1.1. Contact organisation
State Date 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
31 October 2025
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
The R&D statistics for the Private Non-Profit (PNP) sector are compiled by the State Data Agency (Statistics Lithuania) in accordance with Commission Implementing Regulation (EU) No 2020/1197 and the Frascati Manual (FM 2015).
The PNP sector covers non-profit institutions serving households (NPISH) and other private non-profit organisations engaged in or financing research and experimental development (R&D) activities that are not classified under the Business Enterprise, Government, or Higher Education sectors.
In Lithuania, the PNP sector is very small, and the number of active R&D performers is limited.
Therefore, PNP units are surveyed together with the Business Enterprise Sector (BES) as part of the annual R&D statistical survey.
The collected data include:
- Intramural R&D expenditure by source of funds, type of costs, and type of R&D activity;
- R&D personnel (headcount and FTE) by function (researchers and other supporting staff).
Aggregated results are disseminated via the Official Statistics Portal (OSP) and transmitted annually to Eurostat under the European R&D data collection programme.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics)..
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook 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 product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
See below.
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
| Private non-profit sector | No differences from Frascati Manual (FM). |
|---|---|
| Inclusion of units that primarily do not belong to PNP 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 Frascati Manual §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 |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | 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
See below.
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 |
| Defence R&D - method for obtaining data on R&D expenditure | Main economic activity of the institution conducting the R&D activity. No divergences with ISIC/NACE classification. |
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 |
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 available |
| Age | not available |
| Citizenship | not available |
3.4.2.3. FTE calculation
By FM recommendation.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the PNP Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | Statistical Business Register used to define the target population | |
| Estimation of the target population size | 282 |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See concept 12.3.2. (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. 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. The transmission of R&D data is mandatory for Member States and EEA countries.
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
- European Business Statistics Methodological Manual on R&D
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.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
10.1. Dissemination format - News release
See below.
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 | N | |
| 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 the 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 | N | Aggregate figures are published in the Database of Indicators (Science and technology -> Research and development (R&D)) starting from 2024. | |
| Data prepared for individual ad hoc requests | N | ||
| 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
See below.
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 PNP sector coverage is inaccordance to the Frascati Manual.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| not applicable | not applicable | not applicable |
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
See below.
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. Data availability
Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D): 0,46%
12.3.2.1. Incorporation of PNP sector in another sector
| Incorporation of PNP in another sector | Business enterprises sector |
|---|---|
| Reasons for not producing separate R&D statistics for the PNP sector | The number of PNP units surveyed is very low. |
| Share of PNP expenditure in the total expenditure of the other sector | 1% |
| Share of PNP R&D Personnel in the respective figure of the other sector | 1% |
12.3.2.2. Non-collection of R&D data for the PNP sector
| Reasons for not compiling R&D statistics for the PNP sector | Not applicable |
|---|---|
| PNP R&D expenditure/ GERD*100) | 0.46% |
| Share of PNP R&D Personnel in the respective figure of the total national economy | very small share
|
12.3.2.3. Data availability on more detail level
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| R&D Expenditure | Y-2023 | Annual | Source of funds, Type of costs, Type of R&D activity | Type of R&D × Source of funds | Detailed (same structure as BES) but limited (due to small number of units) |
| R&D Personnel (HC) | Y-2023 | Annual | Function, Gender | Function × Gender | Detailed (same structure as BES) but limited (due to small number of units) |
| R&D Personnel (FTE) | Y-2023 | Annual | Function | Detailed (same structure as BES) but limited (due to small number of units) | |
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').
2) Y-start year
12.3.2.4. 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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
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
Confidence interval for Total R&D expenditure: not calculated
Confidence interval for Total R&D personnel (FTE): not calculated
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.
- Extent of non-sampling errors: Not applicable.
- Measures taken to reduce the extent of non-sampling errors: Not applicable.
- Methods used in order to correct/adjust for such errors: Not applicable.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Not requested.
13.3.3. Non response error
Not requested.
13.3.3.1. Unit non-response - rate
Not requested.
13.3.3.2. Item non-response - rate
Not requested.
13.3.4. Processing error
Not requested.
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)
National R&D data for the PNP sector have been published since 2024.
- End of reference period: December 2023.
- Date of first release of national data: National R&D data for the PNP sector have been published since 2024.
- Lag (days): National R&D data for the PNP sector have been published since 2024.
