |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | N | Not concerned |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | N | Not concerned |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | N | Not concerned |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | N | Not concerned |
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 | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | N | Census |
| Survey questionnaire / data collection form | N | Online questionnaire and the responses are hosted in a database. |
| Cooperation with respondents | N | 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 | N | 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 | N | As recommenced by FM 2015, we collect data on GUF and don't include in the intern funds. |
| Data processing methods | N | 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 | N | We impute the value of the previous survey, if not available, for each non-respondent, we affect a group of establishment who it looks the most like based onn the information we have. We affect the median of the group answer to the non-respondent missing values. |
| Variance estimation | Not concerned because we run a census survey | No comment |
| Method of deriving R&D coefficients | We conduct yearly census survey, so we are not concerned. | No comments |
| Quality of R&D coefficients | We conduct yearly census survey, so we are not concerned. | No comments |
| Data compilation of final and preliminary data | Not concerned, we only have final data | 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 | 2004, 1997 | 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. 1997:The main break concerns higher education where the availability of new files on university posts to which appointments have actually been made has provided a basis on which to review the personnel numbers taken into consideration in our surveys (a reduction of approximately 4 800 paid lecturer/researcher FTE). The estimated adjustment has been made on paid personnel. |
| Function | From 1978 | No breaks | No comments |
| Qualification | Not concern, we don't collect data on qualification | Not concern, we don't collect data on qualification | No comments |
| R&D personnel (FTE) | From 1978 | 2004,1997 | 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. 1997:The main break concerns higher education where the availability of new files on university posts to which appointments have actually been made has provided a basis on which to review the personnel numbers taken into consideration in our surveys (a reduction of approximately 4 800 paid lecturer/researcher FTE). The estimated adjustment has been made on paid personnel. |
| Function | From 1978 | No breaks | No comments |
| Qualification | Not concern, we don't collect data on qualification | Not concern, we don't collect data on qualification | No comments |
| R&D expenditure | From 1978 | 1981 | The evaluation of R&D expenditure was modified to take account of: - a reassessment of the proportion of time devoted to research by lecturers. The Ministry of Education currently estimates this share to amount to 50% on average, whereas the coefficients previously supplied by the Ministry and applied until 1980 (natural sciences 65%, medicine 30% and social sciences 10%) amounted on average to approximately 35%; - the cost of research and development work by the Ministry of Defence in connection with the FOST (Strategic Ocean Force), which previously was not included under R&D; - the impact of levying VAT on public research bodies. |
| Source of funds | From 1978 | 2004, 1992 | 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. 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 | No breaks | No comments |
| Type of R&D | From 1978 | No breaks | No comments |
| 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
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
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 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. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
| There are no other statistics for which data from HES can be compared with because we are the only ones conducting the R&D in HES survey. | Not concerned | Not concerned | Not concerned | Not concerned | Not concerned |
15.3.4. Coherence – Education statistics
The Information Systems and Statistical Studies department is the only entity who run national survey on R&D in HES in France. So there is no reference to compare to.
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) | - | - | - |
| Final data (delivered T+18) | 11634 | 42852 | 93273 |
| Difference (of final data) | Can't be computed | Can't be computed | Can't be computed |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | - |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | We are unable to provide this information because we don't have other current costs for external R&D personnel |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | Not available | Not available |
| Data collection costs | Not available | Not available |
| Other costs | Not available | Not available |
| Total costs | Not available | Not available |
| Comments on costs | ||
| No comments | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | 288 | Sum of all R&D-performing universities, colleges and healthcare institutions that responded partially or entirely to the survey. |
| Average Time required to complete the questionnaire in hours (T)1 | 18 | Mean of the time spent reported by the respondents. 90% of respondents to our survey reported their time spent to complete the survey. |
| 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. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | Survey on resources devoted to R&D in higher education and healthcare |
| Type of survey | Census among all known R&D perfoming units in Higher education and healthcare |
| Combination of sample survey and census data | Not concerned |
| Combination of dedicated R&D and other survey(s) | Not concerned |
| Sub-population A (covered by sampling) | Not concerned |
| Sub-population B (covered by census) | Not concerned |
| Variables the survey contributes to | All the variables requested by the European regulation |
| Survey timetable-most recent implementation | Starting date: 30 October, 2023 First reminder: 19 December, 2023 Second reminder: end of January, 2024 Estimated ending date: 15 March, 2024 |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | No sample | Not concerned | Not concerned |
| Stratification variables (if any - for sample surveys only) | - | - | - |
| Stratification variable classes | - | - | - |
| Population size | 332 | - | - |
| Planned sample size | - | - | - |
| Sample selection mechanism (for sample surveys only) | - | - | - |
| Survey frame | We have our own register and we update it every year with "Paysage", an online platform that has information on all the HES facilities in France. | - | - |
| Sample design | Census | - | - |
| Sample size | - | - | - |
| Survey frame quality | Really Good | - | - |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Statistical Studies and Information Systems (SIES) |
| Description of collected data / statistics | In addition to the present survey, we use data from the "Annual report on scientific employment in research organizations". It provides us additionnal information on gender breakdown, but only for universities under the authority of the French ministry of Higher education. |
| Reference period, in relation to the variables the survey contributes to | 2021 |
18.2. Frequency of data collection
See 12.3.3.
