1.1. Contact organisation
State Data Agency (Statistics Lithuania)
1.2. Contact organisation unit
Knowledge Economy and Special Survey Statistics Division
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
29 Gedimino Ave., LT-01500 Vilnius, Lithuania
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
30 October 2025
2.1. Metadata last certified
30 October 2025
2.2. Metadata last posted
30 October 2025
2.3. Metadata last update
30 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.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
No differences from Frascati Manual (FM). |
|---|---|
| Hospitals and clinics | Enterprise type centres classified in the BES. |
| 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 | No differences from Frascati Manual (FM). |
|---|---|
| External R&D personnel | External R&D personnel are included in the Total R&D personnel (internal + external). However, detailed breakdowns (by sex, age, occupation, qualification, etc.) cover only internal personnel. |
| Clinical trials: compliance with the recommendations in FM §2.61. | No differences from Frascati Manual (FM). |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Not available |
|---|---|
| Payments to rest of the world by sector - availability | Not available |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Not available |
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) | No |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Not applicable |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not applicable |
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 | Source of funds follows the FM methodology. |
| Type of R&D | Basic research; Applied research; Experimental development |
| Type of costs | Current costs, R&D capital expenditure |
| Economic activity of the unit | Main econ. activity of the institution conducting the R&D activity. No divergences with ISIC/NACE classification. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Not collected |
| Product field | Not applicable. |
| Defence R&D - method for obtaining data on R&D expenditure | Not applicable. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | End of calendar year |
|---|---|
| Function | R&D personnel are divided into researchers and other R&D staff. Technicians cannot be separated. Data are available only for internal R&D personnel. |
| Qualification | Available only for internal R&D personnel |
| Age | Available only for internal R&D personnel |
| Citizenship | Not available since 2013 |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year |
|---|---|
| Function | R&D personnel are divided into researchers and other R&D staff. Technicians cannot be separated. Data are available only for internal R&D personnel. |
| Qualification | Not collected |
| Age | Not collected |
| Citizenship | Not collected |
3.4.2.3. FTE calculation
By FM recommendation.
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 | All enterprises known or supposed to perform R&D on a continuous or occasional basis | All real R&D performers from VAT declaration on R&D activities |
| Estimation of the target population size | 18836 enterprises. | Not applicable |
| Size cut-off point | Population from 10 employees, know R&D performers all. | No cut-off applied |
| Size classes covered (and if different for some industries/services) | In NACE Rev. 2 section 72, enterprises below 10 employees are also included. Data are available for size classes: 10–49, 50–249, 250–499, 500+. | All enterprise sizes are covered. |
| NACE/ISIC classes covered | All NACE classes are 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 is defined on the basis of a register of mainly known or supposed R&D performing enterprises. Additional enterprises are included in sampling frame, based on the data from the Statistical Business Register on their economic activity and number of employees. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Data from the previous statistical survey; information on enterprises receiving government grants or contracts for R&D; tax exemptions for R&D activities, and publicly available information (e.g. media). |
| Inclusion of units that primarily do not belong to the frame population | Such units are not included. |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | Enterprises previously not known to perform R&D are identified using the Statistical Business Register data on economic activity and employment, and are included annually when the frame is updated. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | 1,162 "new" R&D-performing enterprises were identified and included in the latest R&D survey. |
| Systematic exclusion of units from the process of updating the target population | Enterprises with fewer than 10 employees are systematically excluded from the target population. |
| Estimation of the frame population | 18836 enterprises. |
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.
R&D expenditure – EUR thousand;
R&D personnel – persons HC, FTE.
Reference period is 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 | Yes. National R&D statistics are governed by the general national statistical legislation. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes. Respondents are legally obliged to provide the requested statistical information under the national statistical 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: In the process of statistical data collection, processing and analysis and dissemination of statistical information, the State Data Agency fully guarantees confidentiality of the data submitted by respondents (households, enterprises, institutions, organisations and other statistical units), as defined in the Confidentiality policy guidelines of the State Data Agency.
- Confidentiality commitments of survey staff: Not applicable.
7.2. Confidentiality - data treatment
Statistical Disclosure Control Manual, approved by Order No DĮ-26 of 19 January 2024 of the Director General of the State Data Agency;
The State Data Governance Information System Data Security Regulations and Rules for the Secure Management of Electronic Information in the State Data Governance Information System, approved by Order No DĮ-163 of 20 August 2024 of the Director General of the State Data Agency.
