1.1. Contact organisation
INSTITUTO NACIONAL DE ESTADISTICA (INE)
1.2. Contact organisation unit
Science and Technology Unit
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Avenida de Manoteras 50-52 , planta 3 despacho 327
28050 Madrid (Spain)
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
2.1. Metadata last certified
29 October 2025
2.2. Metadata last posted
29 October 2025
2.3. Metadata last update
29 October 2025
3.1. Data description
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise 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 business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
The field covered by GERD and total R&D personnel complies with the Frascati Manual.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
This sector covers all profit-making companies, both public and private. It also includes commercial enterprises, enterprises in which the government has shareholdings and co-operative research institutes. |
|---|---|
| Hospitals and clinics | Private hospitals are included in the business sector. |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | Not included |
3.3.3. R&D variable coverage
| R&D administration and other support activities | We comply with FM §2.122. recommendations regarding R&D administration and other support activities. |
|---|---|
| External R&D personnel | We collect total personnel and we collect external personnel, so it is possible to split it in internal and external for the breakdowns. |
| Clinical trials: compliance with the recommendations in FM §2.61. | We comply with the recommendation in FM §2.61. referring to inclusion as R&D of the clinical trials phases 1,2 and 3. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Data of funding from abroad are available by sector. |
|---|---|
| Payments to rest of the world by sector - availability | Data of payments to abroad (for external R&D) are available by sector. |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Data of R&D expenditure for Inward FATS is collected. |
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 enterprise) 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 | Extramural expenditure is requested in the questionnaire for all sectors, following the breakdown recommended by the Frascati Manual. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Expenses for external services are requested separately and the question has been improved to try to avoid confusion when completing it |
3.4. Statistical concepts and definitions
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
|---|---|
| Source of funds | Broken down into main disaggregation by sector as shown in table 4.3 FM §4.104-4.108. External funds are broken down into transfer/exchanged |
| Type of R&D | Three R&D types are covered, in line with FM section 2.5 |
| Type of costs | The breakdown of the type of cost is the following: Labour costs (Researcher’s labour cost, Technicians and other staff’s cost), Other current costs (broken down into expenses corresponding to external R&D personnel, expenses corresponding to purchase of services, expenses corresponding to purchase of materials and other current costs), Lands&buildings, Instruments&equipment, Software for R&D and other intellectual property products. |
| Economic activity of the unit | The reporting unit is the enterprise, classified according to its main economic activity (NACE). The information is obtained from business register. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Data are obtained by R&D survey, requesting also the industry served for these enterprises. The R&D expenditure is grouped globally under ISIC rev 4. 72/NACE rev 2 .72, but can be broken down by industry served. |
| Product field | It has been collected by CPA classification |
| Defence R&D - method for obtaining data on R&D expenditure | Defence GERD is underestimated in that the estimate of expenditure is based on the socio-economic objective "Defence". |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons engaged in R&D during the calendar year. |
|---|---|
| Function | Data available. |
| Qualification | Data available. |
| Age | Data available. |
| Citizenship | Data available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Total number of persons engaged in R&D during the calendar year. |
|---|---|
| Function | Data available. |
| Qualification | Data are now available as the breakdown used is comparable from 2006 onwards for researchers and total personnel. |
| Age | Data available. |
| Citizenship | Data available. |
3.4.2.3. FTE calculation
FTE is calculated according to Frascati Manual, using the concept person/year.
We ask it directly in the questionnaire
3.5. Statistical unit
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993,
The statistical unit is the enterprise but the responding unit and the observation unit and the sample unit is the legal unit.
3.6. Statistical population
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
3.6.1. National target population
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 Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
| 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 a subset of the frame population, including only the enterprises which were known as actual or potential R&D performers in the reference period within then national territory. The target population is based on the Directory of Legal Units which were known as actual or potential R&D performers (DIRID). | |
| Estimation of the target population size | 19.554 Legal Units | |
| Size cut-off point | There is no size cut-off point, but the sample part is extracted for legal units with 10 or more employees. | |
| Size classes covered (and if different for some industries/services) | All sizes are covered | |
| NACE/ISIC classes covered | All NACE classes are covered. |
3.6.2. Frame population – Description
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.
