1.1. Contact organisation
Ministry of higher education and research (France)
1.2. Contact organisation unit
Department of statistical studies on research
SIES - Sub-Directorate for Information Systems and Statistical Studies
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Ministère de l'enseignement supérieur et de la recherche
SIES - A2.2
1 rue Descartes
75231 Paris Cedex 05
1.6. Contact email address
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
29 March 2024
2.2. Metadata last posted
29 March 2024
2.3. Metadata last update
29 March 2024
3.1. Data description
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
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 the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- The distribution of principal economic activity and by product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2) ;
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics ;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) ;
- The fields of research and development based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
| not applicable |
|
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Frascati Manual 2015 Chapter 2 - mainly paragraphs 2.2, 2.3 and 2.4 |
| Fields of Research and Development (FORD) | Only GERD and researchers in headcount are disaggregated by FORD |
| Socioeconomic objective (SEO) | No data available by SEO for PNP |
3.3.2. Sector institutional coverage
| Private non-profit sector | Non-profit associations, foundations and public interest group |
| Inclusion of units that primarily do not belong to GOV |
3.3.3. R&D variable coverage
| R&D administration and other support activities | R&D administration and other support activities: no deviations from FM §2.122. |
| External R&D personnel | The treatment of external personnel in R&D expenditure and R&D personnel is compliant with FM §5.20-5.23, Table 5.2. Included categories of external personnel are : R&D personal working into the enterprise without being paid directly by the society (temporary workers, consultancy activities, doctoral/master's students). Volunteers are not included (FM §5.24). |
| Clinical trials | Compliant with Frascati manual. Clinical trials in Phase 1, 2 and 3 are included. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | The following categories can be distinguished : - foreign enterprises |
| Payments to rest of the world by sector - availability | - foreign national organisations - from foreign higher education and other foreign state bodies - foreign enterprises |
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 | External R&D expenditures correspond to subcontracting and collaboration in R&D tasks. It is the tax-free expenditure on complete or partial R&D programs carried out by a third party on behalf of one PNP, amounts paid to support research, and research expenses incurred outside France, including payments to international organizations located abroad. |
| Difficulties to distinguish intramural from extramural R&D expenditure | No. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
| Source of funds | For PNP, the sources of funds by R&D branch are collected on the basis of total expenditure (intramural + extramural R&D expenditure). Compliant with FM (see FM §4.104-4.108, Table 4.3.). Data on internal/external funds are collected. Data on transfer/exchanges are included but not distinguished. |
| Type of R&D | No divergence from FM (FM section 2.5). |
| Type of costs | Labour costs, other current costs (icld costs for external R&D personnel), capital expenditures (breakdown by lands and buildings, instruments and equipments). |
| Defence R&D - method for obtaining data on R&D expenditu | Extramural expenditures are collected only for non-defence sector. No specific information is collected for GERD. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | 31st December of the reference year |
| Function | The R&D personnel is classified by occupation. The researchers category corresponds to the specialists who work with the design or the creation of knowledge, products, processes, methods and new systems. Also included are executives and administrators who plan and manage the researchers work. The technicians category includes engineers whose work is not regarded as R&D per se as well as the other persons performing technical tasks linked with R&D projects. The support personnel consists of workers, qualified or not, and office personnel helping with R&D projects or who are directly associated with the implementation of such projects. |
| Qualification | Researchers data are broken down by seniority. Doctoral student are collected. |
| Age | Less than 25, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, more than 65. |
| Citizenship | Not available |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
| Function | The R&D personnel is classified by occupation. The researchers category corresponds to the specialists who work with the design or the creation of knowledge, products, processes, methods and new systems. Also included are executives and administrators who plan and manage the researchers work. The technicians category includes engineers whose work is not regarded as R&D per se as well as the other persons performing technical tasks linked with R&D projects. The support personnel consists of workers, qualified or not, and office personnel helping with R&D projects or who are directly associated with the implementation of such projects. |
| Qualification | Researchers data are broken down by seniority. Doctoral student are collected. |
| Age | Not available. |
| Citizenship | Not available. |
3.4.2.3. FTE calculation
Full-time equivalents consist of average figures for the year that take account of departures and arrivals during the year and also of the time devoted to research in cases where the activity does not consist solely of R&D.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| not available | ||
3.5. Statistical unit
Compliant with the FM2015 (Chapter 10, §10.2) and the SNA.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the PNP Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | A census survey is used for collection of raw data. Target population consists of all PNP with NACE 72 and legal categories 92 Associations, 93 Foundations and 7410 Public Interest Groups in the business register (which includes PNP), expanded with PNP eligible to R&D tax credit, with resultats from ScanR, a governmental search engine dedicated to the research area, and with the population from the previous year. | no |
| Estimation of the target population size | 590 units | no |
3.7. Reference area
France, including overseas departments and territories.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested.