8.1. Release calendar
Statistical information is published on the Official Statistics Portal according to the Official Statistics Calendar.
8.2. Release calendar access
8.3. Release policy - user access
Statistical information is prepared and disseminated under the principle of impartiality and objectivity, i.e. in a systematic, reliable and unbiased manner, following professional and ethical standards (the European Statistics Code of Practice), and the policies and practices followed are transparent to users and survey respondents.
All users have equal access to statistical information. All statistical information is published at the same time – at 9 a.m. on the day of publication of statistical information as indicated in the calendar on the Official Statistics Portal. Relevant statistical information is sent automatically to news subscribers.
The President and Prime Minister of the Republic of Lithuania, their advisers, the Ministers of Finance, Economy and Innovation, as well as Social Security and Labour of the Republic of Lithuania or their authorized persons, as well as, in exceptional cases, external experts and researchers have the right to receive early statistical information. The specified persons are entitled to receive statistical reports on GDP, inflation, employment and unemployment and other particularly relevant statistical reports one day prior to the publication of this statistical information on the Official Statistics Portal. Before exercising the right of early receipt of statistical information, a person shall sign an undertaking not to disseminate the statistical information received before it has been officially published.
Statistical information is published following the Official Statistics Dissemination Policy Guidelines and the Rules for Information Dissemination and Communication of the State Data Agency, approved by Order No DĮ-208 of 8 October 2024 of the Director General of the State Data Agency.
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 | N | |
| Ad-hoc releases | N |
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 | Y | Statistical information is published in publication ,,Lithuania in Figures". Publications |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Statistical indicators are published in the Database of Indicators (Science and technology -> Research and development (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 | The State Data Agency may, on the basis of contracts concluded with higher education institutions or research institutes, provide statistical microdata to their researchers for specific statistical analyses for research purposes. |
|---|---|
| Access cost policy | Access is provided free of charge. |
| Micro-data anonymisation rules | Statistical microdata are provided in accordance with the provisions specified in the Description of Procedure for Data Depersonalisation and Pseudonymisation. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1) | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | Aggregate figures | Statistical indicators are published in the Database of Indicators (Science and technology -> Research and development (R&D)). |
| Data prepared for individual ad hoc requests | Y | Micro-data, aggregate figures | Statistical information can also be provided upon individual requests (more information is available on the Official Statistics Portal, in section Services). |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Methodological documents are published on the Official Statistics Portal in the section Research and Development (R&D).
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.) | Methodological documents: Users oriented quality report. (In Lithuanian and English language) |
|---|---|
| Requests on further clarification, most problematic issues | No request |
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).
Quality of statistical information and its production process is ensured by the provisions of the European Statistics Code of Practice and ESS Quality Assurance Framework.
In 2007, a quality management system, conforming to the requirements of the international quality management system standard ISO 9001, was introduced at the State Data Agency. Main trends in activity of the State Data Agency aimed at quality management and continuous development in the institution are established in the Quality Policy.
Monitoring of the quality indicators of statistical processes and their results and self-evaluation of statistical survey managers is regularly carried out in order to identify areas which need improvement and to promptly eliminate shortcomings.
More information on assurance of quality of statistical information and its preparation is published in the Quality Management section on the State Data Agency website.
11.2. Quality management - assessment
The quality of the statistical results shall meet the requirements of accuracy, timeliness and punctuality, comparability and consistency. The results are compared with the results of the previous year. Outliers are identified and analysed. In case of significant discrepancies, dataproviders are contacted and reasons are determined. If inaccuracies are detected, data are corrected. The BES sector coverage is inaccordance to the Frascati Manual.
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 | European Commission (DGs, Secretariat General), European Council |
Data for analysis |
| 1-National level | Ministry of the Economy and Innovation, Ministry of Education, Science and Sport, Government Strategic Analysis Center (Strata), Innovation Agency Lithuania |
Data for the market analysis and formation R&D statistics policy |
| 1-National level | Innovation centre | Data for market analysis, marketing strategy, offer consultancy services |
| 4- Researchers and students | Researchers and students | Data for the science works, analysis |
| 3 - Media | International and national media | Data for publications |
| 5- Enterprises or businesses | Enterprises and other business organisations | Data for the own market analysis, their marketing strategy |
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 | Yes, overall satisfaction survey. |
|---|---|
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | The State Data Agency attaches great importance to strengthening relations with users. Users are given the opportunity to test questionnaires, submit their suggestions and requests via email and online, and regular meetings and training sessions with users are organized. |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
According to the Official Statistics Programme Part I, 100 per cent of statistical information is published.
