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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | State Data Agency (Statistics Lithuania) |
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1.2. Contact organisation unit | Knowledge Economy and Special Survey Statistics Division |
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1.5. Contact mail address | 29 Gedimino Ave., LT-01500 Vilnius, Lithuania |
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2.1. Metadata last certified | 31/10/2023 | ||
2.2. Metadata last posted | 31/10/2023 | ||
2.3. Metadata last update | 31/10/2023 |
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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 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. 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).
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. 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. |
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3.2. Classification system | ||||||||||||||||||
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3.2.1. Additional classifications | ||||||||||||||||||
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3.3. Coverage - sector | ||||||||||||||||||
See below. |
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3.3.1. General coverage | ||||||||||||||||||
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3.3.2. Sector institutional coverage | ||||||||||||||||||
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3.3.3. R&D variable coverage | ||||||||||||||||||
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3.3.4. International R&D transactions | ||||||||||||||||||
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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).
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3.4. Statistical concepts and definitions | ||||||||||||||||||
See below. |
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3.4.1. R&D expenditure | ||||||||||||||||||
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3.4.2. R&D personnel | ||||||||||||||||||
See below. |
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3.4.2.1. R&D personnel – Head Counts (HC) | ||||||||||||||||||
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3.4.2.2. R&D personnel – Full Time Equivalent (FTE) | ||||||||||||||||||
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3.4.2.3. FTE calculation | ||||||||||||||||||
By FM recommendation. |
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3.4.2.4. R&D personnel - Cross-classification by function and qualification | ||||||||||||||||||
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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. |
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3.6. Statistical population | ||||||||||||||||||
See below. |
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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.
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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.
1) i.e. enterprises previously not known or not supposed to perform R&D |
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3.7. Reference area | ||||||||||||||||||
Not requested. R&D statistics cover national and regional data. |
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3.8. Coverage - Time | ||||||||||||||||||
Not requested. See point 3.4. |
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3.9. Base period | ||||||||||||||||||
Not requested. |
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R&D expenditure – EUR thousand; R&D personnel – persons HC, FTE. |
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Reference period is the calendar year.
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6.1. Institutional Mandate - legal acts and other agreements | ||||||||||||||
See below. |
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6.1.1. European legislation | ||||||||||||||
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6.1.2. National legislation | ||||||||||||||
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6.1.3. Standards and manuals | ||||||||||||||
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development |
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6.2. Institutional Mandate - data sharing | ||||||||||||||
Not requested. |
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7.1. Confidentiality - policy | |||
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:
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7.2. Confidentiality - data treatment | |||
Data for at least three enterprises in cell are confidential. 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. |
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8.1. Release calendar | |||
Statistical information is published on the Official Statistics Portal according to the Official Statistics Calendar. |
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8.2. Release calendar access | |||
The release calendar is accessible on the website. |
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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 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. |
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Annual |
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10.1. Dissemination format - News release | ||||||||||||||||
See below. |
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10.1.1. Availability of the releases | ||||||||||||||||
1) Y - Yes, N – No |
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10.2. Dissemination format - Publications | ||||||||||||||||
See below. |
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10.2.1. Availability of means of dissemination | ||||||||||||||||
1) Y – Yes, N - No |
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10.3. Dissemination format - online database | ||||||||||||||||
Statistical indicators are published in the Database of Indicators (Science and technology -> Research and development (R&D)). |
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10.3.1. Data tables - consultations | ||||||||||||||||
Not requested. |
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10.4. Dissemination format - microdata access | ||||||||||||||||
See below. |
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10.4.1. Provisions affecting the access | ||||||||||||||||
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10.5. Dissemination format - other | ||||||||||||||||
See below. |
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10.5.1. Metadata - consultations | ||||||||||||||||
Not requested. |
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10.5.2. Availability of other dissemination means | ||||||||||||||||
1) Y – Yes, N - No |
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10.6. Documentation on methodology | ||||||||||||||||
Methodological documents are published on the Official Statistics Portal in the section Research and Development (R&D). |
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10.6.1. Metadata completeness - rate | ||||||||||||||||
Not requested. |
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10.7. Quality management - documentation | ||||||||||||||||
See below. |
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10.7.1. Information and clarity | ||||||||||||||||
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11.1. Quality assurance | |||
Quality of statistical information and its production process is ensured by the provisions of the European Statistics Code of Practice and ESS Qu ality 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. |
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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, data providers are contacted and reasons are determined. If inaccuracies are detected, data are corrected. BES data on R&D are collected by the Frascati Manual recommendations. There are enterprises in Lithuania, which implement R&D-related activities. Moreover, these enterprises face problems while distinguishing R&D activities from other implemented activities. |
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12.1. Relevance - User Needs | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.1.1. Needs at national level | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Users' class codification 1- Institutions: 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. ) |
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12.2. Relevance - User Satisfaction | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys. |
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12.2.1. National Surveys and feedback | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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12.3. Completeness | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.3.1. Data completeness - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
According to the Official Statistics Programme Part I, 100 per cent of statistical information is published. |
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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.
