1.1. Contact organisation
Statistical Office of the Republic of Serbia (SORS)
1.2. Contact organisation unit
Unit for statistics of education, science and culture
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Serbia
11050 Belgrade,
Milana Rakica 5
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
5 March 2026
2.2. Metadata last posted
5 March 2026
2.3. Metadata last update
16 March 2026
3.1. Data description
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
The Statistical Office of the Republic of Serbia 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.
3.2. Classification system
- The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
Yes according to Frascati Manual 2015 |
|---|---|
| Hospitals and clinics | Not included |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | Not included |
3.3.3. R&D variable coverage
| R&D administration and other support activities | yes, according to FM2015 |
|---|---|
| External R&D personnel | Not included, unless contracted particularly for R&D |
| Clinical trials: compliance with the recommendations in FM §2.61. | Not included |
3.3.4. International R&D transactions
Not available – please see note in section 3.1.
| Receipts from rest of the world by sector - availability | Not available |
|---|---|
| Payments to rest of the world by sector - availability | Not available |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | 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 enterprise) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Collected separately |
| Difficulties to distinguish intramural from extramural R&D expenditure | No such difficulties |
3.4. Statistical concepts and definitions
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
3.4.1. R&D expenditure
Not available – please see note in section 3.1.
| Coverage of years | Reference year 2023 |
|---|---|
| Source of funds | |
| Type of R&D | |
| Type of costs | |
| Economic activity of the unit | |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | |
| Product field | |
| Defence R&D - method for obtaining data on R&D expenditure |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Yes, according to FM2015 |
|---|---|
| Function | Yes, according to FM2015 |
| Qualification | Yes, according to FM2015 |
| Age | Available |
| Citizenship | Available for researchers |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Yes, according to FM2015 |
|---|---|
| Function | Yes, according to FM2015 |
| Qualification | Yes, according to FM2015 |
| Age | Available |
| Citizenship | Available only for researchers |
3.4.2.3. FTE calculation
R&D coefficients are used for the calculation and estimation of FTE data to report in line with FM
3.5. Statistical unit
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
3.6. Statistical population
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
3.6.1. National target population
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
Not available – please see note in section 3.1.
| 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 | ||
| Estimation of the target population size | ||
| Size cut-off point | ||
| Size classes covered (and if different for some industries/services) | ||
| NACE/ISIC classes covered |
3.6.2. Frame population – Description
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
| Method used to define the frame population | Based on organization's R&D activities, Frascatti manual. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Multiple sources, including organisational registers and administrative data. |
| Inclusion of units that primarily do not belong to the frame population | Includes units suspected of R&D, even if it is not primary activity. |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | |
| Systematic exclusion of units from the process of updating the target population | |
| Estimation of the frame population |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3. (data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
2023 calendar year.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Law on Official statistics |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not 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: Yes
- Confidentiality commitments of survey staff: Oath of Office
7.2. Confidentiality - data treatment
The protection of secret data and documents has been done in accordance with the Law on Data Secrecy. Confidential data from Article 3 of the Rulebook are considered official secret and cannot be published or communicated, that is, they cannot be part of aggregated data from which individual data can be identified. Individual data can be given only to the owner of those data. Only the employees of the Office authorized by decision of the Director of the Office shall have access to confidential data from administrative sources.
Also, if SORS transmits data with a confidentiality flag or an embargo date, these data are not disseminated until the confidentiality flag is lifted in a subsequent data transmission or the embargo expired.
8.1. Release calendar
The data are available on the last day of August in the results published on the website of the Institute, according to the official calendar of data publishing.
Statistical Office of the Republic of Serbia
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
External users can find information in the Bulletin and in the Statistical Release on the SORS's website.
Bulletin on R&D Main Indicators
Statistical Release on R&D Main Indicators
In line with the Community legal framework and the European Statistics Code of Practice Eurostat disseminates European statistics on Eurostat's website respecting professional independence and in an objective, professional and transparent manner in which all users are treated equitably. The detailed arrangements are governed by the Eurostat protocol on impartial access to Eurostat data for users.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.5 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | Release on Main R&D Indicators |
| 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 | Regular Bulletin on Main R&D Indicators |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Online Free Statistical Database
Statistical Office of the Republic of Serbia website.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | With special approval from Director. |
|---|---|
| Access cost policy | Not available. |
| Micro-data anonymisation rules | Proscribed by the "Procedure for granting access to individual data without identifiers (anonymized microdata)", the internal SORS document. |
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 | Aggregate figures | |
| Data prepared for individual ad hoc requests | Y | Both | Micro-data can be granted only with special approval from Director. |
| Other | Y |
1) Y – Yes, N - No
10.6. Documentation on methodology
Abbreviated methodology in the national language is available on the web site of the SORS.