R&D expenditures are given in Keuros (1.000 euros).
R&D personnel is given in headcounts and in full-time equivalent (FTE).
2021
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 | Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European statistics and repealing Regulation https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:02009R0223-20150608 |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Yes |
| Legal acts | National council for statistical information, Visa n°2022A713RE, survey of general interest and statistical quality. Official bulletin of the Ministry of Higher Education and Research |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Yes |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Yes |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Yes |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Yes |
| Planned changes of legislation | No |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
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 collected through a survey labelled by the National council for statistical information 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
Categories containing information from less than 3 enterprises or 1 enterprise contributing more than 85% cannot be disclosed. In order to prevent indentifcation of these celles by simple substractions from total, at least one additional category must be suppressed.
8.1. Release calendar
Preliminary results : June N+2
Final results : September N+2 and December N+2 (more detailed)
8.2. Release calendar access
| https://www.enseignementsup-recherche.gouv.fr/fr/calendrier-2023-des-publications-statistiques-du-sies-46592 |
8.3. Release policy - user access
Official calendar
Publications
Press releases
Users are treated according to the national statistical system rules, i.e. all users have access to the information at the same time.
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 | |
| Ad-hoc releases | Y | press release on the ministry website when the publication is disseminated |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | https://www.enseignementsup-recherche.gouv.fr/fr/la-depense-de-recherche-et-developpement-experimental-en-2021-92628 |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
|
1) Y – Yes, N - No
10.3. Dissemination format - online database
https://data.enseignementsup-recherche.gouv.fr/explore/dataset/fr-esr-publications-statistiques/information/
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 | Micro-data access for researchers is possible, with legal constraints. |
| Access cost policy | Provisioning costs have to be paid. |
| Micro-data anonymisation rules | No anonymisation, due to the confidentiality rules applied to researchers (see https://www.casd.eu/en/) |
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 | ministry website : https://publication.enseignementsup-recherche.gouv.fr/FR/ | |
| Data prepared for individual ad hoc requests | Y | Specific requests from government bodies (inspections, Cour des comptes), statistical teams from other ministries and Insee | |
| Other | Y | Y | For researchers only : https://www.casd.eu/en/ |
1) Y – Yes, N - No
10.6. Documentation on methodology
https://www.cnis.fr/enquetes/enquete-sur-les-moyens-consacres-a-la-recherche-et-au-developpement-experimental-rd-dans-les-associations-et-les-gip-2022a713re/?theme=1094
https://www.enseignementsup-recherche.gouv.fr/sites/default/files/2022-12/notice---associations-et-gip-2021--14749.pdf
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Explanatory notes linked to the questionnaire. |
| Request on further clarification, most problematic issues | None |
| Measure to increase clarity | None |
| Impression of users on the clarity of the accompanying information to the data | Not always enough. |
11.1. Quality assurance
The R&D survey has obtained the Label of general interest and compliance to the rules of public statistic : https://www.cnis.fr/wp-content/uploads/2022/04/AC_2022_Sies_RD_associations_GIP.pdf
11.2. Quality management - assessment
Every 3 to 5 years, a board of experts from the National Council of statistical information examines the quality of the survey and asks for clarifications and improvements which are assessed at the next session.
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'administration, des finances ou de l'éducation nationale, others Ministries | 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 | |
| User satisfaction survey specific for R&D statistics | |
| Short description of the feedback received |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
not available
12.3.2. Data availability
See below.
