1.1. Contact organisation
Central Statistical Bureau of Latvia
1.2. Contact organisation unit
Business Statistics Methodology Section
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Central Statistical Bureau of Latvia
Lāčplēša street 1, Rīga, LV 1010
Latvia
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
31 October 2025
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
3.1. Data description
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
The size classification is based on the number of employees.
Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
No difference from Frascati Manual |
|---|---|
| Hospitals and clinics | Hospitals and clinics can be included in HES or in GOV sector, it depends on administration |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | Not included |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No difference from Frascati Manual |
|---|---|
| External R&D personnel | No difference from Frascati Manual |
| Clinical trials: compliance with the recommendations in FM §2.61. | No difference from Frascati Manual |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available |
|---|---|
| Payments to rest of the world by sector - availability | Available |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Not covered |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Extramural expenditure are collected for all sectors with special tables in questionnaires |
| Difficulties to distinguish intramural from extramural R&D expenditure | No difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
3.4.1. R&D expenditure
| Coverage of years | Calendar |
|---|---|
| Source of funds | Business enterprises, direct government, funds from abroad |
| Type of R&D | Basic research, applied research, experimental development |
| Type of costs | Intramural R&D expenditure: current costs, R&D capital investments; extramural R&D expenditure |
| Economic activity of the unit | Main economic activity of the institution conducting the R&D activity |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Not collected |
| Product field | Not collected |
| Defence R&D - method for obtaining data on R&D expenditure | Not applicable. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons employed during the calendar year |
|---|---|
| Function | Researchers, technicians and supporting staff are included |
| Qualification | Holders of ISCED 8, ISCED 7, ISCED 6, ISCED 5 are included |
| Age | Only internal researchers |
| Citizenship | Not collected |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar |
|---|---|
| Function | Researchers, technicians and supporting staff are included |
| Qualification | Holders of ISCED 8, ISCED 7, ISCED 6, ISCED 5 are included |
| Age | Not collected |
| Citizenship | Not collected |
3.4.2.3. FTE calculation
Post-graduate students are not included
3.5. Statistical unit
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
3.6. Statistical population
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
3.6.1. National target population
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | Statistical Business Register used to defined target population. The target population is defined as active enterprises during year 2023 whose main economical activity is from A to U | |
| Estimation of the target population size | 91094 | |
| Size cut-off point | No | |
| Size classes covered (and if different for some industries/services) | 0-9, 10-49, 50-249, 250+ | |
| NACE/ISIC classes covered | All |
3.6.2. Frame population – Description
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
| Method used to define the frame population | Frame population includes all enterprises corresponding to the target population and is defined as active enterprises during year 2023 whose main economical activity is from A to U. Not included units with any sign of liquidation. NACE2.red.9603 is not included. If any information is available about R&D conducted, these enterprises are included in the frame. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Enterprises with more than 100 employees; NACE code “72”; also answers for the certain questions of 2022 year questionnaire were taken to define supposed R&D performer, as well as information from administrative data sources |
| Inclusion of units that primarily do not belong to the frame population | Not applicable |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | Not applicable |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | Not applicable |
| Systematic exclusion of units from the process of updating the target population | Not included units with any sign of liquidation. NACE2.red.9603 is not included. |
| Estimation of the frame population | 91094 |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
R&D statistics cover national and regional data.
Latvia
3.8. Coverage - Time
See concept 12.3.3. (data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
Calendar year - 2023
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | By general. Statistics Law |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not applicable
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law:
- Regulation (EC) No 223/2009 of the European Parliament and of the Council on European statistics
- Regulation (EU) 2016/679 of the European Parliament and of the Council
- Statistics Law.
- Confidentiality commitments of survey staff:
- Code of Ethics
- Privacy Statement
7.2. Confidentiality - data treatment
Primary confidentiality
In enterprise statistics cells are defined as confidential according to threshold rule and dominance rule (n,k). Cells are safe to be published if contributed by at least 4 respondents (n=4) as well as share of a single contributor is less than 80% (1,80) or two contributors share is less than 90% (2,90).
Secondary confidentiality
To ensure protection of aggregated data, secondary confidentiality is applied to supress additional cells, thus protecting primary confidential cells. We select secondary confidential cells to provide adequate protection while minimizing information loss.
Applicable secondary confidentiality determination criteria:
- Secondary confidentiality is determined using T-Argus software, which performs automatic secondary confidentiality calculation.
- Secondary confidential cells are selected manually.
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.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
At National level: Release calendar
8.3. Release policy - user access
Statistical release dates and times are pre-announced in the data dissemination calendar.
Annual
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.5 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | N | |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | Y | Core data are available in “Statistical Yearbook of Latvia”; Latvia. Statistics in Brief. Online database is available. |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Core data are available in the online database
10.3.1. Data tables - consultations
Not available
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
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.