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, HR 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 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 have partially completed questionnaires. |
| Time-use surveys for the calculation of R&D coefficients | The units we survey have a recording of the time spent on R&D by all their staff. |
| Realised sample size (per stratum) | No sample |
| 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 head of the department coordinating higher education and research strategies. |
| Follow-up of non-respondents | 1 by email, a second by phone call and a letter as last ressort. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not concerned |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 76% (this is the rate when we only focus on those who completed entirely the survey). But if we consider as well the partial responses, the response rate is 87%. |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | We do imputation for the non-respondents based on their previous year answer and/or the units they look the most like. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | N |
| R&D national questionnaire and explanatory notes in the national language: | https://www.enseignementsup-recherche.gouv.fr/fr/enquete-rd-aupres-des-administrations-81709 |
| Other relevant documentation of national methodology in English: | N |
| Other relevant documentation of national methodology in the national language: | N |
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
We do imputation for non-response by imputing the previous survey's response or by estimating the response based on the responses of similar universities for example. The imputation rate is approximatively equal to 11%.
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | We run our survey every year, so we are not concerned. |
| Data compilation method - Preliminary data | We run our survey every year, so we are not concerned. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | We run our survey every year, so we don't estimate HERD. |
| Revision policy for the coefficients | We run our survey every year, so we don't estimate HERD. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | We run our survey every year, so we don't estimate HERD. |
18.5.4. Measurement issues
| Method of derivation of regional data | We collect information (expenditures and personnal FTE) on the regions where the R&D is performed by the units. |
| Coefficients used for estimation of the R&D share of more general expenditure items | University R&D resources are estimated globally for all universities and institutes on the basis of the R&D share applicable to the various budget items. If the exact share is not available, a 50% rate is applied to personnel costs and to the calculation of FTEs for researchers. This estimate is based on numerous data files, supplemented by a survey of resources per university contract, conducted by the research departments of the French Ministry of Research. In 1997, the use of new administrative sources made it possible to better estimate the number of teacher-researchers, leading to a downward revision of the figures. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT are excluded from R&D expenditure. |
| 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. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | No deviation. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Government: Defense data are supplied by the Ministry of Defense, which provides an estimate of its research budget broken down into intramural and extramural expenditure. Extramural expenditure is consolidated with data collected by the government and business surveys. A new estimate of intramural defense spending was made for 1998, and the 1997 data have therefore been revised. Since 1992, a field correction has been introduced to match that added to the business survey. This correction consists in reclassifying public-sector organizations (GIAT Industries and France Télécom) previously classified under public administration as companies, and explicitly taking into account other organizations (such as ONERA) whose R&D activities had not been fully covered. |
| Description of the estimation method | For the non responses, we do imputation first by imputating the n-1 response if non-missing or by estimating the response. To estimate the missing value, we calculate the median of a group of establishments to which the non-respondent is most similar for the variable of interest. The group of similar establishments is constitute by comparing the non-respondent's answers to the others establishments answers. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
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.
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. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
23 February 2024
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain. Precisely, establishment.
See below.
France and its overseas departments and territories.
- For R&D personnel (HC): 31 st December, 2021
- For R&D personnel (FTE) and expenditures as well as ressources: 2021 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:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditures are given in Keuros (1.000 euros).
R&D personnel is given in headcounts and in FTE (with two decimal place).
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
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.
