Ministry of higher education and research (France)

Department of statistical studies on research

SIES - Sub-Directorate for Information Systems and Statistical Studies

Ministère de l'enseignement supérieur et de la recherche

SIES - A2.2

1 rue Descartes

75231 Paris Cedex 05

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.  

• 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).
  

See below.

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).

See below.

See below.

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.

Compliant with the FM2015 (Chapter 10, §10.2) and the SNA.

See below.

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. 

France, including overseas departments and territories.

Not requested. See point 3.4.

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

See below.

- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development

- European Business Statistics Methodological Manual on R&D

Not requested.

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.

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.

Preliminary results : June N+2

Final results : September N+2 and December N+2 (more detailed)

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

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

https://data.enseignementsup-recherche.gouv.fr/explore/dataset/fr-esr-publications-statistiques/information/

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

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

Not requested.

See below.

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

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.

See below.

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.)

To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.

See below.

not available

See below.

12.3.3.1. Data availability - R&D Expenditure 

1) Y-start year, N – data not available

12.3.3.2. Data availability - R&D Personnel (HC) 

1) Y-start year, N – data not available

12.3.3.3. Data availability - R&D Personnel (FTE) 

1) Y-start year, N – data not available

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.

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.

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)

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.

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.

22% of the units who answered ar not in the target.

Not relevant.

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. 

Not requested.

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) 

Not requested.

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.

Not requested.

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.

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): 

a) End of reference period: 31 December, 2021

b) Date of first release of national data: July 2023

c) Lag (days): Roughly 19 months

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.

Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)

See below.

Not requested.

Nothing noticeable.

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.

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.

See below.

See below.

1)       Breaks years are years for which data are not fully comparable to the previous period.

Are the data produced in the same way in the odd and even years? If no, please explain the main differences. 

Yes it is. 

See below.

Not requested.

Survey results are the input for national accounts, there is no other source for R&D.

See below.

This part compares key R&D variables as preliminary and final data.

(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. 

1)       The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.

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’)

Not requested.

Not requested.

Not requested.

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.

See below.

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.

See below.

The imputation rate for PNP R&D expenditure is equal to 24% in 2021.

Not requested.

Not requested.