Statistics Lithuania

State Data Agency

Knowledge Economy and Special Survey Statistics Division

29 Gedimino Ave., LT-01500 Vilnius, Lithuania

Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and development (R&D) activities, and thereby provide information about the priority governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content.

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 (FM 2015, Chapter 12), which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020.

The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail (Commission Implementing Regulation (EU) 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 (europa.eu)).

Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

Distribution by socioeconomic objectives (SEO) is based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) at one digit level.

See below.

GBARD statistics are assumed to report detailed data on all the government's budget items that may support R&D activities and to measure or estimate their R&D content. For the purposes of GBARD, the Government sector comprises (a) the central (federal) government, (b) regional (state) government and (c) local (municipal) government subsectors (FM2015, Chapter 12).

Not requested.

Institutions funding R&D or administering R&D funds

See below.

The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units, which can be accessed through the frame and the survey data really refer to this population.

Not requested.

Not requested. See point 5.

Not requested. 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.

Not requested.

a) Calendar year: 2021

b) Fiscal year:

    Start month:

    End month:

See below.

Since the beginning of 2021, GBARD statistics are based on 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.  GBARD statistics were based until the end of 2020 on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

Official statistics programme Part I is regulated by the Law on Official Statistics.

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

-          EBS Methodological Manual on R&D Statistics

Not requested.

Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.

A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.

a)       Confidentiality protection required by law:

 In the process of statistical data collection, processing and analysis and dissemination of statistical information, Statistics Lithuania 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 Statistics Lithuania.

b)       Confidentiality commitments of survey staff:

Statistical Disclosure Control Manual, approved by Order No DĮ-107 of 26 April 2022 of the Director General of Statistics Lithuania;

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-202 of 27 August 2021 of the Director General of Statistics Lithuania.

No press release. Statistical information is published on the Official Statistics Portal according to the Official Statistics Calendar.

Official Statistics Calendar

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 Statistical Information Dissemination and Communication Rules of Statistics Lithuania approved by Order No DĮ-176 of 2 July 2021 of the Director General of Statistics Lithuania.

Annual

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

Statistical indicators are published in Database of Indicators (Science and technology -> Research and development (R&D)).

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

Methodological documents are published in the Official Statistics Portal section Research and development (R&D).

Not requested.

See below.

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 Statistics Lithuania. Main trends in activity of Statistics Lithuania 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 Statistics Lithuania website.

The quality of the statistical results meets the requirements of accuracy, timeliness and punctuality, comparability and consistency. The results are compared with the previous year. Outliers are identified and analysed. If inaccuracies are detected, statistical data are corrected.

See below.

1)       Users' class codification

1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.

2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.

3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.

4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)

5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)

6- Other (User class defined for national purposes, different from the previous classes.)

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

See below.

All indicators established by the legislation are published.

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.

1)  Criteria: Obligatory data (provisional budget and final budget). Only 'Very Good' = 100% and 'Very Poor' <100% apply.

2)  Criteria: Optional data (final budget). 'Very Good' = 100%; 'Good' = >75%;'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Stage: P - provisional, F - final. 

2) Availability of the data: No, data are not available, Y: Yes, data are available + start year.

3) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

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.

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 (5) In the event that errors of a particular type do not exist, is used the sign ‘-‘.

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 GBARD.

1) High level of coverage (At least all national or federal ministries and the ministries and agencies responsible for R&D funding at state or regional level). High rate of response (>90%) in data collection. All figures broken down by NABS.  

2) If at least one out of the three criteria described above would not be fully met.

3) In the event that the rate of response would be lower than 80% even by meeting the two remaining criteria.

4) In the event that the average rate of response would be lower than 70% and at least one of the two remaining criteria would not be met.

5) If all the three criteria described above are not met.

Not requested.

Not requested.

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.

Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.

a)       Description/assessment of coverage errors: Not applicable.

b)      Measures taken to reduce their effect:

 Not applicable.

 Not applicable.

Not requested.

Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values.

a)       Description/assessment of measurement errors:

 Not applicable.

b)      Measures taken to reduce their effect:

 Not applicable.

Non response errors: occur 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.

a) Problems in obtaining data from targeted information providers:

 Not applicable.

b) Measures taken to reduce their effect:

 Not applicable.

c) Effect of non-response errors on the produced statistics:

 Not applicable.

Not requested.

Not requested.

Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.

a)       Data processing and editing processes:

 Not applicable.

b)      Description of errors:

 Not applicable.

c)       Measures taken to reduce their effect:

Not applicable.

Model assumption errors occur when the assumptions made for the estimation of parameters, models, the testing of statistical hypotheses, etc., are violated. As a result, the quality of the resulting statistics is affected (e.g. degrees of confidence might be inflated).

Description/assessment:

 Not applicable.

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.

Date of first release of national data:

Statistical information is published in the 10 month after the end of the reference period.

Date of first release of national data:

Statistical information is published in the 10 month after the end of the reference period.

Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.

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

See below.

Not requested.

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Regulation No 2020/1197, Frascati manual and the EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.

GBARD encompass all spending allocations met from sources of government revenue foreseen within the budget, such as taxation. Spending allocations by extra-budgetary government entities are within the scope only to the extent that their funds are allocated through the budgetary process (FM2015 §12.9). The following table lists a number of key methodological issues, which may affect the international comparability of national GBARD statistics.

See below.

See below.

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

GBARD are based on report by funder. Government-financed GERD are based on reports by R&D performers. Differences are because the money is finally spent by the performer in a later year than the one in which it was committed by the funder.

Not requested.

Not requested.

This part compares GBARD statistics from the provisional and final budget for the reference year.

The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible. 

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

1)        T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)

The revision policy applied by Statistics Lithuania is described in the Description of Procedure for Performance, Analysis and Publication of Revisions of Statistical Information.

Not requested.

Not requested.

a)       Provisional data: 

Administrative source, national budget account, compiled by the Ministry of Finance; data from a statistical survey on R&D in higher education.

b)      Final data:

 Administrative source, national budget account, compiled by the Ministry of Finance; data from a statistical survey on R&D in higher education.

c)       General University Funds (GUF): data from a statistical survey on R&D in higher education.

See 12.3.3.

See below.

1) Time of data collection (T+x): T is assumed to represent the end of reference period. x expresses the number of months after (positive) or before (negative) T when data is collected.

Census R&D survey in HES.

In order to ensure statistical data quality, the data is checked (statistical data validation, comparison with the previous year of statistical data).

The eligibility of statistical data is determined on the basis of the quality of the information received. Statistical information is adjusted if adjusted administrative data had been received.

See below.

No imputation

See below.

Not requested.

Not requested.