1.1. Contact organisation
NATIONAL INSTITUTE OF STATISTICS ROMANIA
1.2. Contact organisation unit
DEPARTMENT OF SHORT TERM ECONOMIC INDICATORS STATISTICS
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
16 Libertatii Bvd., Bucharest 5, ROMANIA, Postal code 050706
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
3.1. Data description
Statistics on 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.
R&D definition used is in line with the Frascati Manual (FM).
Research and development is defined as any systematic and creative activity initiated to increase the volume of knowledge, including knowledge about man, culture and society and the use of this knowledge for new applications.
The research-development activity includes the technological design.
Does not include: market research activities, industrial and agricultural micro-production (except execution activities, prototypes, experimental installations, pilot stations), production and related activities, education and training activities, information services, general collection data, testing and standardization, patenting and licensing work, feasibility studies, specialized medical services, regular software development, industrial innovation (other than research and development), policy studies (application of research results -development to evaluate government policies).
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
The business enterprise sector includes all firms, organisations and institutions whose primary activity is the market production of goods and services (other than higher education sector) for sale to the general public at an economically significant price. The private non-profit institutions mainly serving them are included in Private Non-Profit sector. |
|---|---|
| Hospitals and clinics | The higher education sector includes university hospitals and medical clinics. For some of these, as well as for other types of medical center, there are problems of delimitation between R&D activities and health activities and in these cases no data is available on R&D expenditures and personnel. |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | Not applicable. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviations from FM; personnel is not included but expenditure is included. |
|---|---|
| External R&D personnel | Starting with 2018 reference year, new questions related External R&D Personnel; External R&D personnel included in personnel by occupation, but separately by employment status. |
| Clinical trials: compliance with the recommendations in FM §2.61. | Not included (clinical trials are included in Higher Education Sector); Included only private business medical clinics with R&D activity. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available |
|---|---|
| Payments to rest of the world by sector - availability | Available |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Available taking in consideration the specific of reporting unit. |
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 | Starting with 2011, we included in questionnaire a specific question related to extramural expenditure. Starting with 2018, we included in questionnaire a specific questions for intramural and extramural current costs related R&D personnel. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Some difficulties to distinguish and understand for respondents the new indicators for External R&D personnel expenditure. |
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 | In line with FM. |
| Type of R&D | In line with FM. |
| Type of costs | In line with FM, not detailed breakdown of costs. |
| Economic activity of the unit | Main economic activity of unit. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Main economic activity of unit. |
| Product field | Not included. |
| Defence R&D - method for obtaining data on R&D expenditure | Data is obtained in the survey questionnaire. Data for Defense makes reference only to the expenditure for civilian purpose. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Data refer to end of period. |
|---|---|
| Function | Data compatible with ISCO-08. |
| Qualification | No difficulties. |
| Age | No difficulties. Not included. |
| Citizenship | We assimilate the citizenship with the origin country. Starting with 2011, reference year, not included in national BES questionnaire specific question related this. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year |
|---|---|
| Function | Data compatible with ISCO-08. |
| Qualification | No difficulties. |
| Age | No difficulties. Not included. |
| Citizenship | We assimilate the citizenship with the origin country. Starting with 2011, reference year, not included in national BES questionnaire specific question related this. |
3.4.2.3. FTE calculation
The respondent unit calculates the hours worked in research projects and computes in full time equivalent according with methodology described in 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, if there are deviations please explain.
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 national target population consists of all legal units reporting R&D activities in previous R&D survey and all units with R&D activities (continuous or occasionally, know and unknown) selected from innovation survey (CIS), labour forces survey (LFS) and statistical business survey (SBS). | Not applicable. |
| Estimation of the target population size | Aproximative 11000 units according with definition of national target population. | Not applicable. |
| Size cut-off point | Without size cut-off point. | Not applicable. |
| Size classes covered (and if different for some industries/services) | According with FM and without differences for some industries/services. | Not applicable. |
| NACE/ISIC classes covered | According with FM, NACE classification. | Not applicable. |
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 | All enterprises known or supposed to perform R&D which sale goods or services to general public and other firms which declared performing R&D activity in other statistical surveys. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | The data source was the register of enterprises performing R&D activity, the list of enterprises receiving government grants for R&D activity, the list of enterprises which declared R&D activity in the previous survey, the list of enterprises performing R&D activity which took part to trade fairs and exhibitions. Another method to identify unknown units was internet. |
| Inclusion of units that primarily do not belong to the frame population | |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | Efforts to include unknown enterprises performing R&D are made. A combined R&D and innovation survey carry out in 2014 determined identification of new R&D performers. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | 3541 |
| Systematic exclusion of units from the process of updating the target population | We did not excluded any units. |
| Estimation of the frame population | 57772 |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested.
