1.1. Contact organisation
Statistics Estonia
1.2. Contact organisation unit
Economic and Environmental Statistics Department
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
51 Tatari Str, 10134 Tallinn, Estonia
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
30 October 2025
2.1. Metadata last certified
30 October 2025
2.2. Metadata last posted
30 October 2025
2.3. Metadata last update
30 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
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 |
Private and public enterprises only, no NPIs |
|---|---|
| Hospitals and clinics | Enterprise-type centres |
| Inclusion of units that primarily do not belong to BES | Not included |
3.3.3. R&D variable coverage
| R&D administration and other support activities | In case of projects they are reported as whole, in case of R&D performing units or individuals indirect supporting activities are not included |
|---|---|
| External R&D personnel | On-site consultants are not included in intramural R&D |
| Clinical trials: compliance with the recommendations in FM §2.61. | Included as recommended in FM |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available for all sectors |
|---|---|
| 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) | Not covered |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Not applicable |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not applicable |
3.4. Statistical concepts and definitions
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 year |
|---|---|
| Source of funds | According to FM |
| Type of R&D | According to FM |
| Type of costs | According to FM |
| Economic activity of the unit | Activity of the enterprise |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | N/A |
| Product field | N/A |
| Defence R&D - method for obtaining data on R&D expenditure | N/A |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | End of calendar year |
|---|---|
| Function | Data available by researchers and other R&D personnel. |
| Qualification | Data available by breakdown: PhD’s or equivalent (ISCED 8); university degree, polytechnics or equivalent, master or bachelor level (ISCED 6-7); other education |
| Age |
Data on R&D personnel by age (group) in head counts (HC) are available only for researchers. |
| Citizenship | No breakdown by citizenship is available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year |
|---|---|
| Function | Data available by researchers only. |
| Qualification | No breakdown by formal qualification is available. |
| Age | Not collected |
| Citizenship | Not collected |
3.4.2.3. FTE calculation
Working time spent on R&D by R&D personnel in person-years.
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 | Known and potential intramural R&D performers |
Not applicable |
| Estimation of the target population size | No | Not applicable |
| Size cut-off point | No | Not applicable |
| Size classes covered (and if different for some industries/services) | 0 and 1-9 size classes are considered as one. Data on subclasses 10-19, 20-49, 50-99, 100-249, 250-499, 500+ is available. |
Not applicable |
| NACE/ISIC classes covered | All | Not applicable |
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 | List of regular or irregular intramural R&D performers plus new-born enterprises of certain activities (R&D, High-Tech, Biotech) |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | List of regular or irregular intramural R&D performers is yearly updated with the information from government financed foundations supporting R&D and innovation related efforts of SMEs and from governmental agencies procuring R&D activities from enterprises. |
| 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 | To identify R&D performers not known yet among other firms the SBS Frame is used. To include new-born enterprises of certain activities (R&D, HT, Biotech) addition information about R&D activities is seeked from different kinds of media and from enterprise Websites. |
| 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 | No |
| Estimation of the frame population | Not applicable |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, 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.
R&D expenditure: calendar year
R&D personal: Last day of the year
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. 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 | No R&D specific legislation at the national level. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes, under the Official Statistics Act |
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 requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: The dissemination of data collected for the production of official statistics is based on the requirements laid down in §§ 34 and 35 of the Official Statistics Act.
- Confidentiality commitments of survey staff: Confidentiality agreements regarding the data we work with are obligatory for all employees.
7.2. Confidentiality - data treatment
The data are published and transmitted without characteristics that permit identification of the respondents, and
classified into groups of at least three persons, whereas the share of data relating to each person in aggregate data
shall not exceed 90%.
8.1. Release calendar
Notifications about the dissemination of statistics are published in the release calendar, which is available on thewebsite. Every year on 1 October, the release times of the statistical database, news releases, main indicators byIMF SDDS and publications for the following year are announced in the release calendar (in the case of publications –the release month).
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
All users have been granted equal access to official statistics: dissemination dates of official statistics are announced in advance and no user category (incl. Eurostat, state authorities and mass media) is provided access to official statistics before other users. Official statistics are first published in the statistical database. If there is also a news release, it is published simultaneously with data in the statistical database. Official statistics are available on the website at 8:00 a.m. on the date announced in the release calendar.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
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 | Y | |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | N | |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Data are published in the statistical database at Online Statistical Database under the subject areas “Economy / Science. Technology. Innovation / Research and development activities / R&D in business enterprise sector”.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | Not limited |
|---|---|
| Access cost policy | Free Website |
| Micro-data anonymisation rules | The dissemination of data collected for the purpose of producing official statistics is guided by the requirements provided for in § 33, § 34, § 35, § 36, § Policy on Access to Confidential Data
|
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1) | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | ||
| Data prepared for individual ad hoc requests | Y | On request | |
| Other | Y |
1) Y – Yes, N - No
10.6. Documentation on methodology
Frascati Manual. Proposed Standard Practice for Surveys on Research and Experimental Development, OECD (2015)
10.6.1. Metadata completeness - rate
Not requested.