| Method used to define the frame population | The frame population for business statistics is the official Business Register (DIRCE) including all the business enterprises active in the reference period. It registers information such as identity data, location, main activity or number of employees. This information is obtained from administratives sources (Inland Revenue and Social Security) and complemented with data from common statistical operations. Moreover, this directory is annually updated. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | The DIRID is annually updated by the following data: a) Legal units receiving public support or grants for R&D activities (including not only Central Government but also almost all Autonomous Communities Governments). This information is obtained from the Central and Autonomous Communities Government. b) Legal units performing R&D activities in previous surveys. c) Legal units identified by sampling. This information is annually collected by the INE. |
| Inclusion of units that primarily do not belong to the frame population | Not included |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | In order to include new or not known R&D performers, the census is complemented with a sample extracted from the DIRCE, which overlaps with the Innovation sample. The sample covers those NACE activities included in the Innovation survey for legal units with 10 or more employees. Legal units with 200 employees or more, are studied exhaustively as well as those legal units whose activity corresponds to the division 72 of the NACE classification. Also, in a module of the Information and Communication Technology Survey addressed to the legal units with less than 10 employees a question about R&D activities has been included in order to complete the DIRID. By doing this, the coverage of the R&D survey is improved annually as both statistics are collected every year using a combined questionnaire. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | 2870 (unweighted) of final legal units with R&D activities were not included in the initial DIRID, being investigated through the sample part. 3700 (unweighted) of final legal units with R&D activities have been dropped from the DIRID. |
| Systematic exclusion of units from the process of updating the target population | The only NACE categories excluded are those corresponding to GOV, HES or PNP sectors, but in this situation, those units performing R&D are relocated in the appropriate sector. The sample part is extracted for legal units with 10 or more employees, therefore the improvement of coverage does not consider microenterprises. |
| Estimation of the frame population | 3,207,580 |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3. (data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
All questions and indicators refer to the calendar year
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
a) Confidentiality protection required by law:
In Spain, the main national legal regulations applicable to the protection of statistical data are:
-“Ley Orgánica 15/1999 de Protección de Datos de Carácter Personal”
-“Ley 12/1989 de la Función Estadística Pública”
-“Real Decreto 428/1993, de 26 de marzo, por el que se aprueba el Estatuto de la Agencia de Protección de Datos”.
-“Real Decreto 994/1999, de 11 de junio, por el que se aprueba el Reglamento de medidas de seguridad de los ficheros automatizados que contengan datos de carácter personal”.
b) Confidentiality commitments of survey staff:
Survey staff must sign a legal contract, ensuring the acknowledge of the confidentiality issues and data protection law, and therefore they also have legal commitments.
7.2. Confidentiality - data treatment
R&D data deliveries to Eurostat are checked in order to avoid primary and secondary confidentiality. This is done by checking any cell with less than 3 population units, and properly modifying the table to avoid also secondary disclosure.
8.1. Release calendar
The advance release calendar that shows the precise release dates for the coming year is disseminated in the last quarter of each year.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
For Spain, the calendar is disseminated on the INEs Internet website (Publications Calendar)
8.3. Release policy - user access
The data are released simultaneously according to the advance release calendar to all interested parties by issuing the press release. At the same time, the data are posted on the INE Website almost immediately after the press release is issued. Also some predefined tailor-made requests are sent to registered users. Some users could receive partial information under embargo as it is publicly described in the European Statistics Code of Practice
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.5 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | Main results are published in a press release. Press Release |
| Ad-hoc releases | Y | There is the possibility of requesting customised information from the INE User Care Department. At the time of processing said requests, this considers limitations regarding confidentiality or precision. |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | N | |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