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.
Reference period is the calendar previous year - 2023 reference 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
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:
- Confidentiality protection required by law:
No deviations from secure procedure.
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data and repealing Directive 95/46 / EC (General Regulation on data protection) National Legislation on Statistical Confidentiality
Law no. 190 of 18 July 2018 on measures to implement Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of these data and repealing Directive 95/46 / EC (General Data Protection Regulation)
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
LAW no. 363 of December 28, 2018 on protection natural persons regarding the processing of personal data by the competent authorities for the purpose of preventing, detecting, investigating, prosecuting and combating crime or the execution of punishments, educational and security measures, and regarding the free movement of such data.
Law no. 102/2005 on the establishment, organization and functioning of the National Authority for the Supervision of Personal Data Processing, with subsequent amendments and completions.
- Confidentiality commitments of survey staff:
A confidentiality certificate agreement is signed upon employment, where the official terms of confidentiality are established.
7.2. Confidentiality - data treatment
Primary confidentiality:
- The rule of three (all cells with 3 and less units);
- The rule of dominance unit.
Secondary confidentiality:
- Disclosure by subtraction (differencing).
8.1. Release calendar
On the NIS website there are two calendars one for the press releases and the other for publications; both of them are accessible to the general public.
The final data are target to be published in press release and also in national publication 11 months after the end of the reference year (in November).
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
For NIS Romania this is:
- Publication Catalog - for publications
- Press Releases - for press release
8.3. Release policy - user access
The NIS has on the web page a section “Calendar of press releases”, with links to the monthly lists of publications planned for the current year. Each monthly list is sorted by date of publication and contains a brief description of the statistics to be provided. The monthly calendar is established for the following year in December of the previous year. NIS publishes annually on the site the calendar of press releases, calendar based on the terms of the Annual National Statistical Program and contains: title of the press release, reference period, date of issue. The monthly calendar is established and posted on the NIS website from December of the previous year. The calendar of press releases on the NIS website covers only the statistics published by the NIS.
In the event of a change in the broadcast date, this is announced 24 hours before the calendar date, specifying the new broadcast date.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
The frequency of R&D data dissemination at the national data is annual.
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 | Yes | The NIS press releases are sent directly by the NIS Press Office to the accredited media and ministries and are posted online at 9:00 a.m. in the dedicated section. Also, on the site there is the archive of these press releases, structured on statistical topics. |
| Ad-hoc releases | No |
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 | Yes | R&D data are included in Romanian Yearbook, "Territorial statistics" publication, "Romania in figures" publication. Web-site of Romanian National Institute of Statistics |
| Specific paper publication (e.g. sectoral provided to enterprises) | Yes |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Data for BES sector of performance are available in Database TEMPO ONLINE
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 | NIS does not have a "Safe center" for access to microdata. Due to the confidential nature of microdata, direct access to anonymized data is offered only for scientific research projects according to European and national legislation in the field, through an access contract. Legal framework The current European and national legal framework enables access to anonymised microdata available only for scientific purposes. The access is, in principle, limited to universities, research institutes, national statistical institutes, central banks within the EU and euro area countries, as well as to the European Central Bank. Individuals cannot be granted direct access to microdata. |
|---|---|
| Access cost policy | Not applicable |
| Micro-data anonymisation rules | Not applicable. |
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 | Yes | ||
| Data prepared for individual ad hoc requests | Yes | ||
| Other | No |
1) Y – Yes, N - No
10.6. Documentation on methodology
A confidentiality certificate agreement is signed upon employment, where the official terms of confidentiality are established.
