1.1. Contact organisation
State Statistical Office
1.2. Contact organisation unit
Department for research and development, innovations and ICT
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Dame Gruev 4, 1000 Skopje, Republic of North Macedonia
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
2.1. Metadata last certified
6 May 2026
2.2. Metadata last posted
6 May 2026
2.3. Metadata last update
6 May 2026
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.
The State Statistical Office of the Republic of North Macedonia has started recently to re-send R&D data to Eurostat; it might be that some concepts are not fully completed or applicable at this point in time. Concepts currently marked as "Not available'' or Not applicable" will be updated with the next publication.
3.3.2. Sector institutional coverage
| Government sector | Covered |
|---|---|
| Hospitals and clinics | Covered |
| Inclusion of units that primarily do not belong to GOV and the borderline cases. |
Not included |
3.3.3. R&D variable coverage
| R&D administration and other support activities | covered according to Frascati Manual |
|---|---|
| External R&D personnel | |
| Clinical trials: compliance with the recommendations in FM §2.61. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | In the question on financing of R&D the following categories can be distinguished: Other National Governments Higher Education European Commission International Organisations Foreign Business Enterprise Private Non-Profit Organisations |
|---|---|
| Payments to rest of the world by sector - availability | No |
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 | Not applicable |
| 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 | This is split between Private and Public Local and Foreign. Local Public is further split into General University Funds, Own Funds and Direct Government Funds whereas Local Private is split between Business Enterprise and Non-Profit Organisations. Foreign Public is split into Other National Governments, Higher Education, European Commission and International Organisations whilst Foreign Private is split between Foreign Business Enterprise and Non-Profit Organisations. |
| Type of R&D | All 3 types |
| Type of costs | This is split between Current and Capital Expenditure where Current expenditure includes Labour costs and Other current costs and Capital Expenditure includes Land and Buildings, Instruments and Equipemnt, Capitalized computer software and Other intellectual property products. |
| Defence R&D - method for obtaining data on R&D expenditure | Defence R&D is quantitatively negligible |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons employed during the calendar year |
|---|---|
| Function | All 3 types of functions are distinguished |
| Qualification | All Researchers are classified by ISCED Level 8, 7, 6, and 5. Technicians and Support staff are classified at ISCED Level 4 or below |
| Age | Not applicable |
| Citizenship | This applies to Researchers only: National Citizenship Citizenship of the EU Member States Citizenship of other European Countries Citizenship of North America Citizenship of Central and South America Citizenship of Asia Citizenship of Africa Other citizenship |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Total number of persons employed during the calendar year |
|---|---|
| Function | All 3 types of functions are distinguished |
| Qualification | All Researchers are classified by ISCED Level 8, 7, 6, and 5. Technicians and Support staff are classified at ISCED Level 4 or below |
| Age | Not applicable |
| Citizenship | This applies to Researchers only: National Citizenship Citizenship of the EU Member States Citizenship of other European Countries Citizenship of North America Citizenship of Central and South America Citizenship of Asia Citizenship of Africa Other citizenship |
3.4.2.3. FTE calculation
Full-Time Equivalent (FTE) calculation is a method to convert the hours worked by part-time or variable-hour employees into the equivalent of a full-time position. The core formula divides the total hours worked by all employees by the standard full-time hours for the same period
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 | Central Government Ministries and Departments, Extra Budgetary Units, as well as Local Councils | |
| Estimation of the target population size | 200 units |
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 | The units surveyed are all the units belonging to the Government Sector |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | All entities within the Government Sector are surveyed, and then this list is crosschecked with the entity responsible for R&D. Any new Government entity that benefitted from R&D grants is included in the list. |
| Inclusion of units that primarily do not belong to the frame population | None |
| Systematic exclusion of units from the process of updating the target population | None |
| Estimation of the frame population | 200 units |
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.
The reference period is the calendar year 2024.
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 | National: National: Law on State Statistics (“Official Gazette of the Republic of Macedonia” No.54/97, 21/07, 51/11, 104/13, 42/14, 192/15, 27/16, 83/18 and 220/18), and ( “Official Gazette of theRepublic of North Macedonia” No. 31/20), and Programme of Statistical Surveys 2018-2022(“Official Gazette of the Republic of Macedonia” No. 20/13, 24/14, 13/15, 7/16, 22/18 and 224/18),and ( “Official Gazette of the Republic of North Macedonia” No. 18/20 and 300/20). |
|---|---|
| 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: According to National statistical law mentioned.
- Confidentiality commitments of survey staff: Oath of Office.
7.2. Confidentiality - data treatment
Individual data are protected by the Law on State Statistics. Data collected with statistical surveys from the reporting units or indirectly from administrative or other sources are confidential data and are used only for statistical purposes. Results from the statistical processing may also generate information considered as confidential, for example: anonymised individual data, tables with low level of aggregation, as well as unreleased data. The Policy on Statistical Confidentiality contains the basic principles used in the SSO.
8.1. Release calendar
The date of data publication is determined in the Advance Release Calendar, which is updated quarterly.
