1.1. Contact organisation
Statistics Estonia
1.2. Contact organisation unit
Economic and Environmental Statistics Department
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
51 Tatari Str, 10134 Tallinn, Estonia
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
21 February 2024
2.2. Metadata last posted
21 February 2024
2.3. Metadata last update
21 February 2024
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- 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).
3.2.1. Additional classifications
| Additional classification used | Description |
| No additional classifications are used |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | In accordance with the definition in the Frascati Manual |
| Fields of Research and Development (FORD) | No deviations in R&D statistics collection |
| Socioeconomic objective (SEO by NABS) | No particularities, no more detailed breakdown |
3.3.2. Sector institutional coverage
| Higher education sector | Good coverage |
| Tertiary education institution | Included |
| University and colleges: core of the sector | Included |
| University hospitals and clinics | Included |
| HES Borderline institutions | Included (university research institutes) |
| Inclusion of units that primarily do not belong to HES | No |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Indirect support activities are estimated and included as overheads |
| External R&D personnel | PhD level postgraduate students engaged in R&D are considered as researches |
| Clinical trials | Included as recommended in Frascati Manual |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available |
| Payments to rest of the world by sector - availability | Not available |
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) 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 |
| Source of funds | Funding of education and R&D are clearly separated, except GUF funding |
| Type of R&D | As in Frascati Manual |
| 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 | SEO is collected |
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 | Covered |
| Qualification | Covered |
| Age | Covered |
| Citizenship | Covered |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year |
| Function | Covered |
| Qualification | Estimated from HC data by unit |
| Age | N/A |
| Citizenship | N/A |
3.4.2.3. FTE calculation
FTE are calculated by the respondents themselves, instructions for calculating the FTE can be found in the Handbook
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Available | in HC | Yearly |
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
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 HES Sector should consist of all R&D performing institutional 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 | All units are included to the sample | |
| Estimation of the target population size |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. 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.
For personnel data HC and FTE
Expenditure data are in euros
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. 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. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | mandatory |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No |
| Legal acts | Official Statistics Act |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Yes |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Yes |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Yes |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Yes |
| Planned changes of legislation | No |
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
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
a) Confidentiality protection required by law: Official Statistics Act § 34,§ 35, § 36,§ 37, § 38. Procedure for Protection of Data Collected and Processed by Statistics Estonia Government of the Republic Regulation No 41 of 29.01.2001 (RT I 2001, 14, 63), entered into force 4.02.2001
b) 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 respondents.
8.1. Release calendar
Notifications about the dissemination of statistics are published in the release calendar, which is available on the website.
8.2. Release calendar access
Release calendar
Annexes:
Release calendar
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
Yearly
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Y | Press release, Statistics by theme |
| Ad-hoc releases |
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 | Content, format, links, ... |
| General publication/article (paper, online) |
N | |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Data are disseminated in full detail in the Statistics database https://andmed.stat.ee/en/stat
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | Legal persons and organisations can use for research confidential data held byStatistics 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 | Yes |
| Micro-data anonymisation rules | Yes |
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 | In public database | |
| Data prepared for individual ad hoc requests | Y | At request | |
| Other |
1) Y – Yes, N - No
10.6. Documentation on methodology
Quality and metadata description: https://www.stat.ee/en/find-statistics/methodology-and-quality
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 |
| Request on further clarification, most problematic issues | |
| Measure to increase clarity | No need |
| Impression of users on the clarity of the accompanying information to the data | Not available |
11.1. Quality assurance
To assure the quality of processes and products, Statistics Estonia applies the EFQM Excellence Model, the European Statistics Code of Practice and the Quality Assurance Framework of the European Statistical System (ESS QAF).
Statistics Estonia is also guided by the requirements in § 7. “Principles and quality criteria of producing official statistics” of the Official Statistics Act.