Data are accompanied of metadata describing the indicators and the calculation thereof.
To all other questions regarding the methodology or the manner of designing the tables and the data we respond whenever necessary.
In the TEMPO online database, each indicator is accompanied by the related metadata.
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.) | Metadata, graphs, methodological notes and quality report. |
|---|---|
| Requests on further clarification, most problematic issues | Clarifications regarding R&D expenditure and sources of funds. To all other questions regarding the methodology or the manner of designing the tables and the data we respond whenever necessary. |
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).
The quality quantifies how well the statistics are fit for their purpose. The criteria to judge statistical quality correspond to: relevance, accuracy and reliability, timeliness and punctuality, accessibility and clarity, comparability, coherence and cost and burden. The aim is to reduce the errors of coverage, the non-response, the measurements errors, the processing errors.
The legal acts and other document related quality assurance are: Legislation concerning quality assurance, Task Forces or Working Groups, Law No. 226/2009 on the organisation and functioning of official statistics in Romania, Internal procedures, European Statistics Code of Practice, Quality Guidelines for Romanian Official Statistics
Statistical practices used to compile national R&D data for government sector of performance are in compliance with Frascati Manual recommendations.
11.2. Quality management - assessment
National methodology applies harmonized concepts and definitions according with Frascati Manual. As it is recommended, we include in the national R&D survey on the BES sector all enterprises known or supposed to perform R&D.
The methodology was improved through the identification of units belonging BES sector of performance.
The R&D survey for BES sector of performance is conducted to provide knowledge about R&D indicators (mandatory and optional) and to allow comparisons with other European countries.
At every R&D survey for BES sector of performance , before the finalisation of the national questionnaire, the main national users and the representatives of regional NIS departments are invited to a discussion, by Romanian NIS, to express their opinions and suggestions about the final national questionnaire (clarity, difficulties, understanding and perception of the questions and about other national statistical needs).
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 - Institutions | European Commission | Data used for the European R&D statistics and its further development. |
| 1 - Institutions | Governmental departments: Ministry of National Education, Authorities for Regional Development, Ministry of Economy, Ministry of Public Finances. | Data used for R&D national and regional strategies and policies, sectoral comparisons, publications,training. |
| 1 - Institutions | International organizations: OECD | Data used for international comparability and publications. |
| 2 - Social actors | Scientific institutes and universities | Data used for analyses. |
| 2 - Social actors | Trade unions | Data are used for strategies and policies. |
| 2 - Social actors | Employer’s associations | Data are used for strategies, policies and training. |
| 3 - Media | International or regional media | Data used for analyses and comments to the general public. |
| 4 - Researchers and students | Researchers and students | Data used for analyses, scientific projects and access to specific data. |
| 5- Enterprises or businesses | Enterprises or businesses | Market analyses, marketing strategies, consultancy services. |
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 | A user satisfaction survey is conducts in National Institute of Statistics. This survey is addressed to a selection of users of all statistical fields.Last one survey in 2024. |
|---|---|
| User satisfaction survey specific for R&D statistics | National user satisfaction survey is not specific to R&D statistics, but we have comments received from the large user’s categories. |
| Short description of the feedback received | We received feedback and detailed requirements from national users regarding R&D expenditures, detailed regional R&D expenditures, number of personnel involved in R&D projects and researchers by age groups. |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
Data completeness of final mandatory data are very good and good. National questionnaire survey for BES sector of performance included also mandatory and optional R&D indicators.
Starting with 2019 year of reference, we stopped collecting data by NACE industry orientation indicator.