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 | All compulsory data were delivered. |
| Obligatory data on R&D expenditure | All compulsory data were delivered. |
| Optional data on R&D expenditure | Some optional breakdowns were not collected due to low relevance or limited data availability. |
| Obligatory data on R&D personnel | All compulsory data were delivered. |
| Optional data on R&D personnel | Some optional breakdowns were not collected due to low relevance or limited data availability. |
| Regional data on R&D expenditure and R&D personnel | All compulsory data were delivered. |
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-2000 | Annual | ||||
| Type of R&D | Y-2000 | Annual | ||||
| Type of costs | Y-2000 | Annual | ||||
| Socioeconomic objective | N | |||||
| Region | Y-1995 | Annual | Lithuania NUTS 2 | 2018 | Lithuania NUTS 2 | |
| FORD | N | |||||
| Type of institution | N |
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-2000 | Annual | ||||
| Function | Y-1996 | Annual | ||||
| Qualification | Y-1996 | Annual | ||||
| Age | Y-2003 | Annual | ||||
| Citizenship | N | |||||
| Region | Y-2015 | Annual | Lithuania NUTS 2 | 2018 | Lithuania NUTS 2 | |
| FORD | N | |||||
| Type of institution | N | |||||
| Economic activity | Y-2000 | Annual | ||||
| Product field | N | |||||
| Employment size class | N |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-2000 | Annual | ||||
| Function | Y-2000 | Annual | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-2015 | Annual | Lithuania NUTS 2 | 2018 | Lithuania NUTS 2 | |
| FORD | N | |||||
| Type of institution | N | |||||
| Economic activity | Y-2000 | Annual | ||||
| Product field | N | |||||
| 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 |
|---|---|---|---|---|---|
| Not applicable | |||||
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 |
|---|---|---|
| Not available. | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| 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 | 2 | 1 | 5 | 3 | : | +/- |
| Total R&D personnel in FTE | 4 | 2 | 1 | 5 | 3 | : | +/- |
| Researchers in FTE | 4 | 2 | 1 | 5 | 3 | : | +/- |
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
Variance estimate is standard unbiased estimate of variance of direct (which do not uses auxiliary information) estimate of sum for simple random stratified sampling.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | 0.02 | 0.25 | 0.07 |
| R&D personnel (FTE) | 0.01 | 0.31 | 0.08 |
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 | 0 | 0.23 | 0.18 | 0.07 |
| R&D personnel (FTE) | 0 | 0 | 0.15 | 0.25 | 0.08 |
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.
- Description/assessment of coverage errors: Not applicable.
- Measures taken to reduce their effect: Not applicable.
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) | not applicable | not applicable | not applicable | not applicable | not applicable |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | not applicable | not applicable | not applicable | not applicable | not applicable |
| Misclassification rate | not applicable | not applicable | not applicable | not applicable | not applicable |
| 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) | not applicable | not applicable | not applicable | not applicable | not applicable |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | not applicable | not applicable | not applicable | not applicable | not applicable |
| Misclassification rate | not applicable | not applicable | not applicable | not applicable | not applicable |
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.
- Description/assessment of measurement errors: not applicable.
- Measures taken to reduce their effect: not applicable.
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 | 1261 | 907 | 566 | 219 | 2953 |
| Total number of units in the sample | 1285 | 917 | 570 | 219 | 2991 |
| Unit Non-response rate (un-weighted) | 1.87 | 1.09 | 0.70 | 0 | 1,27 |
| Unit Non-response rate (weighted) | 1.87 | 1.09 | 0.70 | 0 | 1.27 |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 978 | 1975 | 2953 |
| Total number of units in the sample | 988 | 2003 | 2991 |
| Unit Non-response rate (un-weighted) | 1.01 | 1.40 | 1.27 |
| Unit Non-response rate (weighted) | 1.01 | 1.40 | 1.27 |
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
E-mail reminders or calls by phone.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | Response rate 98.7 %, therefore non-response survey was not done. |
|---|---|
| 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) (%) | 1% | 1% | 1% |
| Imputation (Y/N) | N | N | N |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | Not applicable |
| Total R&D personnel in FTE | Not applicable |
| Researchers in FTE | Not applicable |
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 are collected electronically through the e-Statistics system, which includes built-in validation rules and automatic checks to prevent incorrect data entry. |
|---|---|
| Estimates of data entry errors | Negligible. Automatic controls in the e-Statistics questionnaire prevent most entry errors; remaining inconsistencies are detected during validation. |
| Variables for which coding was performed | Not applicable |
| Estimates of coding errors | Not applicable |
| Editing process and method | Data are subject to automated and manual logical checks at the microdata level. Outliers and inconsistencies are identified and verified directly with respondents. |
| Procedure used to correct errors | Detected errors are communicated to respondents by phone or email. Respondents correct the data themselves directly in the e-Statistics system. After corrections are submitted, the dataset is automatically revalidated before final approval. |
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: 27 September 2024
- Lag (days): 271
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 23 May 2025
- Lag (days): 509
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) | T+10 | T+18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay | not applicable | not applicable |
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
Comparable. No divergences from FM.