12.3.2.1. Incorporation of PNP sector in another sector
| Incorporation of PNP in another sector | No |
| Reasons for not producing separate R&D statistics for the PNP sector | Not relevant. |
| Share of PNP expenditure in the total expenditure of the other sector | Not relevant. |
| Share of PNP R&D Personnel in the respective figure of the other sector | Not relevant. |
12.3.2.2. Non-collection of R&D data for the PNP sector
| Reasons for not compiling R&D statistics for the PNP sector | Not relevant. Statistics are produced. |
| PNP R&D expenditure/ GERD*100) | |
| Share of PNP R&D Personnel in the respective figure of the total national economy |
12.3.2.3. Data availability on more detail level
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 | 2015- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Type of R&D | 2015- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Type of costs | 2015- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Socioeconomic objective | N | |||||
| Region | N | |||||
| FORD | 2019- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Type of institution | 2009- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
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 | 2015- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Function | 2015- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Qualification | N | |||||
| Employment status | 2015- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Age | N | |||||
| Citizenship | N | |||||
| Region | N | |||||
| FORD | 2019- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Type of institution | N | |
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 | 2015- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Function | 2015- | Yearly | N | Methodological changes on sampling | 2020 | Improving the results |
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | N | |||||
| FORD | N | |||||
| Type of institution | N | |
1) Y-start year, N – data 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:
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' | ||||||||||||||||||||||||||||||||||||
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. |
||||||||||||||||||||||||||||||||||||
| 3.1.2. Assessment of the accuracy with regard to the main indicators | ||||||||||||||||||||||||
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)
Coefficient of variation for Total R&D expenditure : does not apply (the survey is exhaustive)
Coefficient of variation for Total R&D personnel (FTE) : does not apply (the survey is exhaustive)
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.
a) Extent of non-sampling errors: Difficult to measure but wo know we have problems to correctly determine the population, due to the fact that research in itself is not a criterium in official statistical databases
b) Measures taken to reduce the extent of non-sampling errors: We cross several sources to ba as accurate as possible
c) Methods used in order to correct / adjust for such errors: We ask a filter question : do you realize R&D, the result of which is then used to separate the PNP which are not in the scope.
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: It is rather difficult to identify the PNPs who perform R&D. We use four criteria in an attempt to detect them at best : having answered they perform R&D the year before, having asked for tax credit aimed at encouraging R&D, being classified in a NACE R&D, being cited in ScanR, an aggregation site on the R&D theme.
b) Measures taken to reduce their effect: We have a filter question :
Do you perform R&D in 2021 ?
Did you perform R&D during the three previous years ?
Will you perform R&D starting from 2022 ?
We use the answers to better delineate the target population. On top of that we coordinate ourselves with the team that conducts the survey adressed to the firms.
13.3.1.1. Over-coverage - rate
22% of the units who answered ar not in the target.
13.3.1.2. Common units - proportion
Not relevant.
13.3.2. Measurement error
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
Not requested.
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)
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
| 258 | 512 | 50% |
13.3.3.2. Item non-response - rate
Not requested.
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.
| 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 | Online and offline micro-controls and offline macro-controls. Comparison with N-1 data. 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
Nothing noticeable.
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 paragraphs and the EBS Methodological Manual on R&D Statistics with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
| R&D personnel | FM2015 Chapter 5 (mainly 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'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 |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Intramural R&D expenditure | FM2015,Chapter 4 (mainly paragraph 4.2). | N | Not concerned |
| Statistical unit | FM2015, § 10.40-10.42 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Target population | FM2015, § 10.40-10.42 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Sector coverage | FM2015, § 10.2-10.8 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | N | Not concerned |
| Reference period for the main data | Reg. 2020/1197: Annex 1, Table 18 | N | Not concerned |
| Reference period for all data | 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 write them back if there is something wrong or not clear with their answers. |
| 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 | First we impute the N value with the N-1 answer when the unit answered the previous year. Secondly we estimate which part of the non-respondents is in the target and which part is not, based on the proportion in the respondent population cut in strata. It gives us coefficients applied stratum by stratum. |
| 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 | 2020, 1997, 1992 | 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). |
| Function | From 1978 | 2020, 1997, 1992 | 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). |
| Qualification | Not concerned, we don't collect data on qualification. | ||
| R&D personnel (FTE) | From 1978 | 2020, 1997, 1992 | 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). |
| Function | From 1978 | 2020, 1997, 1992 | 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). |
| Qualification | Not concerned, we don't collect data on qualification. | ||
| R&D expenditure | From 1978 | 2020, 1997, 1992, 1981 | 2020 : Methodological innovations on the sampled part. 1997:Change in the method of evaluation of R&D (expenditure and personnel). 1992:The survey method for the private non-profit sector changed. 1981:The evaluation of R&D expenditure was modified to take account of: - a reassessment of the proportion of time devoted to research by lecturers. The Ministry of Education currently estimates this share to amount to 50% on average, whereas the coefficients previously supplied by the Ministry and applied until 1980 (natural sciences 65%, medicine 30% and social sciences 10%) amounted on average to approximately 35%; |
| Source of funds | From 1978 | 2020, 1992 | 2020 : Methodological innovations on the sampled part. 1992 : Account has been taken of the repayment of reimbursable aid in the distribution of R&D expenditure by source of funding. |
| Type of costs | From 1978 | 2020 | 2020 : Methodological innovations on the sampled part. |
| Type of R&D | From 1978 | 2020 | 2020 : Methodological innovations on the sampled part. |
| Other | No |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
Yes it is.