R&D Methodology, and the ESS Metadata Handler
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Please see the sub-concept 10.7.1 in the full metadata view.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Yes. |
|---|---|
| Requests on further clarification, most problematic issues | By request. |
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
Quality is provided by strict implementation of definitions and conceptual frameworks of European Statistics, Frascati methodology and through validation of data. Data were collected from reliable sources applying standards with regard to the OECD methodology and ensuring a high degree of comparability across countries. Data on the R&D activities of business enterprises are collected based on their registered activity and financial statements (investments in science reported in the statistical annex, AOP 9091). In addition, reports from the Tax Administration on approved tax incentives for business entities that invested in scientific research or innovative activities are also used. All data on the number of research organizations and employees refer to the situation as of 31 December of the respective year, while data on research projects, income, and expenditures refer to the entire year.
Major deviations and inconsistencies were not detected.
12.1. Relevance - User Needs
Please see the sub-concept 12.1.1 in the full metadata view.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| European level: Commission (DGs, Secretariat General); International organizations | Eurostat, OECD, UNESCO | |
| Social actors | Employers' associations | |
| National media | Professional magazines, portals | |
| Researches and students | Professional researches, academy staff, students | |
| Other | National ministries, agencies and other government bodies |
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 | No specific survey has been conducted on the satisfaction of users. |
|---|---|
| User satisfaction survey specific for R&D statistics | No specific survey has been conducted on the satisfaction of users. |
| Short description of the feedback received |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
Data represents complete and consistent presentation of business enterprise statistics, in accordance with Eurostat requirements. Also, data on scientific workers - researchers are fully in line with Eurostat requirements.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Final variables only. |
| Obligatory data on R&D expenditure | No missing data (complete data). |
| Optional data on R&D expenditure | No missing data (complete data). |
| Obligatory data on R&D personnel | No missing data (complete data). |
| Optional data on R&D personnel | No missing data (complete data). |
| Regional data on R&D expenditure and R&D personnel | No missing data (complete data). |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y | Annual | ||||
| Type of R&D | Y | Annual | ||||
| Type of costs | Y | Annual | ||||
| Socioeconomic objective | Y | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | Annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | Y | Annual | ||||
| Age | Y | Annual | ||||
| Citizenship | Y | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | Annual | ||||
| Economic activity | Y | Annual | ||||
| Product field | Y | Annual | ||||
| Employment size class | Y | Annual |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | Y | Annual | ||||
| Age | Y | Annual | ||||
| Citizenship | Y | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | Y | Annual | ||||
| Economic activity | Y | Annual | ||||
| Product field | Y | Annual | ||||
| Employment size class | Y | Annual |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not available | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors andProcessing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non- response errors | ||||
| Total intramural R&D expenditure | +/- | +/- | +/- | +/- | +/- | +/- | +/- |
| Total R&D personnel in FTE | +/- | +/- | +/- | +/- | +/- | +/- | +/- |
| Researchers in FTE | +/- | +/- | +/- | +/- | +/- | +/- | +/- |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
Not available – please see note in section 3.1.
| Indicators |
5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | |||||
| Total R&D personnel in FTE | |||||
| Researchers in FTE |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
Not applicable.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | Not available | Not available | Not available |
| R&D personnel (FTE) | Not available | Not available | Not available |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | Not available | Not available | Not available | Not available | Not available |
| R&D personnel (FTE) | Not available | Not available | Not available | Not available | Not available |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors: Considered to be small
- Measures taken to reduce their effect: No measures needed
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.1.3. Frame misclassification rate
Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.
Not available – please see note in section 3.1.
| By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
|---|---|---|---|---|---|
| Number or surveyed enterprises in the stratum (according to frame) | |||||
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | |||||
| Misclassification rate | |||||
| By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99) | 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL |
| Number or surveyed enterprises in the stratum (according to frame) | |||||
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | |||||
| Misclassification rate |
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
- Description/assessment of measurement errors: Considered small
- Measures taken to reduce their effect: No measure needed
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response, which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response, which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate.
Definition:
- Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’
Definition:
- Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
- Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100
13.3.3.1.1. Unit non-response rates by Size Class
Not available – please see note in section 3.1.
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250-and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| Number of units with a response in the realised sample | Not available. | ||||
| Total number of units in the sample | |||||
| Unit Non-response rate (un-weighted) | |||||
| Unit Non-response rate (weighted) |
13.3.3.1.2. Unit non-response rates by NACE
Not available – please see note in section 3.1.
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | Not available. | ||
| Total number of units in the sample | |||
| Unit Non-response rate (un-weighted) | |||
| Unit Non-response rate (weighted) |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.3.3.1.3. Recalls/Reminders description
The SORS maintains constant contact with reporting units throughout the entire survey process. If an enterprise exceeds the prescribed deadline, reminders are sent and an official notice requesting the submission of the required data is issued.
The R&D survey is conducted in accordance with the Law on Official Statistics (“Official Gazette of the Republic of Serbia”, No. 109/25), and reporting units are legally obliged to provide statistical data.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No such specific survey has been conducted |
|---|---|
| Selection of the sample of non-respondents | |
| Data collection method employed | |
| Response rate of this type of survey | |
| The main reasons of non-response identified |
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
Not available – please see note in section 3.1.
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | Not available | ||
| Imputation (Y/N) | |||
| If imputed, describe method used, mentioning which auxiliary information or stratification is used |
13.3.3.3. Magnitude of errors due to non-response
Not available – please see note in section 3.1.