10.4.1. Provisions affecting the access
| Access rights to micro-data | Limited |
|---|---|
| Access cost policy | Microdata are available under some conditions |
| Micro-data anonymisation rules | Microdata are available under some conditions |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not available
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1) | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | ||
| Data prepared for individual ad hoc requests | Y | ||
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Reference metadata SIMS 2.0 standart available in online database.
10.6.1. Metadata completeness - rate
Not available.
10.7. Quality management - documentation
Please see the sub-concept 10.7.1 in the full metadata view.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Definitions and explanations in online survey are available. In on-line database core data and methodology are available. |
|---|---|
| Requests on further clarification, most problematic issues | No such requests. |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
To achieve high user satisfaction and ensure compliance with regulatory requirements, the CSB has introduced a Quality Management System (QMS). The system defines and, at the procedural level, describes processes of statistical production and identifies the persons responsible for their monitoring throughout all production stages. Its structure follows the principles of the Generic Statistical Business Process Model (GSBPM).
The QMS sets out the sequence in which processes are implemented – that is, the activities to be performed, including verifications of processes and produced statistics, the order and implementation requirements of these activities, and the persons responsible for their execution. It also defines the approach to evaluating production processes and their outcomes, and to implementing necessary improvements.
The CSB quality management system is certified to the ISO 9001:2015 standard Quality management systems — Requirements since 2018 (scope of certification: development, production and dissemination of official statistics). The original certification audit was performed by BM Trada Latvija SIA and a recertification audit, in 2024, was performed by Bureau Veritas Latvia SIA.
The CSB information security management system is certified to the ISO/IEC 27001:2022 standard Information security, cybersecurity and privacy protection — Information security management systems — Requirements since 2017 (scope of certification: collection, processing and storage of information and data for functions of the Central Statistical Bureau of Latvia. Provision of statistical information for inland and foreign users). The original certification audit was performed by BM Certification SIA and a recertification audit, in 2024, was performed by Bureau Veritas Latvia SIA.
11.2. Quality management - assessment
The quality of statistics is assessed in accordance with the existing requirements of both external and internal regulatory enactments, as well as the established quality criteria.
Regulation (EC) No 223/2009 of the European Parliament and of the Council on European statistics stipulates that European statistics shall be developed, produced and disseminated on the basis of uniform standards and harmonised methods. In this context, the following quality criteria shall apply: relevance, accuracy, timeliness, punctuality, accessibility, clarity, comparability and coherence.
As the national statistical institute and the principal authority of the national statistical system, the CSB has set common general institutional-level quality requirements for authorities responsible for producing or providing national statistics. These requirements are based on the European Statistics Code of Practice, which comprises 16 principles.
The overall assessment of data quality is good.
12.1. Relevance - User Needs
Please see the sub-concept 12.1.1 in the full metadata view.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 - European level | The European Commission | Data according to Commission Regulation 2020/1197 |
| 1 - National | The Ministry of Economics, the Ministry of Education and Science |
Summary tables - to work out R&D strategy and politics |
| 1 - International organisations | OECD | Data according to Commission Regulation 2020/1197 |
| 4 - Researchers and students | Researchers and students | Summary tables - to analyse the field of R&D |
1) Users' class codification
1- Institutions:
- European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
- in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
- International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes. )
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | Respondent satisfaction survey. |
|---|---|
| User satisfaction survey specific for R&D statistics | There were some questions about R&D survey included in satisfaction survey (2023). |
| Short description of the feedback received | Mostly understandable, supplementary materials are useful. Need more and more specific examples every year. |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
Not available
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Not applicable |
| Obligatory data on R&D expenditure | Not applicable |
| Optional data on R&D expenditure | Not applicable |
| Obligatory data on R&D personnel | Not applicable |
| Optional data on R&D personnel | Not applicable |
| Regional data on R&D expenditure and R&D personnel | Not applicable |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y - 2002 | Annual | ||||
| Type of R&D | Y - 2002 | Annual | ||||
| Type of costs | Y - 2002 | Annual | ||||
| Socioeconomic objective | Y - 2016 | Annual | ||||
| Region | Latvia in NUTS 2 | Annual | ||||
| FORD | Y - 2002 | Annual | ||||
| Type of institution | Y - 2021 | Annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y - 2002 | Annual | ||||
| Function | Y - 2002 | Annual | ||||
| Qualification | Y - 2002 | Annual | ||||
| Age | Y - 2012 (only researchers) | Annual | Starting from year 2016 only about internal researchers | |||
| Citizenship | N | |||||
| Region | Latvia in NUTS2 | |||||
| FORD | Y - 2002 | Annual | ||||
| Type of institution | N | |||||
| Economic activity | Y - 2002 | Annual | ||||
| Product field | N | |||||
| Employment size class | Y - 2002 | Annual |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y - 2002 | Annual | ||||
| Function | Y - 2002 | Annual | ||||
| Qualification | Y - 2002 | Annual | ||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Latvia in NUTS 2 | Annual | ||||