15.3. Coherence - cross domain
See below.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Survey results are the input for national accounts, there is no other source for R&D.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total PNP R&D expenditure (in 1000 of national currency) | Total PNP R&D personnel (in FTEs) | Total number of PNP researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | - | - | - |
| Final data (delivered T+18) | |||
| Difference (of final data) | Not relevant, we disseminte only final data. | Not relevant, we disseminte only final data. | Not relevant, we disseminte only final data. |
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 | ||
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) | 258 | Sum of all surveyed PNPs that responded partially or entirely to the survey. |
| Average Time required to complete the questionnaire in hours (T)1 | Those who perform R&D : 3h30 Those who answered they don't perform R&D : 27 mn |
Mean of the time spent reported by the respondents. 98 % of the respondents gave the time spent. |
| 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 private non profit organizations and public interest groupings |
| Type of survey | Census among all known PNPs deemed to perform R&D |
| 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: October 30, 2023 First reminder: March 1st, 2024 Estimated ending date: April 30, 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 | 512 | - | - |
| Planned sample size | - | - | - |
| Sample selection mechanism (for sample surveys only) | - | - | - |
| Survey frame | We mix four sources : NACE 72, tax credit for research, previous year respondents, completed by ScanR | - | - |
| Sample design | Census | - | - |
| Sample size | 512 | - | - |
| Survey frame quality | Reasonnably good |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | DgFip ; Insee |
| Description of collected data / statistics | Individual data on the Research Tax Credit ; Extract from Insee Firms Register SIRUS ==> both in order to select the units in the survey |
| Reference period, in relation to the variables the survey contributes to | 2019 - 2021 for RTC ; 2021 for SIRUS file |
18.2. Frequency of data collection
Yearly.
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, their function, the type of contract they are on, who pay them, their work place. |
| Data collection method | All the units receive an email to inform them about the survey, the deadlines and the link to the online questionnaire with their identifiers. We have access to their questionnaire whether it is completed or not. That means, we can use partially completed questionnaires. |
| Time-use surveys for the calculation of R&D coefficients | Not asked. We ask for FTE. |
| Realised sample size (per stratum) | No sample, it is census. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Online survey. The units have access to the questionnaire and just have to fill it. |
| Incentives used for increasing response | Follow-up and calls and explanation of the use of the data collected and as a last ressort, a letter of the sub-director for Information Systems and Statistical Studies |
| Follow-up of non-respondents | By email if an email adress is known, by postal mail for the others, by phone call for the most influent ones. |
| 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) | 50 % |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | We use the filter question "Did you performed R&D in year N" to apply coefficients in order to estimate the non-response part and we do imputation for the non-respondents based on their previous year answer if available. |
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: |
18.4. Data validation
Emails and phone follow-up to increase the response rate, consistency checks with the last survey answers and overall consistency of the answers (personnel expenditure and FTE for example).
Final results are discussed with our colleagues responsible for HESSI and the sub-director for Information Systems and Statistical Studies.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
The imputation rate for PNP R&D expenditure is equal to 24% in 2021.
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. 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 in the survey. |
| Coefficients used for estimation of the R&D share of more general expenditure items | Not concerned. We ask directly for R&D amount in the survey. But the units are free to explicit such a ratio in their own calculations. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT are excluded from R&D expenditure. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | No deviation. |
18.5.4. Weighting and estimation methods
| Description of weighting method | Every respondent unit has a weight equal to one since it is a census. |
| Description of the estimation method | We first do imputation for non-response by imputing the previous survey's response if available. Then we calculate a share by criteria of selection into the population of the units who realy perform research. Theses calculations give coefficients applied to the summary results of the population of respondents. The criteria are : - selected because of NACE 72, - selected beceause of tax credit for research, - selected because they were in the population the previous year and declared they performed R&D - selected because they were in the population the previous year but didn't answer - other source |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
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 the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
29 March 2024
See below.
Compliant with the FM2015 (Chapter 10, §10.2) and the SNA.
See below.
France, including overseas departments and territories.
2021
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' | ||||||||||||||||||||||||||||||||||||
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. |
||||||||||||||||||||||||||||||||||||
| 3.1.2. Assessment of the accuracy with regard to the main indicators | ||||||||||||||||||||||||
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. |
R&D expenditures are given in Keuros (1.000 euros).
R&D personnel is given in headcounts and in full-time equivalent (FTE).
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.
