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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | Central Statistical Bureau of Latvia |
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1.2. Contact organisation unit | Enterprise Structural and Financial Statistic Section |
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1.5. Contact mail address | Central Statistical Bureau of Latvia Lāčplēša street 1, Rīga, LV 1010 Latvia |
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2.1. Metadata last certified | 21/03/2024 | ||
2.2. Metadata last posted | 21/03/2024 | ||
2.3. Metadata last update | 21/03/2024 |
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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 | ||||||||||||||||||
Post-graduate students are not included |
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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 | ||||||||||||||||||
Latvia |
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3.8. Coverage - Time | ||||||||||||||||||
See point 5. |
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3.9. Base period | ||||||||||||||||||
Not applicable |
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R&D indicators are available according to 5 units of measure:
PN: Number for number of enterprises and number of persons employed. PS: Number of R&D personnel (both internal and external R&D personnel) in headcounts. FT: Number of R&D personnel (both internal and external R&D personnel) activities in full-time equivalent. XDC: Thousands of nacional currency. All financial variables are provided in thousands of euros. PT: Percent |
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2021 |
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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 | ||||||||||||||
Annexes: Cabinet regulation Nr. 782 "Official Statistics Programme for 2022–2024" (only in Latvian) Statistics law |
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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 applicable |
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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:
b) Confidentiality commitments of survey staff:
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7.2. Confidentiality - data treatment | |||
Statistical data shall be considered confidential if they directly or indirectly allow for identification of the private individuals or State authorities regarding which personal statistical data have been provided (primary and secondary confidentiality are applied). All table cells whose values are derived from less than 4 respondents are treated as confidential. In order to ensure confidentiality, the dominance criteria shall also be useded.
In order to ensure that summary information is protected, additional (so-called secondary) cell values are defaulted, thereby protecting primary confidential cells.
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8.1. Release calendar | |||
The release policy and release calendar exists and they are publicly accessible. All official statistics are published according to the data release calendar, at 13.00. |
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8.2. Release calendar access | |||
Release calendar is available. Annexes: Release calendar |
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8.3. Release policy - user access | |||
Users are informed that the data is being released by release calendar. Before the official time of publication, some officials are granted access to statistical data to ensure them time needed for data analysis, understanding and preparation of the point of view. Before provision of such information, the CSB assesses the need and benefits to the society, as well as concludes an agreement on compliance with data confidentiality. Information on the privileged access to statistical data is published on the CSB website. |
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R&D statistics is conducted and disseminated every year. |
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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 Annexes: R&D statistics Statistical Yearbook of Latvia 2022 |
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10.3. Dissemination format - online database | ||||||||||||||||
Core data are available in the online database Annexes: Statistics database |
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10.3.1. Data tables - consultations | ||||||||||||||||
Not available |
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10.4. Dissemination format - microdata access | ||||||||||||||||
It is possible to use remote access to anonymized individual data in research. Depending on the additional data processing methods applied, the datasets are available for use on the researcher's infrastructure (OffSite) or on the remote access system of the Central Statistical Bureau (OnSite). The data are available if application is filled in and contract is concluded in case of positive decision from the Central Statistical Bureau. Anonymized individual data can be only used for scientific or research purposes, moreover, research result has to assure benefit to all society. Individual data or microdata are records from surveys, population censuses or registers on individuals, households or enterprises. |
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10.4.1. Provisions affecting the access | ||||||||||||||||
Annexes: Research data catalogue |
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10.5. Dissemination format - other | ||||||||||||||||
See below. |
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10.5.1. Metadata - consultations | ||||||||||||||||
Not available |
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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 | ||||||||||||||||
Reference metadata SIMS 2.0 standart available in online database. Annexes: Reference metadata SIMS 2.0 |
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10.6.1. Metadata completeness - rate | ||||||||||||||||
Not available. |
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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 | |||
CSB has introduced Quality Management System (QMS). The system is directed towards providing high user satisfaction and ensuring compliance with regulatory enactments. Based on the structure of Generic Statistical Business Process Model (GSBPM), QMS defines and at the level of procedures describes processes of statistical production as well as sets the persons responsible for the monitoring of processes at all stages of the statistical production. QMS defines the sequence how processes are implemented (i.e., activities to be performed (incl. verifications of processes and statistics, sequence and implementation requirements thereof, as well as persons responsible for the implementation)), procedures used in the evaluation of processes and statistics, as well as any improvements needed. |