INEbase is the system the INE uses to store statistical information on the Internet. It contains all the information the INE produces in electronic formats. The primary organisation of the information follows the theme-based classification of the Inventory of Statistical Operations of the State General Administration . The basic unit of INEbase is the statistical operation, defined as the set of activities that lead to obtaining statistical results on a determined sector or topic using data collected individually
Data availability since 1964
Main results compiled into a single ZIP file “Statistics on R&D”
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | Researchers may access to microdata files through online Secure Places. These microdata set available at online Secure Places do not include either direct identification variables or possible data aggregation. Researchers who wish to gain access to the microdata file must sign an agreement with the National Statistics Institute for access - for the research personnel - to the confidential data of the INE for statistical purposes. The agreement describes the project and the need for access to those microdata, specifies the period during which the research team will work in the INE, provides the name of the research team, and establishes the agreement clauses, including the statistical confidentiality clause. |
|---|---|
| Access cost policy | Products and Services/Information prices (See 'Information prices'). Prices of dissemination products from the National Statistics Institute (INE) were established in the Resolution on 1 September 2021 by the President of the National Statistics Institute by which the private prices for dissemination products of the body are established. (BOE 218, 11 September 2021). |
| Micro-data anonymisation rules | We supress every sensitive information that can disclose an enterprise |
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 | Apart from press release and the on-line database, there is no other type of data dissemination. From our point of view, the web-site offer R&D data with clarity and with an adequate structure. The accessibility to the data results is free. |
|
| Data prepared for individual ad hoc requests | Y | Aggregate figures | More specific requirements of information made by national and international institutions as well as individual users can be fulfilled under request, but keeping statistical secrecy in any case. |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Standardised Methodological Report
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Please see the sub-concept 10.7.1 in the full metadata view.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | In order to facilitate the adequate comprehension and proper use of data, some documents are also published together in the web page. These documents are:
In order to facilitate the adequate comprehension and use of data, some documents are also published together with them. These documents are:
Metadata published |
|---|---|
| Requests on further clarification, most problematic issues | Besides, if a user have any request or doubt concerning data or metadata, it is possible to contact with the Science and Technology Unit (via an electronic template) in order to obtain a more extended response or clarification. |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
11.2. Quality management - assessment
- Use of multiple sources of data: DIRID and DIRCE
- Actions for increasing the rate of response in surveys:
- We use the helping approach: a strategy of specifically requesting help as a way to compel participation.
- We try to conduct a well-designed, attractive survey in order to be easier to complete it.
- The use of multiple contacts with members of the sample. We contact non-respondents using combination of messages and surveys.
- Quality management in data processing: A check list of the different ways a data set is validated (internal consistency checks, non-zero values, number of records in is equal to number of records out) combined with responses with various outcomes (weak error and strong error)
- Annual mandatory survey with high response rate.
- Time series available, coherent with innovation data as both surveys are carried out coordinately.
- Methodology of the survey in line with the Frascati Manual.
- Full compliance of the Commission Regulation No 995/2012.
- Overall quality of data deemed to be very good
12.1. Relevance - User Needs
Please see the sub-concept 12.1.1 in the full metadata view.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1-European level | The European Commission (DG ENTR) | Data used for Structural indicators and others |
| 1-National level | The European Commission (DG ENTR) | Data used for policy-making and assessment of R&D phenomena |
| 1-National level | National Statistical Office | Data used for annual publication on R&D |
| 1-Regional level | Local authorities | Data used for policy-making and assessment of R&D phenomena |
| 1-International organisations | Eurostat, OECD | Data used for different analyses or studies and market studies. |
| 4- Researchers and students | Universities | Data used for different analyses or studies and market studies. |
| 5-Enterprises or business | Enterprises or business | Data used for different analyses or studies and market studies. |
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 | The INE conducted five general surveys of user satisfaction in 2007, 2010, 2013, 2016 and 2019. The specific needs of users are also taken into account when revising the survey design, in order to adapt the content of the survey to the specific requirements of its users, increasing the level of satisfaction. |