11.2. Quality management - assessment
The HES R&D statistics methodology is in line with FM the only exception being the reporting unit. The problem is concerning only four data providers — our largest universities. But during dozen years of close co-operation with R&D management units in those universities we are pretty confident that the personnel of these units are competent and have knowledge about FM definitions and methodology. Therefore data compiled by them for the university as a whole body have sufficient quality. It is used not only for SE data collection but for administrative records or institution’s annual reports as well. So it is guaranteed that several data owners (SE, Ministry of Education and Science, Ministry of Finances, universities themselves) use and publish same figures.
This practice is for all concerned parties beneficial and therefore there is no need for short-term changes in this respect.
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,mmunicipalities of Tallinn and Tartu |
Detailed data on capacity and trendsof Estonian R&D performance for R&D and innovation and education policy decisions and strategy planning |
| 1 | Tertiary education institutions | Data for self-estimates and planning |
| 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 | https://www.stat.ee/en/statistics-estonia/about-us/user-surveys |
| User satisfaction survey specific for R&D statistics | Not available |
| Short description of the feedback received |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
All obligatory data for R&D personnel (HC, FTE) 100%
optional data- missing researchers breakdown by seniority level; extramural R&D personnel
Data for R&D expenditures, all obligatory are 100%
Optional data - missing data about Type of cost- Capitalized computer software and Other intellectual property products) also extramural R&D expenditures; breakdown by affiliation status; breakdown by sectfund- General university funds and Direct government
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. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 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 | Estonia as a whole is NUTS 2 level |
Criteria:
A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.
B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | 1996 | Yearly | ||||
| Type of R&D | 1996 | Yearly | ||||
| Type of costs | 1996 | Yearly | ||||
| Socioeconomic objective | 1996 | Yearly | ||||
| Region | N/A, Estonia is NUTS2 | |||||
| FORD | 1996 | Yearly | ||||
| Type of institution | 1996 | Yearly |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | 1996 | Yearly | ||||
| Function | 1996 | Yearly | ||||
| Qualification | 1996 | Yearly | ||||
| Age | 1996 | Yearly | ||||
| Citizenship | 2004 | Yearly | ||||
| Region | N/A, Estonia is NUTS2 | |||||
| FORD | 1996 | Yearly | ||||
| Type of institution | 1996 | 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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | 1996 | Yearly | ||||
| Function | 1996 | Yearly | ||||
| Qualification | 1996 (estimated) | Yearly | ||||
| Age | N/A | |||||
| Citizenship | N/A | |||||
| Region | N/A, Estonia is NUTS2 | |||||
| FORD | 1996 | Yearly | ||||
| Type of institution | 1996 | 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 |
| Not available |
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
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 errors | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | - | - | - | - | - | - | - |
| Total R&D personnel in FTE | - | - | - | - | - | - | - |
| Researchers in FTE | - | - | - | - | - | - | - |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
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' = In the event that at least one out of the three criteria above described would not be fully met.
3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.
5) 'Very Poor' = If all the three criteria are not met.
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
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
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) | ||
| Function | 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.
a) Description/assessment of coverage errors: N/A
b) Measures taken to reduce their effect:
13.3.1.1. Over-coverage - rate
Not applicable
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.