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-1993 | annual | 1993,1994 only current expenditure | introduced total expenditure | 1995 | to be in line with Frascati Manual |
| Type of R&D | Y-1995 | annual | ||||
| Type of costs | Y-1995 | annual | ||||
| Socioeconomic objective | Y-1995 | annual | ||||
| Region | Y-2000 | annual | ||||
| FORD | Y-1999 | annual | ||||
| Type of institution | Y-2019 | 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-1999 | annual | ||||
| Function | Y-1993 | annual | ||||
| Qualification | Y-1993 | annual | 1993-2003 first stage tertiary education theoretical (ISCED-5A) and practical (ISCED-5B) were surveyed together; since 2003 we have comparable data as they were surveyed separately. | |||
| Age | Y-1993 | annual | 1993-2002 | new breakdown of the ages corresponding to Frascati Manual:up to 25, 25-34, 35-44,45-54, 55-64, 65 and more | 2003
|
new breakdown of the ages corresponding to Frascati Manual:up to 25, 25-34, 35-44,45-54, 55-64, 65 and more |
| Citizenship | Y-2004 | annual | ||||
| Region | Y-2000 | annual | ||||
| FORD | Y-1999 | annual | ||||
| Type of institution | Y-2019 | annual | ||||
| Economic activity | Y-1995 | annual | ||||
| Product field | Y-2010-2018 | annual | until 2019 | |||
| Employment size class | Y-2002 | 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-1999 | annual | ||||
| Function | Y-1993 | annual | ||||
| Qualification | Y-1993 | annual | 1993-2003 first stage tertiary education theoretical (ISCED-5A) and practical (ISCED-5B) were surveyed together; since 2003 we have comparable data as they were surveyed separately. | |||
| Age | No | |||||
| Citizenship | No | |||||
| Region | Y-2000 | annual | ||||
| FORD | Y-1999 | annual | ||||
| Type of institution | Y-2019 | annual | ||||
| Economic activity | Y-1995 | annual | ||||
| Product field | Y-2010-2018 | annual | until 2019 | |||
| Employment size class | Y-2002 | 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 |
|---|---|---|---|---|---|
| Number of scientific meetings organised at national level with international participation. | 2000-2010 | annual | |||
| Training courses of R&D personnel. | 2000-2010 |
annual | |||
| Publications papers by scientific programs according with NABS classifications (domestic level and international level). | 2000-2010 | annual | |||
| Number of R&D projects by NABS programs and by sources of funds. | 2000-2010 | annual | |||
| Breakdown of public funds by type of national R&D projects. | 2000-2010 | annual |
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 | annual |
| YES | FTE | annual |
| YES | both | annual |
| YES | expenditure | 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,
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 | 4 | 4 | 4 | 5 | 5 | - | + |
| Total R&D personnel in FTE | 4 | 4 | 4 | 5 | 5 | - | + |
| Researchers in FTE | 4 | 4 | 4 | 5 | 4 | - | + |
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
Soft opensource "R", versiunea 4.5.1.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | 0.0417 | 0.038 | 0.040 |
| R&D personnel (FTE) | 0.0360 | 0.027 | 0.031 |
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 | 0.022 | 0.016 | 0.021 | 0.027 | 0.040 |
| R&D personnel (FTE) | 0.026 | 0.016 | 0.020 | 0.024 | 0.031 |
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:
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.
b) Measures taken to reduce their effect:
The magnitude of the error is computed as a percentage of the relative difference between the indicator's expected "true values" based on the target population and the indicators observed values in the frame population.
Magnitude of error(%)=(Observed Value-True Value)/True Value (%)
Note:non-responding units are not considered omitted
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) | 934 | 1851 | 1637 | 703 | 5125 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 86 |
113
|
83 | 68 | 350 |
| Misclassification rate | 0.09208 |
0.06105 | 0.05070 | 0.09673 | 0.06829 |
| 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) | 1281 | 2583 | 1342 | 388 | 5594 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 111 | 146 | 46 | 16 | 319 |
| Misclassification rate | 0.08665 | 0.05652 | 0.03428 | 0.04124 | 0.05703 |
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:
Few processing or measurement errors.
b) Measures taken to reduce their effect:
The measures for reducing errors consisted in selection of staff with knowledge in R&D methodology and experience in data entry and validation checks for online questionnaires. Also, we developed detailed methodological notes regarding the new terms and their definition.
We recontact the respondents for supplementary clarifications.