|---|---|
| User satisfaction survey specific for R&D statistics | No, it covers all the statistical operations of the institution. |
| Short description of the feedback received | Not available |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
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.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Not applicable |
| Obligatory data on R&D expenditure | Not applicable |
| Optional data on R&D expenditure | Not applicable |
| Obligatory data on R&D personnel | Not applicable |
| Optional data on R&D personnel | Not applicable |
| Regional data on R&D expenditure and R&D personnel | Not applicable |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y | Annual | ||||
| Type of R&D | Y | Annual | ||||
| Type of costs | Y | Annual | Since 2008, consultancy costs are included as a category in the breakdown of the type of ‘Other current costs’ | |||
| Socioeconomic objective | Y | Available for odd years till 2001. | ||||
| Region | Y | Annual | ||||
| FORD | Y-1995-2002 | Available till 2002. | ||||
| Type of institution | Y | Annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-1997 | Biennial. From 2002,annual. |
Even years till 2001 | |||
| Function | Y | Annual | ||||
| Qualification | Y-1995 | Available from 1995 till 2001 for odd years. From 2020 annual | Even years till 2021 | |||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y | Annual | ||||
| FORD | Y-1995 | Biennial, available from 1995 till 2001 | Even years. | |||
| Type of institution | Y | Annual | ||||
| Economic activity | Y | Annual | ||||
| Product field | N | |||||
| Employment size class | Y | Annual |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-1995 | Biennial. From 2002, annual. |
Even years till 2001. | |||
| Function | Y | Annual | ||||
| Qualification | Y-1995-2001 Y-2007 |
Available from 1995 till 2001 for odd years. From 2007. | Even years. From 2002-2006 |
Implementation of ISCED 2011 | ||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y | Annual | ||||
| FORD | Y-1995-2001 | Biennial, available from 1995 till 2001. | Even years. | |||
| Type of institution | Y | Annual | ||||
| Economic activity | Y | Annual | ||||
| Product field | N | |||||
| Employment size class | Y | Annual |
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 |
|---|---|---|---|---|---|
| Extramural R&D | Y | Annual since 2003, previously biennial. | |||
| Number of R&D personnel in full-time equivalent (FTE) |
|
Implementation of ISCED 2011 |
|||
| Number of R&D researchers in full-time equivalent (FTE) |
|
Implementation of ISCED 2011 |
|||
| Intramural R&D Expenditure |
|
|
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'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Yes | HC (For researchers) | From 2020 annual |
| Yes | FTE (For researchers) | From 2007 annual |
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 errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non- response errors | ||||
| Total intramural R&D expenditure | : | 2 | 3 | 4 | 1 | : | +/- |
| Total R&D personnel in FTE | : | 2 | 3 | 4 | 1 | : | +/- |
| Researchers in FTE | : | 2 | 3 | 4 | 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 (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
Statistics on R&D Activities are a census operation, so there are no sampling errors.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | Not applicable | Not applicable | Not applicable |
| R&D personnel (FTE) | Not applicable | Not applicable | Not applicable |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| R&D personnel (FTE) | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame 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:
There are units that are misclassified in other sectors and units performing R&D not included in the DIRID.
b) Measures taken to reduce their effect:
Legal units with less than 10 employees not included in the DIRID are not sampled. In order to minimize the possible undercovering, the ICT survey is used to detect microenterprises not included in the DIRID but performing R&D activities in the reference period.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Frame misclassification rate
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
| By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
|---|---|---|---|---|---|
| Number or surveyed enterprises in the stratum (according to frame) | 879 | 2,720 | 2,158 | 806 | 6,563 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 281 | 493 | 330 | 123 | 1,227 |
| Misclassification rate | 0.320 | 0.181 | 0.153 | 0.153 | 0.187 |
| By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
| Number or surveyed enterprises in the stratum (according to frame) | 2,972 | 3,137 | 1,308 | 657 | 8,074 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 1,062 | 944 | 299 | 113 | 2,418 |
| Misclassification rate | 0,357 | 0,301 | 0,229 | 0,172 | 0,299 |
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:
R&D concepts are very complex, so measurement errors are usual.
b) Measures taken to reduce their effect:
Survey inspectors are responsible for theoretical and practical training of the staff involved in field work, and for controlling work relating to the collection of information. To this purpose, manuals and training documents are available.
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 satisfying by computing the weighted and un-weighted response rate.
