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
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
7 July 2025
3.1. Data description
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the 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) Handbook 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 local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is 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
See below.
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
| Government sector | No deviations from FM15. |
|---|---|
| Hospitals and clinics | No deviations from FM15, university hospital included to HES. |
| Inclusion of units that primarily do not belong to GOV and the borderline cases. |
- |
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, but included for R&D institutions as overheads. |
|---|---|
| External R&D personnel | Not included. |
| Clinical trials: compliance with the recommendations in FM §2.61. | In compliance with FM15. |
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 | - |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | No |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | |
| Difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
|---|---|
| Source of funds | As in FM15, internal and external funds are included, transfer/exchange funds are not covered. |
| Type of R&D | As in FM15. |
| Type of costs | Investments are collected in some details, but capitalized computer software and other intellectual property products are not covered. |
| Defence R&D - method for obtaining data on R&D expenditure | Defence R&D expenditure is broken down in the breakdown of R&D expenditure by socio-economic objective. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | 31 December. |
|---|---|
| Function | In line with the FM15 methodology. |
| Qualification | In line with the FM15 methodology. |
| Age | In line with the FM15 methodology. |
| Citizenship | Available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
|---|---|
| Function | In line with the FM15 methodology. |
| Qualification | Not available. |
| Age | Not avaialble. |
| Citizenship | Not available. |
3.4.2.3. FTE calculation
The FM15 recommendations are followed.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
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 of institutional units.
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 Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| 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 | To identify the units performing R&D comprehensive list exists in SE that is regularly updated from statistical register, administrative (funds and agencies financing various activities including R&D) and other sources. | |
| Estimation of the target population size | To identify the units performing R&D comprehensive list exists in SE that is regularly updated from statistical register, administrative (funds and agencies financing various activities including R&D) and other sources. |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | frame population List of regular performers (scientific institutions, archives, museums, hospitals ) is comprehensive, non-regular performers (smaller museums, some hospitals) are communicated by phone or E-mail at the end of the year to establish their survey status |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | State budget, Register of scientific institutions (Ministry of Education and Science), funds and agencies financing various activities, internet sites of possible R&D performing units. The frame population comprises all units under the Government sector known or assumed to perform R&D activities. |
| Inclusion of units that primarily do not belong to the frame population | Not included. |
| Systematic exclusion of units from the process of updating the target population | Not included. |
| Estimation of the frame population | Not included. |
3.7. Reference area
Not requested.
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.
2023 calendar 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 Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. 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 |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics 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: Official Statistics Act § 34,§ 35, § 36,§ 37, § 38. Procedure for Protection of Data Collected and Processed by Statistics Estonia Government of the Republic.
- Confidentiality commitments of survey staff: Not applicable.
7.2. Confidentiality - data treatment
The data are published and transmitted without characteristics that permit identification of the respondent.
8.1. Release calendar
Statistical information is published on the website of the Statistical Office according to the official statistical calendar.
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
Annexes:
Release calendar national
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.
Annualy.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | |
| Ad-hoc releases | N |
1) Y - Yes, N – No
Annexes:
Releases
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 disseminated in full detail in the Statistical database.
Annexes:
Statistical database
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 the micro-data |
Legal persons and organisations can use for research confidential data held by Statistics Estonia. The data can be used on a safe centre computer or remotely, depending on the nature of the data and contract conditions. |
|---|---|
| Access cost policy | Access to microdata is paid. |
| Micro-data anonymisation rules | Microdata is anonymised. A unique anonymisation code is in use which can be used across all surveys. |
Annexes:
Use of 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 | aggregated figures | In public database |
| Data prepared for individual ad hoc requests | Y | microdata/aggregated data | At request. Microdata for anonymised use in a research environment |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
The data published on the website is provided with additional information that conveys the ESMS metadata information.
There are no separate instructions or methodology documents for the GOV sector, but there are instructions for the respondent as a handbook based on FM.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
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 reports. These reports are available online on website and followas a common standard for all official statistics. |
|---|---|
| Requests on further clarification, most problematic issues | N|o 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).