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.) | Statistics in online database is accompanied with adequate metadata |
|---|---|
| Requests on further clarification, most problematic issues | Additional explanations (assistance) are provided to the users if required. |
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).
11.2. Quality management - assessment
The BES R&D statistics methodology is in line with FM.
Statistics Estonia performs all statistical activities according to an international model (Generic Statistical Business Process Model – GSBPM). According to the GSBPM, the final phase of statistical activities is overall evaluation using information gathered in each phase or sub-process; this information can take many forms, including feedback from users, process metadata, system metrics and suggestions from employees. This information is used to prepare the evaluation report which outlines all the quality problems related to the specific statistical activity and serves as input for improvement actions.
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 - International organisations | OECD | Data according to Commission Regulation 2020/1197 |
| 1 - National | President, Parliament, Ministries, political parties, governmental agencies and foundations, municipalities. | Detailed data on capacity and trends of Estonian R&D performance for R&D and innovation and education policy decisions and strategy planning. |
| 2- Social actors | Estonian Employers’ Confederation | Detailed data on capacity and trends of Estonian R&D performance for R&D and innovation. |
| 3- Media | Media for general public, specialised media for entrepreneurs and researches | Analysis of changes in Estonian R&D performance together with international comparisons. |
| 4- Researchers and students | Researchers and students | Statistics, analysis and access to microdata |
| 5- Enterprises or businesses | Enterprises and other business organisations | Data for the own market analysis, their marketing strategy |
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 | Since 1996 Statistics Estonia conducts reputation surveys and user surveys. The survey is conducted at least once a year, the existing as well as potential consumers are interviewed. The results of the surveys are applied to provide better services for consumers as well as in the improvement of products. |
|---|---|
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | Certainly there exist interest for some additional information, but this can be a subject for other statistical instruments. General surveys confirm high level of user's satisfaction. |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
All obligatory data for R&D personnel (HC, FTE) 100%
Data for R&D expenditures, all obligatory are 100%
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.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells | |
|---|---|---|---|---|---|---|
| Preliminary variables | X | |||||
| Obligatory data on R&D expenditure | X | |||||
| Optional data on R&D expenditure | X | |||||
| Obligatory data on R&D personnel | X | |||||
| Optional data on R&D personnel | X | |||||
| Regional data on R&D expenditure and R&D personnel | Not applicable | Estonia is NUTS2 |
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 | 1998 | Yearly | ||||
| Type of R&D | 1998 | Yearly | ||||
| Type of costs | 1998 | Yearly | ||||
| Socioeconomic objective | 1998 | Yearly | ||||
| Region | Not applicable Estonia is NUTS2 |
|||||
| FORD | Not available | |||||
| Type of institution | Not available |
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 | 1998 | Yearly | ||||
| Function | 1998 | Yearly | ||||
| Qualification | 1998 | Yearly | ||||
| Age | 2008 | Yearly | ||||
| Citizenship | N | |||||
| Region | Not applicable, Estonia is NUTS2 | |||||
| FORD | N | |||||
| Type of institution | N | |||||
| Economic activity | 1998 | Yearly | ||||
| Product field | 2000 | Yearly | ||||
| Employment size class | 1998 | Yearly |
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 | 1998 | Yearly | ||||
| Function | 1998 | Yearly | ||||
| Qualification | 1998 | Yearly | ||||
| Age | 2008 | Yearly | ||||
| Citizenship | N | |||||
| Region | Not applicable, Estonia is NUTS2 | |||||
| FORD | N | |||||
| Type of institution | N | |||||
| Economic activity | 1998 | Yearly | ||||
| Product field | 2000 | Yearly | ||||
| Employment size class | 1998 | Yearly |
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 |
|---|---|---|---|---|---|
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 available | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. 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 | Not applicable |
||||||
| Total R&D personnel in FTE | Not applicable |
||||||
| Researchers in FTE | Not applicable |
||||||
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
Not applicable.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | Not calculated, as there is census survey |
Not calculated, as there is census survey |
Not calculated, as there is census survey |
| R&D personnel (FTE) | Not calculated, as there is census survey |
Not calculated, as there is census survey |
Not calculated, as there is census survey |
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 calculated, as there is census survey |
Not calculated, as there is census survey |
Not calculated, as there is census survey |
Not calculated, as there is census survey |
Not calculated, as there is census survey |
| R&D personnel (FTE) | Not calculated, as there is census survey |
Not calculated, as there is census survey |
Not calculated, as there is census survey |
Not calculated, as there is census survey |
Not calculated, as there is census survey |
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: The main identifiers of the active R&D population are: active R&D enterprises from previous rounds, the Community Innovation Survey (CIS), as it addresses a particular question to R&D; and other administrative sources for which enterprises apply to benefit from funds or tax rebates related to R&D.