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11.2. Quality management - assessment | |||
Quality of statistics is assessed in accordance with the existing requirements of external and internal regulatory enactments and in accordance with the established quality criteria. Regulation (EC) no 223/2009 of the European Parliament and of the Council on European statistics states that European Statistics European statistics shall be developed, produced and disseminated on the basis of uniform standards and of harmonised methods. In this respect, the following quality criteria shall apply: relevance, accuracy, timeliness, punctuality, accessibility, clarity, comparability and coherence. |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not available |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Since the year 2021 the coefficient of variation is no longer calculated. Threshold sampling and no changes in strata. |
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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: Not available
b) Measures taken to reduce their effect: Not available
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
No administrative data |
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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: Not applicable
b) Measures taken to reduce their effect: Not applicable
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Reminders are sent twice and it is called several times to remind that the questionnaire should be submitted. |
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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 applicable |
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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: At national level data published in 31.08.2020 (as preliminary); preliminary data sent to Eurostat in 12.10.2022. c) Lag (days): 0 |
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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: Data sent to Eurostat in 30.06.2023. The corrections of the data at national level has been done at the same time. c) Lag (days): 0 |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
EU data on EU-27 as a whole and for each EU Member State are published on Eurostat website. |
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15.1.1. Asymmetry for mirror flow statistics - coefficient | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not available |
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15.1.2. General issues of comparability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
No differences from Frascati Manual. |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The data can be compared since 1993. |
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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 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The data are produced in the same way in the odd and even years. |
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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. If an enterprise has reported R&D expenditures, these data for the reference year (2020) are preprinted in the the Community innovation survey 2020 (CIS2020). Differences arise when an enterprise does not become a sample unit (or doesn't show the R&D) expenditures) of one or another survey (R&D survey or Community innovation survey). |
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15.3.1. Coherence - sub annual and annual statistics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not available |
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15.3.2. Coherence - National Accounts | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not available |
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15.3.3. National Coherence Assessments | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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15.3.4. Coherence – Foreign-controlled EU enterprises – inward FATS | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not available |
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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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In line with the strategic directions of the European Statistics System and latest trends in statistical production, continuous use of information acquired in regular CSB surveys and proportionate reduction of the response burden are among the key CSB priorities. In cooperation with holders of administrative data and in line with the competences provided for in the Statistics Law, CSB is striving to solve the issues related to the use of administrative data sources, thus aiming to acquire as comprehensive and high-quality administrative data allowing to reduce response burden on enterprises and households as possible. |
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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 | |||
Revision Policy is an important component of good governance practice addressed more and more often in the international statistical society. The objective of the Revision Policy is to lay down the order of review or revision of the prepared and published data. The first chapter of the present document explains the terms applied in the Revision Policy, the second chapter shortly characterises the CSB Revision Policy, whereas the third chapter stipulates the revision cycle of the statistical data produced by the CSB. Annexes: REVISON POLICY GUIDELINES |
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17.2. Data revision - practice | |||
Published data are not revised. |
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17.2.1. Data revision - average size | |||
Not applicable |
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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 | ||||||||||||||||||||||||||||||||||||||||||||
Annexes: Questionnaire (LV) Explanatory notes (LV) |
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18.4. Data validation | ||||||||||||||||||||||||||||||||||||||||||||
Collected data has been compared with previous years. Respondents who have given a negative response are beeing checked after the managment report and annual report. |
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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 | ||||||||||||||||||||||||||||||||||||||||||||
No imputated data.
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18.5.1.2. Imputation rate (un-weighted) (%) by NACE | ||||||||||||||||||||||||||||||||||||||||||||
No imputated data.
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 applicable |
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18.6.1. Seasonal adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not applicable |
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