Criteria: A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply. B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%. |
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12.3.3. Data availability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.3.3.1. Data availability - R&D Expenditure | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.2. Data availability - R&D Personnel (HC) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.3. Data availability - R&D Personnel (FTE) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Y-start year, N – data not available |
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12.3.3.4. Data availability - other | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 |
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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. |
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13.1.1. Accuracy - Overall by 'Types of Error' | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 R&D. |
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13.1.2. Assessment of the accuracy with regard to the main indicators | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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' = In the event that at least one out of the three criteria above described would not be fully met. 3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria. 4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met. 5) 'Very Poor' = If all the three criteria are not met. |
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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. |
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13.2.1. Sampling error - indicators | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The main indicator used to measure sampling errors is the coefficient of variation (CV). |
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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. |
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13.2.1.2. Coefficient of variation for key variables by NACE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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) |
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13.2.1.3. Coefficient of variation for key variables by Size Class | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. |
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13.3.1. Coverage error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors: There were no coverage errors
b) Measures taken to reduce their effect: None
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13.3.1.1. Over-coverage - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Magnitude of error (%) = (Observed Value-True Value)/ True Value (%) |
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13.3.1.1.1. Over-coverage rate - groups | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.1.2. Common units - proportion | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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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.
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13.3.2. Measurement error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors: There were no measurement errors.
b) Measures taken to reduce their effect: None
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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. |
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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. |
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13.3.3.1.1. Unit non-response rates by Size Class | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.1.2. Unit non-response rates by NACE | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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) |
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13.3.3.1.3. Recalls/Reminders description | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
E-mail reminders or calls by phone. |
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13.3.3.1.4. Unit non-response survey | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.2. Item non-response - rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Definition: |
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13.3.3.2.1. Un-weighted item non-response rate | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.3.3. Magnitude of errors due to non-response | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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. |
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13.3.4.1. Identification of the main processing errors | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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13.3.5. Model assumption error | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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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. |
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14.1.1. Time lag - first result | |||||||||||||||
Time lag between the end of reference period and the release date of the results:
a) End of reference period: 31.12.2021 b) Date of first release of national data: 7.10.2022 c) Lag (days): 280 |
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14.1.2. Time lag - final result | |||||||||||||||
a) End of reference period: 31.12.2021 b) Date of first release of national data: 7.06.2023 c) Lag (days): 523 |
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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. |
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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). |
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14.2.1.1. Deadline and date of data transmission | |||||||||||||||
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15.1. Comparability - geographical | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.1.1. Asymmetry for mirror flow statistics - coefficient | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.1.2. General issues of comparability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Comparable. No divergences from FM. |
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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 and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
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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.
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15.2. Comparability - over time | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.1. Length of comparable time series | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.2. Breaks in time series | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Breaks years are years for which data are not fully comparable to the previous period. |
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15.2.3. Collection of data in the even years | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Odd and even years have the same data. |
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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 2020 (CIS2020) (inn_cis12) (europa.eu) also collects the R&D expenditure of enterprises that form the coverage of the CIS2020 survey. |
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15.3.1. Coherence - sub annual and annual statistics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.3.2. Coherence - National Accounts | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The indicators of institutional sectors are internally coherent. |
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15.3.3. National Coherence Assessments | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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15.3.4. Coherence – Foreign-controlled EU enterprises – inward FATS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Coherent with Foreign-controlled EU enterprises, inward FATS. |
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15.4. Coherence - internal | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.4.1. Comparison between preliminary and final data | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This part compares key R&D variables as preliminary and final data.
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15.4.2. Consistency between R&D personnel and expenditure | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
(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). |
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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. |
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16.1. Costs summary | |||||||||||||||||||||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies. |
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16.2. Components of burden and description of how these estimates were reached | |||||||||||||||||||||
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’) |
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17.1. Data revision - policy | |||
Not requested. The revision policy applied by Statistics Lithuania is described in the Description of Procedure for Performance, Analysis and Publication of Revisions of Statistical Information. |
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17.2. Data revision - practice | |||
Not requested. |
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17.2.1. Data revision - average size | |||
Not requested. |
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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. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in. |
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18.1.1. Data source – general information | ||||||||||||||||||||||||||||||||||||||||||||
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18.1.2. Sample/census survey information | ||||||||||||||||||||||||||||||||||||||||||||
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18.1.3. Information on collection of administrative data or of pre-compiled statistics | ||||||||||||||||||||||||||||||||||||||||||||
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18.2. Frequency of data collection | ||||||||||||||||||||||||||||||||||||||||||||
See 12.3.3. |
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18.3. Data collection | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.3.1. Data collection overview | ||||||||||||||||||||||||||||||||||||||||||||
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18.3.2. Questionnaire and other documents | ||||||||||||||||||||||||||||||||||||||||||||
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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. |
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18.5. Data compilation | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.5.1. Imputation - rate | ||||||||||||||||||||||||||||||||||||||||||||
Imputation is the method of creating plausible (but artificial) substitute values for all those missing. |
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18.5.1.1. Imputation rate (un-weighted) (%) by Size class | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.1.2. Imputation rate (un-weighted) (%) by NACE | ||||||||||||||||||||||||||||||||||||||||||||
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)
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18.5.2. Data compilation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.3. Measurement issues | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.4. Weighting and estimation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.6. Adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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18.6.1. Seasonal adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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Reference to methodology: The process of preparation of statistical information is described in the Methodology of statistical survey on research and development (only in Lithuanian). |
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