Definition:
Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100
13.3.3.1.1. Unit non-response rates by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| Number of units with a response in the realised sample | 5,019 | 6,414 | 3,909 | 1,545 | 16,887 |
| Total number of units in the sample | 6,457 | 6,931 | 4,060 | 1,570 | 19,018 |
| Unit Non-response rate (un-weighted) | 0.777 | 0.925 | 0.963 | 0.984 | 0.888 |
| Unit Non-response rate (weighted) | 0.588 | 0.867 | 0.861 | 0.946 | 0.778 |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 7,373 | 9,514 | 16,887 |
| Total number of units in the sample | 7,897 | 11,121 | 19,018 |
| Unit Non-response rate (un-weighted) | 0.934 | 0.855 | 0.888 |
| Unit Non-response rate (weighted) | 0.849 | 0.730 | 0.778 |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.3.3.1.3. Recalls/Reminders description
There are two written official reminders before the enterprise is fined, both for census or sample, as the completion of the survey is mandatory for all legal units. Nevertheless, the legal unit can be contacted by phone, fax or e-mail during the process of data collection.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No non-response survey is carried out. |
|---|---|
| Selection of the sample of non-respondents | |
| Data collection method employed | |
| Response rate of this type of survey | |
| The main reasons of non-response identified |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 11.4% | 11.8% | 12.1% |
| Imputation (Y/N) | Y | Y | Y |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used | In case the legal unit cannot be contacted by any means to obtain this information, individual estimation is carried out for every unit using historical R&D data of the legal unit and/or external information available (concerning financial support of R&D projects) | In case the legal unit cannot be contacted by any means to obtain this information, individual estimation is carried out for every unit using historical R&D data of the legal unit and/or external information available (concerning financial support of R&D projects) | In case the legal unit cannot be contacted by any means to obtain this information, individual estimation is carried out for every unit using historical R&D data of the legal unit and/or external information available (concerning financial support of R&D projects) |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | Limited |
| Total R&D personnel in FTE | Limited |
| Researchers in FTE | Limited |
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 | The process starts with the collection of the questionnaire and the computer coding, both for census and sample. The information can be received either by paper questionnaire (by postal mail, fax…) or using the on-line tool available for this purpose at INE's webpage. If the questionnaire is received by paper, it is manually recorded by the agent. In this stage, the program used (IRIA) provides the agent with information about illogical or inconsistence errors that can occur as well as items non-answered. Therefore, the interviewer can correct the register adequately and contact the legal unit to request the proper information needed. After data entry is finalized, files are sent to the S&T Unit, where a second data checking is carried out in order to minimize the processing errors. In this stage, SAS programs are used for a second checking of logical and consistency errors, as well as comparing data with information available of previous years or other data sources. The program used for this stage is CS-PRO, as a tool for interfacing with the questionnaire, accessing extended information about the legal unit and making the appropriate changes. Legal unit can be contacted again by phone, fax or e-mail if necessary |
|---|---|
| Estimates of data entry errors | - |
| Variables for which coding was performed | - |
| Estimates of coding errors | - |
| Editing process and method | In a first stage, we checked two different types of errors, both for census and sample: -Firstly, detecting out of range or invalid values produced in the editing process or due to a mistake in the completion of the questionnaire. -Secondly, detecting inconsistent values produced in the editing process or due to a mistake in the completion of the questionnaire. These errors include also the detection of item non-response. This stage starts with the reception of the questionnaire in the Unit for Survey Collection. When the field work is finished, the files are sent to the Central Offices, where once again the data is checked and validated, using our own SAS programs, although the legal unit can still be contacted by phone, fax or mail. Once the treatment of micro-data is done, another checking at macro-level is carried out. |
| Procedure used to correct errors | Mainly re-contact with the information provider, logical relations between different questions, checks against other variables available (historical R&D data, annual accounts, web sites…). Imputation is the method used in case that contact is not possible. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: 31 October 2024
- Lag (days): 305
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 30 June 2025
- Lag (days): 547
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No deviations from recommendations
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| 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). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation |
15.2. Comparability - over time
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
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) | 2021, 2002, 1980 | 2021: new unit Enterprise 2002:Data include the occasional and the systematic R&D. 1980: A substantial proportion of the growth in resources devoted to R&D by the enterprise sector is due to the considerable increase (more than 11%) in the number of firms responding to the survey |
|
| Function | |||
| Qualification | 2013 | 2013: ISCED 2011 is used for the first time | |
| R&D personnel (FTE) | 2021, 2002, 1980 | 2021: new unit Enterprise 2002:Data include the occasional and the systematic R&D. 1980: A substantial proportion of the growth in resources devoted to R&D by the enterprise sector is due to the considerable increase (more than 11%) in the number of firms responding to the survey |
|
| Function | |||
| Qualification | 2013 | 2013: ISCED 2011 is used for the first time | |
| R&D expenditure | 2021, 2002, 2000, 1980 | 2021: new unit Enterprise 2002: Data include the occasional and the systematic R&D. 2000: From 1995 to 1999 inclusive, units whose main economic activity is ISIC 73 (research and development) are classified in the industry that benefits directly from the R&D. As from 2000, this information is not available and R&D data are classified according to the main activity of the enterprise. Accordingly, the expenditure of R&D enterprises is grouped globally under ISIC 73, resulting in a break in series for all industries for that year. 1980:A substantial proportion of the growth in resources devoted to R&D by the enterprise sector is due to the considerable increase (more than 11%) in the number of firms responding to the survey. |
|
| Source of funds | |||
| Type of costs | |||
| Type of R&D | |||
| Other |
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
Even years for BES, the questionnaire is embedded in the “Encuesta sobre Innovacion en las Empresas” (CIS)
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. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
On the one hand, the classification used for R&D data collection activities is compatible with the SNA institutional classification, with the exception of the higher education sector, which is identified as a separate sector because of its prominence in R&D activities.