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 | 1982 | 4073 | 2804 | 1021 | 9880 |
| Total number of units in the sample | 2215 | 4434 | 2979 | 1091 | 10719 |
| Unit Non-response rate (un-weighted) | 0.1052 | 0.0814 | 0.0587 | 0.0642 | 0.0783 |
| Unit Non-response rate (weighted) | 0.1073 | 0.1211 | 0.0970 | 0.0638 | 0.1139 |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 4771 | 5109 | 9880 |
| Total number of units in the sample | 5125 | 5594 | 10719 |
| Unit Non-response rate (un-weighted) | 0.0691 | 0.0867 | 0.0783 |
| Unit Non-response rate (weighted) | 0.1060 | 0.1197 | 0.1139 |
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
Two reminders.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | It was not necessary a non-response survey. |
|---|---|
| Selection of the sample of non-respondents | Not applicable |
| Data collection method employed | Not applicable |
| Response rate of this type of survey | Not applicable |
| The main reasons of non-response identified | Not applicable |
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) (%) | 0 | 2.5 | 2.1 |
| Imputation (Y/N) | Not applicable | Not applicable | Not applicable |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used | Not applicable | Not applicable | Not applicable |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | 0 |
| Total R&D personnel in FTE | 0 |
| Researchers in FTE | 0 |
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 | Data entry method used is data keying and responses through electronic online questionnaire. |
|---|---|
| Estimates of data entry errors | 0,1% |
| Variables for which coding was performed | The variables for which coding was performed have been: Product field. |
| Estimates of coding errors | 0,1% |
| Editing process and method | The editing method is a combination of automated and manual methods. We are applying a value range checked for every variable and compared with data from previous collection of the same statistics. |
| Procedure used to correct errors | Re-contact units. |
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: 15 November 2024.
- Lag (days): 319.
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 29 November 2024
- Lag (days): 333
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) | no delay | no delay |
| 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
Previous 1993 R&D data could not be recomputed according with Frascati Manual due to the inclusion of other activities that did not belonged to Frascati Manual.
Since 1993 R&D data are in concordance to international classifications and respect recommendations of Frascati Manual except the following:
- military defense R&D ( defense R&D data include only civil defense R&D);
- R&D data for sector of performance abroad (and funded nationally-Table CE13-Joint OECD/Eurostat questionnaire).
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 | |
| Researcher | FM2015, §5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| 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 | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No | |
| 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 | |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| 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 | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No |
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 | |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | |
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No | |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No |
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) | NONE | ||
| Function | NONE | Not applicable. | |
| Qualification | 2003 | 1993-2003 first stage tertiary education theoretical (ISCED-5A) and practical (ISCED-5B) were surveyed together; since 2003 we have comparable data as they were surveyed separately. | |
| R&D personnel (FTE) | NONE | ||
| Function | NONE | Not applicable. | |
| Qualification | 2023 | 1993-2003 first stage tertiary education theoretical (ISCED-5A) and practical (ISCED-5B) were surveyed together; since 2003 we have comparable data as they were surveyed separately. | |
| R&D expenditure | NONE | ||
| Source of funds | 1993,1994 | During 1993 - 1994 we have data breakdown only by sources of funds for the current costs. | |
| Type of costs | 1993,1994 | There are included only current costs and not sub-total capital expenditures. | |
| Type of R&D | 1993,1994 | We have only total expenditures and not breakdown by sectors of performance. | |
| Other | 1996, 2011 |
|
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
Yes.
The same as in the odd years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. 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
R&D statistics for BES sector of performance are compiled in according with institutional BES sector as defined based on the System of National Account (SNA).
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 |
|---|---|---|---|---|---|
| Not applicable. | |||||
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) | 5111317 | 13754 | 7144 |
| Final data (delivered T+18) | 5111317 | 13754 | 7144 |
| 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) | 184639 | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 1058937 |
(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 | |
| Data collection costs | not available | |
| Other costs | not available | |
| Total costs | 1,110,189.74 estimate only for NIS central staff |
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 : Total costs is calculated for reference year 2024. Not available for reference year 2023.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 198 | Dedicated question related number of respondents in BES questionnaire. |
| Average Time required to complete the questionnaire in hours (T)1 | 12,6 | Dedicated question related time required to complete questionnaire in BES questionnaire. |
| Average hourly cost (in national currency) of a respondent (C) | Not available | |
| Total cost | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
The type of survey is combined sample and census survey for continously or occasionally businesses units, known or assumed to perform R&D.