Enterprises which do not feature in any of the above categories, which however carry out R&D activities, would not be included in the target population, thus resulting in under coverage. Under coverage cannot, however, be quantified.
- Measures taken to reduce their effect: Multiple sources of information are used to update the register of known or assumed R&D performers. As they are the major contributors to the final R&D numbers, we feel confident in our coverage of the target population.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
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) | 137 | 225 | 195 | 54 | 611 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 0 | 0 | 0 | 0 | 0 |
| Misclassification rate | 0 | 0 | 0 | 0 | 0 |
| 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) | 524 | 255 | 107 | 33 | 919 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 0 | 0 | 0 | 0 | 0 |
| Misclassification rate | 0 | 0 | 0 | 0 | 0 |
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: Data collection and processing software includes controls to eliminate errors and logical inconsistencies. All errors and logical inconsistencies are consulted with respondents and corrected.
- Measures taken to reduce their effect: Arithmetic and qualitative controls are used in the validation process, including comparison with other data.
Before data dissemination the internal coherence of the data is checked.
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 | 547 | 439 | 289 | 84 | 1359 |
| Total number of units in the sample | 661 | 480 | 302 | 87 | 1530 |
| Unit Non-response rate (un-weighted) | 17.25 | 8.55 | 4.31 | 3.45 | 11.18 |
| Unit Non-response rate (weighted) | not applicable |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 559 | 800 | 1359 |
| Total number of units in the sample | 611 | 919 | 1530 |
| Unit Non-response rate (un-weighted) | 8.52 | 12.95 | 11.18 |
| Unit Non-response rate (weighted) | Not applicable |
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
By phone and email
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No need for non-response survey. |
|---|---|
| Selection of the sample of non-respondents | No need for non-response survey. |
| Data collection method employed | No need for non-response survey. |
| Response rate of this type of survey | No need for non-response survey. |
| The main reasons of non-response identified | No need for non-response survey. |
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) (%) | 0% | 0% | |
| Imputation (Y/N) | N | N | 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 | 8.25 |
| Total R&D personnel in FTE | 8.25 |
| Researchers in FTE | 8.25 |
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 | Electronic online questionnaire. |
|---|---|
| Estimates of data entry errors | Not applicable. Respondents fill the online questionnaire, which guides the respondents. |
| Variables for which coding was performed | Not applicable |
| Estimates of coding errors | Not applicable |
| Editing process and method | The data is checked by means of arithmetical and logical controls used within individual tables and between tables. Different ratios are calculated to compare head-count and FTE data, and expenditure and personnel data etc. In the case of major R&D performers their data is compared against administrative orother available data |
| Procedure used to correct errors | In case of logical inconsistencies or suspicious data values the respondent is recontacted by phone or e-mail for data editing |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: 02 December 2024
- Lag (days): 330
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 02 December 2024
- Lag (days): 330
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 |
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 requested.
15.1.2. General issues of comparability
No differences from Frascati Manual.
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). | No deviation | |
| 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 | Yes | size-classes 0 and 1–9 are not separable |
| 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
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | 2003 | NACE Rev. 2 section K included first time | |
| Function | 2003 | NACE Rev. 2 section K included first time | |
| Qualification | 2003 | NACE Rev. 2 section K included first time | |
| R&D personnel (FTE) | 2003 | NACE Rev. 2 section K included first time | |
| Function | 2003 | NACE Rev. 2 section K included first time | |
| Qualification | 2003 | NACE Rev. 2 section K included first time | |
| R&D expenditure | 2003 | NACE Rev. 2 section K included first time | |
| Source of funds | 2003 | NACE Rev. 2 section K included first time | |
| Type of costs | 2003 | NACE Rev. 2 section K included first time | |
| Type of R&D | 2003 | NACE Rev. 2 section K included first time | |
| Other | 2003 | NACE Rev. 2 section K included first time |
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 requested.
15.3.2. Coherence - National Accounts
R&D data are used in the SNA.