8.2. Release calendar access
8.3. Release policy - user access
All users have equal access to statistics at the same time: this means that the publication dates are announced in advance and no user has access to official statistics before they are published. Statistical data are first published in the "News Releases" edition on the website of the State Statistical Office at 12:00.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
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
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | Y | |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y |
1) Y – Yes, N - No
10.3. Dissemination format - online database
MakStat database - Education and Science - Science - Research and Development
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 |
No |
|---|---|
| Access cost policy | No costs |
| Micro-data anonymisation rules | Not available for external users |
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 | Yes | Aggregate figures | A news release is issued in June/July. This release is also uploaded on the NSO’s website for future reference |
| Data prepared for individual ad hoc requests | Yes | ||
| Other | No |
1) Y – Yes, N - No
10.6. Documentation on methodology
Documentation on R&D Indicators Methodology
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.) | Graphs, metadata, methodologycal notes |
|---|---|
| Requests on further clarification, most problematic issues | No requests received |
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 State Statistical Office carries out statistical activities in accordance with the Statistical Business Process Model, which is based on the international model - Generic Statistical Business ProcessModel (GSBPM). The application of this model and international standards in statistical production ensures a high level of accuracy and comparability of data.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | Malta Council for Science and Technology | Public body established by the Central Government with the mandate of advising government on science and technology policy. Detailed data on capacity and trends of Malta's R&D performance for R&D and innovation and education policy decisions and strategy planning |
| 1 | Parliament, Ministries, Political Parties, Government Departments and International Organisations | Aggregated R&D data |
| 3 | Media for general public | Analysis of changes in Malta’s R&D performance together with international comparisons |
| 4 | Researchers and students | Statistics and analysis |
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 | The most recent User Satisfaction survey was carried out by the National Statistics Office in 2022. Occasionally we ask our main users to comment on the overall quality |
|---|---|
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | Our main users were asked to comment on the overall quality of our R&D data published. Their feedback was that the data is useful, on time and in sufficient detail |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
In terms of the indicators provided according to Commission Implementing Regulation (EU) No 2020/1197, SSO provides about 50% of them.
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.
Please see note in Concept 3.3.1.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | |
| Obligatory data on R&D expenditure | |
| Optional data on R&D expenditure | |
| Obligatory data on R&D personnel | |
| Optional data on R&D personnel | |
| Regional data on R&D expenditure and R&D personnel |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
Please see note in Concept 3.3.1.
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | ||||||
| Type of R&D | ||||||
| Type of costs | ||||||
| Socioeconomic objective | ||||||
| Region | ||||||
| FORD | ||||||
| Type of institution |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
Please see note in Concept 3.3.1.
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | ||||||
| Function | ||||||
| Qualification | ||||||
| Age | ||||||
| Citizenship | ||||||
| Region | ||||||
| FORD | ||||||
| Type of institution |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
Please see note in Concept 3.3.1.
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | ||||||
| Function | ||||||
| Qualification | ||||||
| Age | ||||||
| Citizenship | ||||||
| Region | ||||||
| FORD | ||||||
| Type of institution |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not applicable | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
Please see note in Concept 3.3.1.
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | |||||||
| Total R&D personnel in FTE | |||||||
| Researchers in FTE | |||||||
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. 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
Please see note in Concept 3.3.1.
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | |
| Government | |
| Higher education | |
| Private non-profit | |
| Rest of the world | |
| Total |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
Please see note in Concept 3.3.1.
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | |
| Technicians | ||
| other support staff | ||
| Qualification | ISCED 8 | |
| ISCED 5-7 | ||
| ISCED 4 and below |
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.
Please see note in Concept 3.3.1.
- Description/assessment of coverage errors :
- Measures taken to reduce their effect:
- Share of PNP (if PNP is 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.
Please see note in Concept 3.3.1.
- Description/assessment of measurement errors:
- Measures taken to reduce their effect:
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
Please see note in Concept 3.3.1.
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
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
Please see note in Concept 3.3.1.
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | |||
| Comments |
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 | The electronic filled-in questionnaire is uploaded to the R&D IT system. Data entry errors are non-existent |
|---|---|
| Estimates of data entry errors | The questionnaire is uploaded directly to the R&D IT system in order to eliminate data entry errors |
| Variables for which coding was performed | No codes are used; not applicable |
| Estimates of coding errors | No codes are used; not applicable |
| Editing process and method | The questionnaire has in-built checks to ensure consistency between the different tables, moreover after uploading the questionnaire into the R&D IT system the first step is a validation process that checks the questionnaire was filled in properly |
| Procedure used to correct errors | In case of logical inconsistencies or suspicious data values, the respondent is re-contacted by phone or e-mail |
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)
Please see note in Concept 3.3.1.
- End of reference period: 31 December 2024
- Date of first release of national data:
- Lag (days):
14.1.2. Time lag - final result
Please see note in Concept 3.3.1.
- End of reference period: 31 December 2024
- Date of first release of national data:
- Lag (days):
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
Please see note in Concept 3.3.1.
| 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) | ||
| Delay (days) | ||
| 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
Main concepts and definitions are used for the production of R&D statistics are given by the Frascati Manual. Comparability of data is ensured for the whole data series, without any break in the time series.