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | Not available |
| Total R&D personnel in FTE | |
| Researchers in FTE |
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
Not available – please see note in section 3.1.
| Data entry method applied | Not available |
|---|---|
| Estimates of data entry errors | |
| Variables for which coding was performed | |
| Estimates of coding errors | |
| Editing process and method | |
| Procedure used to correct errors |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: T + 10
- Lag (days): 300
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: T+18
- Lag (days): 545
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | ||
| Reasoning for delay |
15.1. Comparability - geographical
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
The comparability is ensured by the application of common definitions and methodological framework based on internationally methodologies and standards.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
Not available – please see note in section 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 Full-time equivalence (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). | ||
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | ||
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | ||
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | ||
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | ||
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | ||
| Reference period for all data | 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.
Not available – please see note in section 3.1.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | ||
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | ||
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | ||
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | ||
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | ||
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | ||
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | ||
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | ||
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | ||
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | ||
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | ||
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). |
15.2. Comparability - over time
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
Not available – please see note in section 3.1.
| Length of comparable time series | Break years1 | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | |||
| Function | |||
| Qualification | |||
| R&D personnel (FTE) | |||
| Function | |||
| Qualification | |||
| R&D expenditure | |||
| Source of funds | |||
| Type of costs | |||
| Type of R&D | |||
| Other |
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
Survey have been using same tools and rules and is a comprehensive in all calendar years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
R&D statistics are largely harmonized with the National Accounts. The SNA mainly uses R&D data to calculate preliminary GDP estimates and/or other calculations. More detailed information are not available at the moment.
15.3.3. National Coherence Assessments
Not available – please see note in section 3.1.
| 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 | |||||
15.4. Coherence - internal
Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
Not available – please see note in section 3.1.
| Total R&D expenditure (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | Not available | ||
| Final data (delivered T+18) | |||
| Difference (of final data) |
Comments :
....
15.4.2. Consistency between R&D personnel and expenditure
Not available – please see note in section 3.1.
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | ||
| 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
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use / person / day | |
|---|---|---|
| Staff costs | Not available separately | |
| Data collection costs | Not available separately | |
| Other costs | Not available separately | |
| Total costs |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs :
....
16.2. Components of burden and description of how these estimates were reached
Not available – please see note in section 3.1.
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | ||
| Average Time required to complete the questionnaire in hours (T)1 | ||
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
Data on research and development were collected by regular annual survey since 1965. Since 2007, the applied methodology has been harmonized with the international: Frascati Manual. The survey includes: all research and development organizations, regardless of whether it is their main activity or not. The basic source of data is the accounting records of costs and investments made in research and development. The annual report on research and experimental development provides data on resources (revenues, expenditures and investments) by type of research, sources of financing and purposes, and data for human capital as well. All data is available by type of research organization, according to fields of science, type of research activity, type of employment and length of working hours of employees, level of economic activities (KD2010 = NACE2) and according to the territorial principle (to the level of NUTS2). The basic set of survey is all organizations that are engaged in research and development (R&D), regardless of whether it is their core activity or not.
18.1.2. Sample/census survey information
Not available – please see note in section 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
Not available – please see note in section 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
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
Not available – please see note in section 3.1.
| Realised sample size (per stratum) | |
|---|---|
| Mode of data collection | |
| Incentives used for increasing response | |
| Follow-up of non-respondents | |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | |
| 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 18.5.4 Data compilation - Weighting and Estimation methods) |
|
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 in English |
| R&D national questionnaire and explanatory notes in the national language: | R&D National Questionnaire |
| Other relevant documentation of national methodology in English: | R&D Methodology in English |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
Data validation includes thorough check for accuracy and completeness. If necessary, another contact with enterprise is establishing for any clarification or additional information. Transmitted figures are checked both internally, by SORS and externally, by Eurostat.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)
18.5.1.1. Imputation rate by Size class
Not available – please see note in section 3.1.
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | ||||
| 10-49 employees and self-employed persons | ||||
| 50-249 employees and self-employed persons | ||||
| 250-and more employees and self-employed persons | ||||
| TOTAL | ||||
18.5.1.2. Imputation rate by NACE
Not available – please see note in section 3.1.
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | ||||
| Services2) | ||||
| TOTAL | ||||
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
Not available – please see note in section 3.1.
| Data compilation method - Final data | |
|---|---|
| Data compilation method - Preliminary data |
18.5.3. Measurement issues
Not available – please see note in section 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
| Weight calculation method | Not applicable |
|---|---|
| Data source used for deriving population totals (universe description) | Not applicable |
| Variables used for weighting | Not applicable |
| Calibration method and the software used | Not applicable |
| Estimation | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise sector, i.e. R&D expenditure and R&D personnel. In line with this objective, the target population for the national R&D survey of the business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).
The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
The Statistical Office of the Republic of Serbia 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.
16 March 2026
Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
Not requested. R&D statistics cover national and regional data.
2023 calendar year.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- 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 andProcessing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.