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, in uneven years no other sources exist. | ||||
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) | 405 609,3 | 4 524,2 | 3 034,1 |
| Final data (delivered T+18) | 405 609,3 | 4524,2 | 3 034,1 |
| Difference (of final data) | 0 | 0 | 0 |
Comments :
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not applicable | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not applicable |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use / person / day | |
|---|---|---|
| Staff costs | Not available | |
| Data collection costs | Not available | |
| Other costs | Not available | |
| Total costs | Not available |
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) | 1359 | The number of respondents |
| Average Time required to complete the questionnaire in hours (T)1 | 0,49 | Average report completion time of report submitters, hours per report (the report has a corresponding question) |
| Average hourly cost (in national currency) of a respondent (C) | Not available | |
| Total cost | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
| Survey name | R&D in enterprise |
|---|---|
| Type of survey | Census survey covering expert sample |
| Combination of sample survey and census data | Not applicable |
| Combination of dedicated R&D and other survey(s) | Not applicable |
| Sub-population A (covered by sampling) | Not applicable |
| Sub-population B (covered by census) | Not applicable |
| Variables the survey contributes to | The number of R&D personnel (HC) by categories of R&D personnel, by gender, by level of formal qualification in the end of year. The number of R&D personnel (FTE) by categories of R&D personnel during calendar year. Intramural expenditure on R&D by type of costs, by type of R&D and by type of product. The extramural expenditure on R&D by type of receiver. Sources of funds for intramural and extramural R&D. |
| Survey timetable-most recent implementation | Collection: May-September Publication: December |
18.1.2. Sample/census survey information
| Sampling unit | Enterprise |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable, census survey |
| Stratification variable classes | Not applicable, census survey |
| Population size | Not applicable, census survey |
| Planned sample size | Not applicable, census survey |
| Sample selection mechanism (for sample surveys only) | Not applicable, census survey |
| Survey frame | List of enterprises that are engaged in or are potentially engaged in research and development (R&D). The list is generated from the Business Register for Statistical Purposes List of entities who have received R&D support, compiled by the Environmental Investment Centre and Enterprise Estonia Enterprises who have reported R&D activities since 2016 (as part of various statistical activities) Enterprises with knowledge-intensive business activities that were born in the previous year |
| Sample design | Census survey among all potential R&D performers. |
| Sample size | 1530 |
| Survey frame quality | Good |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | census survey |
|---|---|
| Description of collected data / statistics | census |
| Reference period, in relation to the variables the administrative source contributes to | 2023 calendar year |
| 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) | 1359 |
|---|---|
| Mode of data collection | On-line survey |
| Incentives used for increasing response | Reminding letters, other contacts to the respondents |
| Follow-up of non-respondents | Repeated phone and e-mail reminding |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 89% |
| 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: | Ouestionnaire "Research and development (R&D) (in companies) 2023 (yearly)" |
| R&D national questionnaire and explanatory notes in the national language: | Küsimustik "Teadus- ja arendustegevus (ettevõttes)" |
| Other relevant documentation of national methodology in English: | Manual |
| Other relevant documentation of national methodology in the national language: | Käsiraamat |
18.4. Data validation
Arithmetic and qualitative controls are used in the validation process, including comparison with other data. Before data dissemination the internal coherence of the data is checked.
In determining the population and checking the received data, the data of foundations providing research support (Enterprise Estonia – EAS, Environmental Investment Centre – EIC, Estonian Reseach Council – ETAG) are used.
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
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | 0% | 0% | 0% | 0% |
| 10-49 employees and self-employed persons | 0% | 0% | 0% | 0% |
| 50-249 employees and self-employed persons | 0% | 0% | 0% | 0% |
| 250-and more employees and self-employed persons | 0% | 0% | 0% | 0% |
| TOTAL | 0% | 0% | 0% | 0% |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | 0% | 0% | 0% | 0% |
| Services2) | 0% | 0% | 0% | 0% |
| TOTAL | 0% | 0% | 0% | 0% |
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 | The final data can differ from preliminary data if some further checks reveal some errors. Otherwise the processing of BES data is finished at T+10. |
18.5.3. Measurement issues
| Method of derivation of regional data | Not applicable, Estonia is NUTS2 |
|---|---|
| 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 | VAT excluded |
18.5.4. Weighting and estimation methods
| Weight calculation method | Not used |
|---|---|
| Data source used for deriving population totals (universe description) | census |
| Variables used for weighting | census |
| Calibration method and the software used | census |
| Estimation | census |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
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.
30 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.
Not requested. R&D statistics cover national and regional data.
R&D expenditure: calendar year
R&D personal: Last day of the year
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
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.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
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.