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.
Please see note in Concept 3.3.1.
| 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). | ||
| Researcher | FM2015, § 5.35-5.39. | ||
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | ||
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | ||
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | ||
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | ||
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | ||
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 |
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.
Please see note in Concept 3.3.1.
| 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). | ||
| Survey questionnaire / data collection form | FM2015 Chapter 8 (mainly sub-chapter 8.5). | ||
| Cooperation with respondents | FM2015 Chapter (mainly sub-chapter 8.5). | ||
| Data processing methods | FM2015 Chapter 8 (mainly sub-chapters 8.5-8.6). | ||
| Treatment of non-response | FM2015 Chapter 8 (mainly sub-chapter 8.5). | ||
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.7). | ||
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
Please see note in Concept 3.3.1.
| Length of comparable time series | Break years1) | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | 2004 | ||
| Function | 2004 | ||
| Qualification | 2004 | ||
| R&D personnel (FTE) | 2004 | ||
| Function | 2004 | ||
| Qualification | 2004 | ||
| R&D expenditure | 2004 | ||
| Source of funds | 2004 | ||
| Type of costs | 2004 | ||
| Type of R&D | 2004 | ||
| Other | 2004 |
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 produced every year. Data produced in the same way in the odd and even years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
R&D statistics are produced according to System of National Accounts (SNA) and Frascati Manual 2015. Data from the R&D survey of all sectors of performance are made available to National Accounts 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.
Please see note in Concept 3.3.1.
| 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) | |||
| Final data (delivered T+18) | |||
| Difference (of final data) |
15.4.2. Consistency between R&D personnel and expenditure
Please see note in Concept 3.3.1.
| 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) | ||
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) |
(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
Please see note in Concept 3.3.1.
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use/person/day | |
|---|---|---|
| Staff costs | ||
| Data collection costs | ||
| Other costs | ||
| Total costs |
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) | 200 | |
| Average Time required to complete the questionnaire in hours (T)1) | Not possible to estimate - respondents were not asked for the time taken to fill in the questionnaire | |
| Average hourly cost (in national currency) of a respondent (C) | Not possible to estimate the hourly cost of a respondent | |
| Total cost | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
Data sources are statistical surveys where each reporting unit deliveres individual questionnaire if in the calendar year had scientific research activity. Research and development, sector of business entities - IR.1 The business sector comprises business entities in the field of economy, organisations and institutions whose primary activity is production of goods and services (other than higher education) for sale.
18.1.2. Sample/census survey information
Please see note in Concept 3.3.1.
| Sampling unit | |
|---|---|
| Stratification variables (if any - for sample surveys only) | |
| Stratification variable classes | |
| Population size | |
| Planned sample size | |
| Sample selection mechanism (for sample surveys only) | |
| Survey frame | |
| Sample design | |
| Sample size | |
| Survey frame quality | |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
Please see note in Concept 3.3.1.
| Source | |
|---|---|
| Description of collected data / statistics | |
| Reference period, in relation to the variables the administrative source contributes to | |
| Variables the administrative source contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | As mentioned above (in point 2.2) the GOV sector comprises ministries, government departments, institutional units forming part of government and Local councils. A list of these units can be found in ANNEX 1. All these units are surveyed for R&D data collection |
|---|---|
| Description of collected information | a) The number of R&D personnel, by FT/PT, by field of science, by categories of R&D personnel, by gender, by level of qualification in the end of year; b) The researches, by FT/PT, by gender, by citizenship in the end of year; c) The intramural expenditure devoted to R&D during year by field of science, by sources of financing (local and foreign sources further split into more sources), by type of costs, by type of R&D activities, by socio-economic objectives. The FTE is calculated by dividing the PT employees by 3. It’s a ratio that was established at the NSO |
| Data collection method | A questionnaire in excel format is sent by email to enable the respondents to fill up the questionnaire electronically. Email reminders are sent if they do not reply on time and follow up by phone calls, if necessary |
| Time-use surveys for the calculation of R&D coefficients | None |
| Realised sample size (per stratum) | 200 |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | By email |
| Incentives used for increasing response | No incentives were used. |
| Follow-up of non-respondents | Two reminders sent by email. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | For non-respondents of known R&D performers, the last year's questionnaire is retained. |
| 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: | Survey of Research and Development in the General Government Sector.pdf |
| R&D national questionnaire and explanatory notes in the national language: | Not applicable |
| Other relevant documentation of national methodology in English: | Not applicable |
| Other relevant documentation of national methodology in the national language: | Not applicable |
18.4. Data validation
Data validation is done in accordance with the defined criteria for control. Initial check of the data is done by the responsible person for the survey in the subject matter department while receiving the completed questionnaires. Data validation is done after data entry too
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
Please see note in Concept 3.3.1.
| Data compilation method - Final data | |
|---|---|
| Data compilation method - Preliminary data |
18.5.3. Measurement issues
Please see note in Concept 3.3.1.
| Method of derivation of regional data | |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not applicable |
|---|---|
| Description of the estimation method | Not applicable |
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.
6 May 2026
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.
The reference period is the calendar year 2024.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