On the other hand, R&D data in the SNA calculations allows, apart from translating R&D expenditure data into a SNA compatible format, computing R&D capital stock and its appropriate deflators.
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 |
|---|---|---|---|---|---|
| R&D expenditure | odd years CIS is not collected | ||||
15.4. Coherence - internal
Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.
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 (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 12,615,739 | 134,793 | 71,235 |
| Final data (delivered T+18) | 12,615,739 | 134,793 | 71,235 |
| Difference (of final data) | 0 | 0 | 0 |
Comments :
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 54,846 | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 55,978 |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use / person / day | |
|---|---|---|
| Staff costs | Not available | Not available |
| Data collection costs | Not available | Not available |
| Other costs | Not available | Not available |
| Total costs | 1,559,262 | Not available |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs :
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | Not available | |
| Average Time required to complete the questionnaire in hours (T)1 | Not available | |
| Average hourly cost (in national currency) of a respondent (C) | Not available | |
| Total cost |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
We collect data through a survey. It is a combination of census and sample. Census data cover legal units included in the register of the R&D-performing enterprises (DIRID).
Sample survey cover legal units with 10 or more employees and activity classes of Innovation survey, as this survey is carried out together with R&D survey even years. Legal units with more than 200 employees are studied exhaustively.
The questionnaires are launched in May; data collection is carried out till September; and the first results are published in November.
18.1.2. Sample/census survey information
| Sampling unit | Legal unit |
|---|---|
| Stratification variables (if any - for sample surveys only) | Census data cover legal units included in the register of the R&D-performing enterprises (DIRID) as well as legal units with more than 200 employees. Sample survey covers legal units with 10 or more employees and economic activity classes of Innovation survey, as this survey is carried out together with R&D survey even years |
| Stratification variable classes | Economic activity, size and region as used for Innovation survey. |
| Population size | 3,207,580 |
| Planned sample size | |
| Sample selection mechanism (for sample surveys only) | In order to give data at the level of the Statistical Enterprise Unit (SUE), indirect sampling is applied, in the sense that results are given by SUE from the sample of Legal Units (LU). Systematic selection randomly started is executed for extracting the legal unitssample. |
| Survey frame | The Central Businesses Directory (DIRCE) collects all Spanish businesses in a single directory and it is used for extracting the sample. Besides, the DIRID collects the census part with the R&D performing enterprises. |
| Sample design | The sample part is extracted from the DIRCE (official, up-to-date, statistical business register), excluded the legal units which were known as actual or potential R&D performers from the DIRID, by crossing the following variables: size, economic activity and region, in order to cover other possible legal units that are engaged in R&D activities and not included in the census part. Consequently, units comprising the DIRID are not considered in the population of extraction, as they are studied exhaustively. For the sample part that is extracted from the DIRCE, a stratified design is carried out, similar to that of other years, based on a LU sample. All LUs with 200 or more employees are exhaustively investigated. Smaller LUs still relevant to the survey are also exhaustively; while the rest are stratified by autonomous community, main branch of economic activity and size group, according to number of employees. In each stratum, a random sample is obtained, of a size between proportional and uniform, maintaining the elevation factors of the previous survey. For the sample par this yeart, a questionnaire has only been sent to approximately 51% of the sample. For the rest of the units that complete the random sample, data or incidence from the sample of the previous year has been repeated |
| Sample size | 18,889 legal units |
| Survey frame quality | Comparison of units with the same ID to explore repeated units in different sectors. Reclassification of units in a difference sector according to the Frascati Tree. |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | No such data collection is carried out. |
|---|---|
| Description of collected data / statistics | Not applicable |