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 | Yes | Total R&D personnel are reported according to the formula: Internal + External. External R&D personnel are included in the total, but detailed breakdowns (by sex, age, occupation, qualification, etc.) refer only to internal personnel. |
| 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 | In the NACE Rev.2 section 72 census survey. In other NACE sections information about enterprises, with 10 or less employees and engaged in R&D activities are obtained from administrative sources. |
| 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 |
The frame is updated annually using previous survey data and the Statistical Business Register. Units are contacted electronically via the e-Statistics system. |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | Data collected through an electronic questionnaire in the e-Statistics system with built-in validation and consistency checks. |
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | Respondents are contacted by phone or email when clarification is needed. |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | Reminders are sent by email and phone. Non-response is rare because participation is mandatory. |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | Automatic and manual validation checks are performed to ensure internal consistency before dissemination. |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | Non-response is minimal (around 1%). Missing or inconsistent data are verified directly with respondents. |
| 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 | Both preliminary and final R&D data are compiled and published. |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | The survey is based on a combined approach — random and targeted sampling. Random sampling is applied to the general business population, while targeted selection ensures full coverage of known or likely R&D performers. |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | A mixed design combining random and targeted sampling is used. Stratified random sampling is applied by NACE and enterprise size, while known or potential R&D performers are fully included through targeted selection. |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | Online questionnaire consistent with national R&D data requirements and FM recommendations. |
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) | No | ||
| Function | No | ||
| Qualification | No | ||
| R&D personnel (FTE) | No | ||
| Function | No | ||
| Qualification | No | ||
| R&D expenditure | 2000 | R&D expenditure by financing sources is comparable only since 2000. | |
| Source of funds | No | ||
| Type of costs | No | ||
| Type of R&D | No | ||
| Other | No |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Odd and even years have the same data production.
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
The indicators of institutional sectors are internally coherent.
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 |
|---|---|---|---|---|---|
| No relevant indicators | |||||
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) | 318402 | 5479 | 3162 |
| Final data (delivered T+18) | 322308 | 5518 | 3204 |
| Difference (of final data) | 3906 | 39 | 42 |
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) | Not applicable | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not applicable |
(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 | 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 :
Not available
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 2953 | |
| Average Time required to complete the questionnaire in hours (T)1 | 0.78 h | |
| Average hourly cost (in national currency) of a respondent (C) | Not applicable | |
| Total cost | Not applicable |
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 R&D survey in the Business Enterprise Sector (BES) is conducted annually by the State Data Agency.
The survey covers enterprises that perform or finance research and experimental development (R&D) activities in the business sector.
The data collection is carried out through a mixed design, combining random sampling for the general enterprise population and targeted inclusion of known or likely R&D performers identified from previous surveys, administrative sources, and the Statistical Business Register.
Data are collected online via the e-Statistics system using an electronic questionnaire. The survey started in March and survey results are published in September.
The survey collects all variables required by Commission Implementing Regulation (EU) No 2020/1197, including:
-
R&D personnel (headcount and FTE) by function, qualification, and age;
-
Intramural R&D expenditure by source of funds, type of cost, and type of R&D;
-
Distribution of expenditure by socio-economic objectives and scientific fields.