Enterprises with less than 100 employees are sample surveyed and enterprises with more than 100 employees are census surveyed.
The main variables are:
- number of R&D employees in HC at 31 December and FTE aggregated by occupation, qualification, by sex;
- researchers- by sex;
- R&D expenditures- by type of costs, by sources of funds, by type of research, by sources and type of funds;
- R&D expenditures - payments received from abroad by type of funds institutions.
Data collection: August - after reference year.
Data processing, validation, comparison: September - after reference year.
Data dissemination (Press Release Communicate, Publication, Data base on line, Yearbook) - November after reference year.
18.1.2. Sample/census survey information
| Sampling unit | Enterprise unit. |
|---|---|
| Stratification variables (if any - for sample surveys only) | NACE+SIZE CLASS+REGION |
| Stratification variable classes | |
| Population size | 57772 units |
| Planned sample size | 11000 units |
| Sample selection mechanism (for sample surveys only) | SRS |
| Survey frame | |
| Sample design | In accordance with the requirements |
| Sample size | 10719 units |
| Survey frame quality | Good |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Information are collected from R&D survey only and not from administrative data. |
|---|---|
| Description of collected data / statistics | Not used these methods. |
| Reference period, in relation to the variables the administrative source contributes to | 2021 |
| 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) | 9880, at least 3 units/stratum |
|---|---|
| Mode of data collection | Web online portal or selfregister by paper. |
| Incentives used for increasing response | Not applicable for mandatory survey. |
| Follow-up of non-respondents | For non-responses, the units are contacted again for 2-3 times. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | In case for the non-response unit, it can be replaced with another known unit from the same stratum, if available. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 92.9% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Not applicable. |
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: | CD BES |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
For survey, data are collected online using the Portal WEB application and self - administrated method. We have an IT solution developed to find out measurement and processing errors occurred in different stages of the survey. The application was designed for online data collection and validation.
The IT solution allowed to perform online data entry and validation at unit level. Also, solution allowed to perform data entry and validation questionnaires received on paper by post/email from all our 42 counties.
The IT solution contained the following categories of logical sets to check:
- the primary data from the questionnaires;
- the logical flows among the questionnaire chapters;
- the data integrity and correctness;
- the data comparability between indicators related personnel and expenditures.
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
Not available
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 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 | ||||
18.5.1.2. Imputation rate by NACE
Not available
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | ||||
| Services2) | ||||
| TOTAL | ||||
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 | Every year we carry out a dedicated R&D survey. |
|---|---|
| Data compilation method - Preliminary data | In accordance with the Romanian Statistical Program approved by the Government and published in the Official Journal within 10 months of the reference period we provide data for the previous year which are final data. |
18.5.3. Measurement issues
| Method of derivation of regional data | Each unit from sample has a specific code in order to have regional identification. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Exclusion of VAT and depreciation. |
18.5.4. Weighting and estimation methods
| Weight calculation method | The method used for weights calculation was the calculation of the inverse of the sampling fraction using turnover and average number of employees |
|---|---|
| Data source used for deriving population totals (universe description) | The universe is formed of the enterprises belonging to the whole industry and services selected from National Business Register. The data source for the totals is represented by the population of the enterprises used in Structural Business Survey (SBS). |
| Variables used for weighting | The variables used for weighting were turnover and the number of employees. |
| Calibration method and the software used | VFP software SEGuide |
| Estimation | Estimation is solved at the receiving of data in the period of validation after the comparison with Business Structural Survey and Labour Force Survey through the delivering of data provided, keeping the structure of the indicators obtained from units. |
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.
31 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, if there are deviations please explain.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
Not requested.
Reference period is the calendar previous year - 2023 reference 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,
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.
The frequency of R&D data dissemination at the national data is annual.
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.