11.2. Quality management - assessment
The R&D statistics methodology is in line with FM15 methodology. Minor improvements can be achieved looking up possible R&D performers not detected yet to increase the coverage.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | Government Office of Estonia,Parliament, Ministries, political parties, governmental agencies and funds, municipalities of Tallinn and Tartu | Detailed data on capacity and trends of Estonian R&D performance for R&D and innovation and education policy decisions and strategy planning. |
| 2 | Estonian Employers’ Confederation | Detailed data on capacity and trends of Estonian R&D performance for R&D and innovation. |
| 3 | Media for general public | Analysis of changes in Estonian R&D performance together with international comparisons. |
| 4 | Researchers and students | Statistics, analysis and access to microdata. |
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 | Yes |
|---|---|
| User satisfaction survey specific for R&D statistics | Not available. |
| Short description of the feedback received | Not available. |
Annexes:
User surveys
12.3. Completeness
See below.
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 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | No missing variables. |
| Obligatory data on R&D expenditure | No missing variables. |
| Optional data on R&D expenditure | No missing variables. |
| Obligatory data on R&D personnel | No missing variables. |
| Optional data on R&D personnel | No information available on external R&D personnel. |
| Regional data on R&D expenditure and R&D personnel | 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 | 1996 | Annual | ||||
| Type of R&D | 1996 | Annual | ||||
| Type of costs | 1996 | Annual | ||||
| Socioeconomic objective | 1996 | Annual | ||||
| Region | N | Estonia is NUTS2 | ||||
| FORD | 1996 | Annual | ||||
| Type of institution | 1996 | 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 | 1996 | Annual | ||||
| Function | 1996 | Annual | 2023 | Information on technicians and other support staff is no longer collected separately, but is broken down into researchers and other R&D personnel. | ||
| Qualification | 1996 | Annual | ||||
| Age | 1996 | Annual | ||||
| Citizenship | 2004 | Annual | ||||
| Region | N | Estonia is NUTS2 | ||||
| FORD | 1996 | Annual | ||||
| Type of institution | 1996 | 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 | 1996 | Annualy | ||||
| Function | 1996 | Annualy | 2023 | Up to 2022 the data were estimated (technicians and other R&D peronnel), from 2023 onwards information is obtained through a questionnaire, but information on technicians and other support staff is no longer collected separately, but is broken down into researchers and other R&D personnel. | ||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | N | Estonia is NUTS2 | ||||
| FORD | 1996 | Annualy | ||||
| Type of institution | 1996 | Annualy |
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 |
|---|---|---|---|---|---|
| Intramural R&D Expenditure | 1996 | Yearly | R&D expenditures by source of funds and by SEO. | SEO | |
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').
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:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
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 | - | - | 1 | 1 | +/- | ||
| Total R&D personnel in FTE | - | - | 3 | 3 | +/- | ||
| Researchers in FTE | - | - | 3 | 3 | +/- | ||
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. 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 R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not applicable. |
| Government | Not applicable. |
| Higher education | Not applicable. |
| Private non-profit | Not applicable. |
| Rest of the world | Not applicable. |
| Total | Not applicable. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not applicable. |
| Technicians | Not applicable. | |
| other support staff | Not applicable. | |
| Qualification | ISCED 8 | Not applicable. |
| ISCED 5-7 | Not applicable. | |
| ISCED 4 and below | Not applicable. |
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 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 coverage errors were not detected.
- Measures taken to reduce their effect: No need of such measures.
- Share of PNP (if PNP is included in GOV): PNP is not included in GOV.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
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 known.
- Measures taken to reduce their effect: If such errors appear, they are contacted by phone.
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 satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
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
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 69 | 69 | 0 |
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 | 0 |
| Comments | The collecting application doesn't allow to provide data if the mandatory fields are not answered in the questionnaire. |
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. |
| Variables for which coding was performed | Online questionnaire, no coding. For foreign researchers respondent selects regions in which R&D is performed from the list provided. |
| 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 re-contacted 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: 27 June 2024.
- Lag (days): 178.
14.1.2. Time lag - final result
- End of reference period: 31 December 2023.
- Date of first release of national data: 27 June 2024.
- Lag (days): 178.
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
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No divergences from FM15.