a) Description/assessment of measurement errors: the main errors occur in the calculation of FTE
b) Measures taken to reduce their effect: Guidelines in the Handbook, as well as advice to respondents
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)
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) |
| 20 | 20 | 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 variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| N/A | there is no item non-response,because the data collection system has controls that do not allow to submit a questionnaire if any of the data fields are not filled in |
13.3.3.3. Measures to increase response rate
Automatic reminders: 2 preventive reminders —8 days and 1 day before the deadline, 3 treminder after deadline—3 days, 7 days and 37 days after deadline. Additionally the telephone contacts were made if needed
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 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)
a) End of reference period:T+6
b) Date of first release of national data: T+6
c) Lag (days): 0
14.1.2. Time lag - final result
a) End of reference period: T+6
b) Date of first release of national data: T+6
c) Lag (days): 0
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 deviations from the 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 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 paragraph 5.2). | No | External personnel not included to the questionnaire |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat'EBS Methodological Manual on R&D Statistics). | No | |
| 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 | FTE for technicians and supporting staff is collected without sex aggregation |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | Yes | External personnel missing |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | No | |
| Survey questionnaire / data collection form | No | |
| Cooperation with respondents | No | |
| Coverage of external funds | No | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | Yes | |
| Data processing methods | No | |
| Treatment of non-response | No | |
| Variance estimation | Not applicable | |
| Method of deriving R&D coefficients | No | The R&D coefficients are not used at SE level, but they are used at reporting unit level and in such case are based on FM recommendations |
| Quality of R&D coefficients | No | |
| Data compilation of final and preliminary data | No |
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 timeline 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
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
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. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Compliance with the SNA is monitored
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 |
| Not available |
15.3.4. Coherence – Education statistics
.
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 – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | 185 213,3 | 2 869,0 | 2 417,2 |
| Final data (delivered T+18) | 185 213,3 | 2 869,0 | 2 417,2 |
| Difference (of final data) | 0 | 0 | 0 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 49398,1 |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | not availbale |
(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) | % sub-contracted1) | |
| Staff costs | ||
| Data collection costs | ||
| Other costs | ||
| Total costs | ||
| Comments on 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.
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 | 2,82 | 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. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | Research and development |
| Type of survey | Census |
| Combination of sample survey and census data | No |
| Combination of dedicated R&D and other survey(s) | No |
| Sub-population A (covered by sampling) | |
| Sub-population B (covered by census) | |
| Variables the survey contributes to | The number of R&D personnel (HC) by field of science, by categories of R&D personnel, by gender, by level of formal qualification in the end of year. The researches by age, by gender, by citizenship in the end of year. The work-time in man-years devoted to R&D during year (FTE) by field of science, by categories of R&D personnel and also by gender for researches. 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 |
| Survey timetable-most recent implementation | Collection: February-May Publication: June |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Legal unit | ||
| Stratification variables (if any - for sample surveys only) | |||
| Stratification variable classes | |||
| Population size | |||
| Planned sample size | 20 | ||
| Sample selection mechanism (for sample surveys only) | |||
| Survey frame | Updated list of R&D performers | ||
| Sample design | |||
| Sample size | 20 | ||
| Survey frame quality | very good |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | N/A |
| Description of collected data / statistics | N/A |
| Reference period, in relation to the variables the survey contributes to | N/A |
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 — tertiary education institutions (universities, professional higher education institutions and institutions associated with universities separately). |
| Description of collected information | Comments: 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 man-years 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 | N/A |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Online filling up |
| Incentives used for increasing response | |
| Follow-up of non-respondents | Repeated phone and e-mail reminders |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | N/A |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | N/A |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | |
| R&D national questionnaire and explanatory notes in the national language: | Questionnaire in national language "Teadus ja arendustegevus" |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: | Handbook |
Annexes:
Handbook
National questionnaire 2021
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
Not applicable
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | Not applicable |
| Data compilation method - Preliminary data | For HES the final data is available on T+6 |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | No R&D coefficients are used at SE level. They are used by some large universities for the estimation of FTE data to report. |
| Revision policy for the coefficients | |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not used on SE level. If used at reporting unit level they are based on recommendations of FM. |
18.5.4. Measurement issues
| Method of derivation of regional data | N/A, Estonia is NUTS2 |
| Coefficients used for estimation of the R&D share of more general expenditure items | N/A |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT excluded |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | N/A, education and R&D funding are clearly separated. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | N/A |
18.5.5. Weighting and estimation methods
| Description of weighting method | Not used |
| Description of the estimation method | N/A |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
21 February 2024
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
See below.
Not requested. R&D statistics cover national and regional data.
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.
For personnel data HC and FTE
Expenditure data are in euros
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
Yearly
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.