18.1.2. Sample/census survey information
| Sampling unit | Enterprise |
|---|---|
| Stratification variables (if any - for sample surveys only) | Economic activity, number of employees |
| Stratification variable classes | NACE Rev. 2 classes; enterprise size groups: 0–9, 10–49, 50–249, 250 and more employees. |
| Population size | 18836 |
| Planned sample size | 3000 |
| Sample selection mechanism (for sample surveys only) | Mixed approach – stratified random sampling for the general enterprise population and targeted inclusion of known or potential R&D performers identified from previous R&D surveys, administrative sources |
| Survey frame | Statistical Business Register mainly, administrative sources |
| Sample design | Combined random and targeted sampling design. Stratified random sampling is applied by NACE section and enterprise size class, while known R&D performers are fully included. |
| Sample size | 2991 |
| Survey frame quality | Good |
| Variables the survey contributes to | All R&D variables required by Commission Implementing Regulation (EU) No 2020/1197: R&D personnel (HC and FTE) by function, qualification, and age; intramural R&D expenditure by source of funds, type of cost, and R&D type |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | State Tax Inspectorate (VMI), Innovation Agency |
|---|---|
| Description of collected data / statistics | Administrative sources are used mainly for updating and validating the R&D survey frame. Data from the State Tax Inspectorate (VMI) include information on enterprises applying R&D tax incentives and reporting R&D expenditure in corporate income tax declarations. Data from the Innovation Agency provide information on enterprises receiving national or EU funding for R&D and innovation projects. These sources are used to identify potential R&D performers, verify reported R&D expenditure, and ensure coverage of public funding in the R&D survey. |
| Reference period, in relation to the variables the administrative source contributes to | Data refer to the same reference year as the R&D survey. |
| Variables the administrative source contributes to | Identification of R&D-performing enterprises; validation of reported intramural R&D expenditure; information on R&D funding sources from government and EU programmes. |
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) | 2953 |
|---|---|
| Mode of data collection | Data are collected via the electronic statistical data preparation and transfer system e-Statistics. |
| Incentives used for increasing response | None. The survey is compulsory under the national statistical legislation. |
| Follow-up of non-respondents | Non-respondents are reminded by email and phone calls. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable – non-response replacement is not used. All identified R&D performers are obliged to provide data. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | Response rate is about 99 per cent. |
| 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 in English. |
| R&D national questionnaire and explanatory notes in the national language: | The statistical report form is published at the following address: Statistical Report Form (only in Lithuanian). |
| Other relevant documentation of national methodology in English: | Research and development activities |
| Other relevant documentation of national methodology in the national language: | Methodological documents are published on the Official Statistics Portal in the section Research and Development (R&D). |
18.4. Data validation
To ensure the quality of statistical data, the statistical database is checked. The error protocol is checked, completeness of the entered statistical data is analyzed, and relationships between the indicators are analyzed. The check determines whether data meet mathematical and logical control conditions. After collecting all data, the data is checked again: a comparative analysis is performed between the data provided by respondents and information available from other sources, exceptions to the quantitative indicators are identified, the data set is compared with the previous period, inaccuracies are assessed. In case of deviations, reasons for them are explained and, if necessary, respondents are contacted. Data are corrected if inaccuracies are identified.
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)
Non applicable
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) | Not available | |||
| 10-49 employees and self-employed persons | Not available | |||
| 50-249 employees and self-employed persons | Not available | |||
| 250-and more employees and self-employed persons | Not available | |||
| TOTAL | Not available | |||
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | Not available | |||
| Services2) | Not available | |||
| TOTAL | Not available | |||
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 | Annual survey |
|---|---|
| Data compilation method - Preliminary data | Annual survey |
18.5.3. Measurement issues
| Method of derivation of regional data | Sample on NUTS 2 |
|---|---|
| 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 | Sample on NUTS 2 |
18.5.4. Weighting and estimation methods
| Weight calculation method | Sampling weights are calculated as the inverse of the inclusion probability within each stratum. Adjustment factors are applied for unit non-response. Known R&D performers included with certainty have a weight of 1. |
|---|---|
| Data source used for deriving population totals (universe description) | The population totals were derived from sample survey and known R&D performers. |
| Variables used for weighting | The variable used for weight calculation was number of employees, economic activity. |
| Calibration method and the software used | The non-respondent units were assumed to resemble those who have responded to the survey and treated as nonselected units. For this, the weighting or the grossing up factors were adjusted: the design weight Nh / nh is replaced by Nh / mh where Nh is the size of stratum h, nh is the sample size in stratum h and mh is the number of respondents in stratum h. The software package SAS CLAN was used for calculations. |
| Estimation | Horwitz-Thompson estimation |
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.
30 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. R&D statistics cover national and regional data.
Reference period is 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.
R&D expenditure – EUR thousand;
R&D personnel – persons HC, FTE.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. 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.