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, 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). | Yes | External R&D personnel not included |
| Approach to obtaining 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 | Yes | In the absence of data on external R&D personnel |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No deviation | |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period | 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 method | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | ||
| Survey questionnaire / data collection form | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | ||
| Cooperation with respondents | FM2015 Chapter (mainly sub-chapter 8.5). | No deviation | ||
| Data processing methods | FM2015 Chapter 8 (mainly sub-chapters 8.5-8.6). | No deviation | ||
| Treatment of non-response | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | ||
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | ||
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | Only final data |
15.2. Comparability - over time
See below.
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) | Since 1996 | There has not been a direct time-line break in the GOV, but there has been an impact when some research institutions in the public sector have moved to the HES sector. | |
| Function | Since 1996 | ||
| Qualification | Since 1996 | ||
| R&D personnel (FTE) | Since 1996 | ||
| Function | Since 1996 | ||
| Qualification | Since 1996 | ||
| R&D expenditure | Since 1996 | ||
| Source of funds | Since 1996 | ||
| Type of costs | Since 1996 | ||
| Type of R&D | Since 1996 | ||
| Other | Since 1996 |
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
Data are collected annualy.
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.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Used as input to NA.The indicators of institutional sectors are internally coherent.
15.4. Coherence - internal
See below.
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 – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers(in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 59870 | 826 | 547 |
| Final data (delivered T+18) | 59870 | 826 | 547 |
| Difference (of final data) |
Comments:
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanations of consistency issues, if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 34703,75 | |
| 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 separately. | No subcontracted. |
| Data collection costs | Not available separately. | |
| Other costs | Not available separately. | |
| Total costs | Not available |
1) 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:
Not separately available.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | ||
| Average Time required to complete the questionnaire in hours (T)1) | 3.22 | This time cost includes all sectors together (GOV, HES and PNP) submitting data with the questionnaire on R&D expenditure. |
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
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
Annual survey "Research and Develeopment".
18.1.2. Sample/census survey information
| Sampling unit | Legal units categorised as governement sector (Institutes, departments, centres, museums, hospitals etc,). |
|---|---|
| Stratification variables (if any - for sample surveys only) | census |
| Stratification variable classes | census |
| Population size | census |
| Planned sample size | Census. |
| Sample selection mechanism (for sample surveys only) | census |
| Survey frame | Updated list of R&D performers. |
| Sample design | List of potential R&D performers. |
| Sample size | 69 |
| Survey frame quality | Good. |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Not available. |
|---|---|
| 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 | census |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | One type of providers — registered governmental or municipal institutions (scientific institutions, governmental agencies with R&D activities, museums etc.) |
|---|---|
| Description of collected information | a) The number of R&D personnel by field of science, by categories of R&D personnel, by gender, by level of formal qualification in the end of year; b) The researches by age, by gender, by citizenship in the end of year; c) The work-time in full time equivalent devoted to R&D during year (that is FTE) by field of science, by categories of R&D personnel and also by gender for researches; d) The intramural expenditure devoted to R&D during year by field of science, by sources of financing (government and foreign sources structured in details), by type of costs, by type of R&D activities, by socio-economic objectives). |
| Data collection method | Web-questionnaire with alternative possibility to load down a pdf-file and send filled by post or e-mail. |
| Time-use surveys for the calculation of R&D coefficients | Not appplicable |
| Realised sample size (per stratum) | Not appplicable |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Online filling up (with alternative possibility to make printout and send by postor by e-mail. |
| Incentives used for increasing response | Repeated reminders. |
| 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) | 100% |
| 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: | "Teadus- ja arendustegevus". Käsiraamat. |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: |
Annexes:
Questionnaire
Handbook
18.4. Data validation
Arithmetic and qualitative controls are used in the validation process, including comparison with previous year 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, Horizont2020, 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) Not relevant.
18.5.2. Data compilation methods
| Data compilation method - Final data | Annual census survey. |
|---|---|
| Data compilation method - Preliminary data | There are no separate preliminary and final data, data are collected annually by cencus survey. |
18.5.3. Measurement issues
| Method of derivation of regional data | Estonia is NUTS 2. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | No coefficients are estimated. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT excluded. |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not used |
|---|---|
| Description of the estimation method | No estimation is used, the sample is small and in case of errors the unit is contacted by telephone. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the 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) Handbook 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.
7 July 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested.
2023 calendar 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:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
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.
Annualy.
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.
See below.
See below.