14.1.2. Time lag - final result
National R&D data for the PNP sector have been published since 2024.
- End of reference period: December 2023.
- Date of first release of national data: National R&D data for the PNP sector have been published since 2024.
- Lag (days): National R&D data for the PNP sector have been published since 2024.
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
See below.
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) paragraphs and the EBS Methodological Manual on R&D Statistics with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly 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 | |
| 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. |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Intramural R&D expenditure | FM2015,Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Statistical unit | FM2015, § 10.40-10.42 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015, § 10.40-10.42 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015, § 10.2-10.8 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | 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, 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 method | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | |
| Survey questionnaire / data collection form | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | |
| Cooperation with respondents | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | |
| Data processing methods | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | |
| Treatment of non-response | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviation | |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | No deviation |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1) | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | No break | ||
| Function | No | ||
| Qualification | No | ||
| R&D personnel (FTE) | No | ||
| Function | No | ||
| Qualification | No | ||
| R&D expenditure | No | ||
| 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.
15.3. Coherence - cross domain
See below.
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.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total PNP R&D expenditure (in 1000 of national currency) | Total PNP R&D personnel (in FTEs) | Total number of PNP researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 3555 | 82 | 78 |
| Final data (delivered T+18) | 3527 | 81 | 78 |
| Difference (of final data) | -28 | -1 | 0 |
Comments: No 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 |
1) 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) | 75 | |
| Average Time required to complete the questionnaire in hours (T)1) | 0.8 h | |
| 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 R&D data for the PNP sector are collected annually by the State Data Agency (Statistics Lithuania) as part of the national R&D statistical survey (form MT-02), conducted jointly with the Business Enterprise Sector (BES).
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.
18.1.2. Sample/census survey information
| Sampling unit | Legal unit (enterprise or private non-profit organisation) as recorded in the Statistical Business Register. |
|---|---|
| Stratification variables (if any - for sample surveys only) | Same as for the BES: NACE Rev.2 section and enterprise size class (number of employees). |
| Stratification variable classes | Same as for the BES |
| Population size | 282 |
| Planned sample size | 75 |
| Sample selection mechanism (for sample surveys only) | Same as for the BES |
| Survey frame | Based on the Statistical Business Register and supplementary information from the State Tax Inspectorate and Innovation Agency on entities receiving R&D-related grants or benefits. |
| Sample design | Same as for the BES |
| Sample size | 75 |
| Survey frame quality | Good |
| Variables the survey contributes to | R&D personnel (HC, FTE) and intramural R&D expenditure by source of funds, type of costs |
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 12.3.2.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Private non-profit organisations performing or financing R&D activities (foundations, associations, research centres). |
|---|---|
| Description of collected information | Data on personnel engaged in R&D activities and R&D expenditure |
| Data collection method | The data are collected via the electronic statistical data preparation and transfer system e-Statistics. |
| Time-use surveys for the calculation of R&D coefficients | Not applicable. |
| Realised sample size (per stratum) | 75 |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | online survey |
| Incentives used for increasing response | Not applicable. |
| Follow-up of non-respondents | Phone and e-mail reminding |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | Response rate 99 per cent. |
| Non-response analysis (if applicable -- also see section 18.5. 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: Collection of Data from Legal Entities (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
18.5.2. 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
| Description of weighting method | not applicable |
|---|---|
| Description of the estimation method | not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
The R&D statistics for the Private Non-Profit (PNP) sector are compiled by the State Data Agency (Statistics Lithuania) in accordance with Commission Implementing Regulation (EU) No 2020/1197 and the Frascati Manual (FM 2015).
The PNP sector covers non-profit institutions serving households (NPISH) and other private non-profit organisations engaged in or financing research and experimental development (R&D) activities that are not classified under the Business Enterprise, Government, or Higher Education sectors.
In Lithuania, the PNP sector is very small, and the number of active R&D performers is limited.
Therefore, PNP units are surveyed together with the Business Enterprise Sector (BES) as part of the annual R&D statistical survey.
The collected data include:
- Intramural R&D expenditure by source of funds, type of costs, and type of R&D activity;
- R&D personnel (headcount and FTE) by function (researchers and other supporting staff).
Aggregated results are disseminated via the Official Statistics Portal (OSP) and transmitted annually to Eurostat under the European R&D data collection programme.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics)..
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook 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
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- 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.
The frequency of R&D data dissemination at Eurostat level 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.
See below.
See below.