| FORD | Y - 2002 | Annual | ||||
| Type of institution | N | |||||
| Economic activity | Y - 2002 | Annual | ||||
| Product field | N | |||||
| Employment size class | Y - 2002 | Annual |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| No additional dimension or variable available at national level | |||||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not applicable | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non- response errors | ||||
| Total intramural R&D expenditure | +/- | +/- | +/- | +/- | +/- | +/- | +/- |
| Total R&D personnel in FTE | +/- | +/- | +/- | +/- | +/- | +/- | +/- |
| Researchers in FTE | +/- | +/- | +/- | +/- | +/- | +/- | +/- |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | x | ||||
| Total R&D personnel in FTE | x | ||||
| Researchers in FTE | x |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below
13.2.1.1. Variance Estimation Method
Since the year 2021 the coefficient of variation is no longer calculated. Threshold sampling.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | Not applicable | ||
| R&D personnel (FTE) |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | Not applicable | ||||
| R&D personnel (FTE) |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors: Not applicable.
- Measures taken to reduce their effect: Not applicable.
13.3.1.1. Over-coverage - rate
0.21%
13.3.1.2. Common units - proportion
Not applicable
13.3.1.3. Frame misclassification rate
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
| By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
|---|---|---|---|---|---|
| Number or surveyed enterprises in the stratum (according to frame) | |||||
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | |||||
| Misclassification rate | |||||
| By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
| Number or surveyed enterprises in the stratum (according to frame) | |||||
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | |||||
| Misclassification rate |
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
- Description/assessment of measurement errors: Not applicable.
- Measures taken to reduce their effect: Not applicable.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response, which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response, which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate.
Definition:
Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100
13.3.3.1.1. Unit non-response rates by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| Number of units with a response in the realised sample | 617 | 642 | 1238 | 271 | 2768 |
| Total number of units in the sample | 695 | 678 | 1268 | 272 | 2913 |
| Unit Non-response rate (un-weighted) | 0.107 | 0.052 | 0.023 | 0.004 | 0.048 |
| Unit Non-response rate (weighted) | 0.107 | 0.052 | 0.023 | 0.004 | 0.048 |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 1050 | 1718 | 2768 |
| Total number of units in the sample | 1112 | 1801 | 2913 |
| Unit Non-response rate (un-weighted) | 0.054 | 0.044 | 0.048 |
| Unit Non-response rate (weighted) | 0.054 | 0.044 | 0.048 |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.3.3.1.3. Recalls/Reminders description
Reminders are sent twice and it is called several times to remind that the questionnaire should be submitted.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No |
|---|---|
| Selection of the sample of non-respondents | No |
| Data collection method employed | No |
| Response rate of this type of survey | Not applicable |
| The main reasons of non-response identified | Not applicable |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | |||
| Imputation (Y/N) | |||
| If imputed, describe method used, mentioning which auxiliary information or stratification is used |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | No magnitude of errors (%) calculated. |
| Total R&D personnel in FTE | |
| Researchers in FTE |
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | Respondents respond through electronic online questionnaires (CAWI). The programme for data input does not allow inputting erroneous for it has logical and mathematical data controls. |
|---|---|
| Estimates of data entry errors | Not applicable |
| Variables for which coding was performed | Not applicable |
| Estimates of coding errors | |
| Editing process and method | |
| Procedure used to correct errors |
13.3.5. Model assumption error
Not applicable
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: At national level data published in November 2024 (as preliminary); preliminary data sent to Eurostat in October 2024.
- Lag (days): 300
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: Final data sent to Eurostat in June 2025. At national level data published in July 2025 (as final).
- Lag (days): 545
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay | No delay | No delay |
15.1. Comparability - geographical
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not applicable
15.1.2. General issues of comparability
Not applicable
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | Not applicable | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation |
15.2. Comparability - over time
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
15.2.1. Length of comparable time series
The data can be compared since 1993.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | No | ||
| Function | No | ||
| Qualification | No | ||
| R&D personnel (FTE) | No | ||
| Function | No | ||
| Qualification | No | ||
| R&D expenditure | No | ||
| Source of funds | No | ||
| Type of costs | No | ||
| Type of R&D | No | ||
| Other | No |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
The data are produced in the same way in the odd and even years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not applicable
15.3.2. Coherence - National Accounts
Not available
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
|---|---|---|---|---|---|
| Intramural R&D expenditure | In even years we compare with CIS data. | ||||
15.4. Coherence - internal
Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 117344.366 | 1842 | 1306 |
| Final data (delivered T+18) | 118109.518 | 1857 | 1316 |
| Difference (of final data) | +765.152 | +15 | +10 |
Comments :
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not applicable | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not applicable |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
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.