| Reference period, in relation to the variables the administrative source contributes to | Not applicable |
| Variables the administrative source contributes to | Not applicable |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
| Realised sample size (per stratum) | Census |
|---|---|
| Mode of data collection | The main data collection method is via online questionnaire. However, it is also possible to respond by mailed questionnaire. |
| Incentives used for increasing response | No incentives |
| Follow-up of non-respondents | There are two written official reminders before the enterprise is fined, as the completion of the survey is mandatory for all legal units. Nevertheless, the legal unit can be contacted by phone, fax or e-mail during the process of data collection. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No replacement |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 88.79% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | No non-response analysis is carried out. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | ite_cues23.pdf |
| Other relevant documentation of national methodology in English: | metoi+d23_en.pdf |
| Other relevant documentation of national methodology in the national language: | metoi+d23.pdf |
Annexes:
metoi+d23.pdf
ite_cues23.pdf
metoi+d23_en.pdf
18.4. Data validation
The population coverage is basically based on the DIRID, whick is complemented with sampling.
The responses rate are checked.
Statistics are compared both over time and between regions.
A micro and macro editing is performed in order to capture inconsistencies using CSPRO and SAS programs.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)
18.5.1.1. Imputation rate by Size class
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | 12.44% | 10.24% | 13.01% | 10.67% |
| 10-49 employees and self-employed persons | 11.54% | 11.23% | 11.70% | 11.35% |
| 50-249 employees and self-employed persons | 10.93% | 12.21% | 11.51% | 12.70% |
| 250-and more employees and self-employed persons | 9.16% | 9,39% | 10.05% | 10.29% |
| TOTAL | 11.40% | 10.96% | 11.83% | 11.31% |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | 9.66% | 10.03% | 9.98% | 10.28% |
| Services2) | 12.81% | 11.61% | 13.34% | 12.03% |
| TOTAL | 11.40% | 10.96% | 11.83% | 11.31% |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
| Data compilation method - Final data | The R&D survey is carried out annually. |
|---|---|
| Data compilation method - Preliminary data | Preliminary data is sent to Eurostat in T+10 according to Regulation, and it is compiled on the basis of data collection for the reference year. The difference between preliminary and final data relays on the ongoing process of validation and calculation of weighting factors. |
18.5.3. Measurement issues
| Method of derivation of regional data | According to Frascati Manual, with a dedicate section in the questionnaire. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Those who compile the statistics use their own assumptions |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Enterprises do not include VAT in R&D expenditure in view of the VAT accounting procedures followed by Spanish enterprises. Accordingly, VAT is not included in the R&D expenditure of other sectors. Depreciation is also excluded in the measurement of expenditures in all sectors. |
18.5.4. Weighting and estimation methods
| Weight calculation method | Initially, the weighting factors are : Please, see the attached document for further information ("anex_weightcalc"). |
|---|---|
| Data source used for deriving population totals (universe description) | The Central Businesses Directory (DIRCE) |
| Variables used for weighting | The population has been stratified by crossing the following variables: b) the size of the company c) the main activity (NACE) d) the Autonomous Community where it is located (NUTS level2) |
| Calibration method and the software used | Calibration so that the estimator of the total R&D expenditure at the LU level coincides with the estimator at the SUE level |
| Estimation | Not applicable |
Annexes:
calculation of weight factors
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise 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 business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
29 October 2025
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993,
The statistical unit is the enterprise but the responding unit and the observation unit and the sample unit is the legal unit.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
Not requested. R&D statistics cover national and regional data.
All questions and indicators refer to the 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 expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
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.
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