CSB measures to improve use of administrative data and reduce response burden taken in Use of Administartive Data - 2024 (in Latvian only).
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use / person / day | |
|---|---|---|
| Staff costs | Confidential | |
| Data collection costs | Confidential | |
| Other costs | Confidential | |
| Total costs | Confidential |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs :
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 2768 | |
| Average Time required to complete the questionnaire in hours (T)1 | ~ 47 min | |
| Average hourly cost (in national currency) of a respondent (C) | Not applicable | |
| Total cost | Not applicable |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘re-contact time’)
17.1. Data revision - policy
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.
Revision policy guidelines - .2022
17.2. Data revision - practice
Published data are not revised.
17.2.1. Data revision - average size
Not applicable.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
| Survey name | Overview of the execution of research and development work in the business sector |
|---|---|
| Type of survey | Threshold survey |
18.1.2. Sample/census survey information
| Sampling unit | Enterprise |
|---|---|
| Stratification variables (if any - for sample surveys only) | Full sample - economic activity, enterprise size-class by number of employees |
| Stratification variable classes | 2 digit NACE, size-class |
| Population size | 91094 |
| Planned sample size | ~3000 |
| Sample selection mechanism (for sample surveys only) | Threshold sampling |
| Survey frame | Statistical Business Register mainly, as well as other data sources on enterprises received funding for R&D, enterprises with R&D personnel etc. |
| Sample design | The biggest part of enterprises were stratified using three indicators: number of employees (4 size classes); NACE group; “big” units with 100% sample. Hovewer, the largest enterprises were stratified separately (in each strata - 1 enterprise). The number of strata is 303 (44 of them were large enterprises). |
| Sample size | 2913 |
| Survey frame quality | Good |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Ministry of Economics Republic of Latvia, Investment and Development Agency of Latvia, Central Finance and Contracting Agency Republic of Latvia, Register of a Scientific Institutions etc. |
|---|---|
| Description of collected data / statistics | Information only |
| Reference period, in relation to the variables the administrative source contributes to | Not applicable |
| Variables the administrative source contributes to | Not applicable |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
| Realised sample size (per stratum) | 2913 |
|---|---|
| Mode of data collection | Online survey, telephone interview, questionnaire sent by e-mail |
| Incentives used for increasing response | None |
| Follow-up of non-respondents | Email reminders, repeated phone reminders |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | None |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 95.22% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Not applicable |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | Questionnaire: Pārskats par pētniecības un attīstības darbu izpildi uzņēmējdarbības sektorā 2023. gadā Explanatory notes: Informatīvais materiāls veidlapas aizpildīšanai |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | Not available |
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.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)
18.5.1.1. Imputation rate by Size class
Not available
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | ||||
| 10-49 employees and self-employed persons | ||||
| 50-249 employees and self-employed persons | ||||
| 250-and more employees and self-employed persons | ||||
| TOTAL | ||||
18.5.1.2. Imputation rate by NACE
Not available
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | ||||
| Services2) | ||||
| TOTAL | ||||
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
| Data compilation method - Final data | The survey is conducted annually. |
|---|---|
| Data compilation method - Preliminary data | Institutions submit questionnaires to the CSB until T+3.5, and then they are processed. Preliminary data are ready T+10. |
18.5.3. Measurement issues
| Method of derivation of regional data | Not applicable |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Not applicable |
18.5.4. Weighting and estimation methods
| Weight calculation method | The inverse of the sampling fraction was used as weights. In the realized sample weights = Nh /mh where Nh is the total number of enterprises in the stratum h of the population and mh is the number of enterprises in the realised sample in the stratum h, assuming that each unit in the stratum had the same inclusion probability. This will automatically adjust the sample weights of the respondents to compensate for unit non-response. |
|---|---|
| Data source used for deriving population totals (universe description) | Data source is the statistical Business Register. |
| Variables used for weighting | Nh is the total number of enterprises in the stratum h of the population and mh is the number of enterprises in the realised sample in the stratum h. |
| Calibration method and the software used | Not applicable |
| Estimation | Horwitz-Thompson estimation |
18.6. Adjustment
Not applicable
18.6.1. Seasonal adjustment
Not applicable
No comments.
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
31 October 2025
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
R&D statistics cover national and regional data.
Latvia
Calendar year - 2023
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
Annual
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
