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
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Milana Rakica 5
11050 Belgrade
Serbia
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
31 October 2023
2.2. Metadata last posted
31 October 2023
2.3. Metadata last update
31 October 2023
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
4 sectors are covered by the survey: Business sector, Government sector, Higher education sector, Private non-profit sector
Type of R&D organisation: R&D organisations are institutions and other legal entities, as well as units incorporated in business entities and institutions, which activity is completely or predominantly R&D-related. All the organisations are classified into:
- independent R&D institutes;
- Centre of extraordinary values: the status of the centre can be acquired by an institute, i.e. tertiary education institution or their organisational part(s) if they have achieved in a five-year period ultimate and internationally cognised scientific and professional results in a specific scientific discipline based on what they have an extended international scientific, technical and technological co-operation.
- R&D units of business entities; - tertiary education institutions;
- Non-profit organisations/associations.
Sector of performance is determined according to the division of the economic activity in which R&D is performed. There are five sectors:
- Business sector covers business entities and organisations which primary activity is the market production of goods and services, and their sale at economically significant prices.
- This sector includes also private non-profit organisations, as well as incorporated R&D units.
- Government sector includes organisations, department offices and other bodies furnishing common services, other than tertiary education, which cannot be provided under market conditions and reflects the economical and social policy of the society. By definition, this sector covers: activities of the administration, defence and public order; health, education, culture, recreation and other social services; promotion of economic growth and living standard, and technological development. The legal, executive and institutional structure should be included in this sector, whether these are funded from regular or extraordinary budget.
- Non-profit sector covers non-market, private non-profit organisation serving households free of charge or at low cost. These organisations may be created by citizens’ associations in order to provide goods and services to the members of the association or for general purposes. This sector includes professional associations, humanitarian organisations, trade associations, consumers’ associations, etc.
- Tertiary education sector covers universities, faculties and academies, whatever their funding sources and legal status. This sector includes R&D institutes and clinics operating under the direct control of or administered by the tertiary education organisation.
- Sector “abroad” covers organisations and individuals located beyond the political boundaries of the country, as well as related land owned by these organisations. It also includes all international organisations, including their facilities on the national territory
3.2. Classification system
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
| ISCED-F 2013 | International Standard Classification of Education |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Higher education sector covers universities and other institutions in which post secondary education takes place, irrespective of the source of financing (state and private). Research institues, experimental units under direct supervision of public higher education institutions are also covered. The main source used for identifying reporting units in HES is the survey on higher education and the Business register, Ministry for education, science and technological development. |
| Fields of Research and Development (FORD) | No deviations In R&D statistics collection. Major fields of science are in line with FM and also more detailed breakdowns for each major field of science |
| Socioeconomic objective (SEO by NABS) | acording to NABS |
3.3.2. Sector institutional coverage
| Higher education sector | Tertiary education sector covers universities, faculties and academies, whatever their funding sources and legal status. This sector includes R&D institutes and clinics operating under the direct control of or administered by the tertiary education organisation. |
| Tertiary education institution | Yes |
| University and colleges: core of the sector | Yes |
| University hospitals and clinics | na |
| HES Borderline institutions | na |
| Inclusion of units that primarily do not belong to HES | No |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviations |
| External R&D personnel | na |
| Clinical trials | na |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | yes |
| Payments to rest of the world by sector - availability | na |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | No |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | |
| Difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
| Source of funds | Domestic funding: Planned budgetary funds dedicated to R&D from the ministries; General funds of the university/faculty; Funds for R&D from other government funds, agencies and foundations; Funds for R&D from local authorities’ bodies; Funds for R&D from enterprises; Funds from tertiary education institutions; Funds for R&D from non-profit organizations; Own funds of the reporting unit; Other funds for R&D from own sources and Funds from abroad: Funds from enterprises (in the same group, other enterprises outside the group); Funds for R&D from foreign governments; Funds for R&D from the university and other tertiary education institutions; Funds for R&D from non-profit organizations; Funds for R&D from the European Commission; Funds for R&D from international organizations; Other foreign funds |
| Type of R&D | Basic, applied research and experimental development |
| Type of costs | Labour costs, other current costs, capital expenditures, no deviations. |
| Defence R&D - method for obtaining data on R&D expenditure | Counting records of the receipts and realised investments in R&D |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | The whole calendar year. |
| Function | All occupations are clasified as:researchers, professional associates, tehnicians and other personnel (supoer) |
| Qualification | For researchers and professional associates questionnaire requests levels 6-8 (ISCED-F 2013), for the others all levels |
| Age | Less than 25; 25-34; 35-44; 45-54; 55-64; 65 and more |
| Citizenship | Data are collected from 2007 onwares: citizenship - by geographical position of the country. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | The whole calendar year. |
| Function | All occupations are classified as:researchers, professional associates, technicians and other personnel (supoer) |
| Qualification | For researchers and professional associates questionnaire requests levels 6-8 (ISCED-F 2013), for the others all levels |
| Age | Less than 25; 25-34; 35-44; 45-54; 55-64; 65 and more |
| Citizenship | Data are collected from 2007 onwards: citizenship - by geographical position of the country. |
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.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| By educational attainment and qualification | FTE and HC | Every year |
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
Tertiary educational institutions (faculties and arts academies), which activity, pursuant to the Law, is education and R&D-related.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | all universities and faculties (private and state) | |
| Estimation of the target population size | na |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
Republic of Serbia (without data for Kosovo and Metohija).
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
Funds are presented in thousands of RSD. The data on employed staff and researchers relate to persons (shown as natural persons and as full-time equivalent).
The calendar year.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
| Legal acts / agreements | Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | SORS, as part of the ESS, fills in and reports on metadata and a quality report. |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | yes |
| Legal acts | The survey "Annual Report on Research and Development" is carried out on the basis of the Law on Official Statistics (“Official Journal of the RS”, number 104/2009), in accordance with the international lows |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | This is covered by the plan and program of statistical research adopted by the Serbian Government |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | The Official Statistics Law („Official Gazette of the RS“, No. 104/09) specifies the legal framework for the production and dissemination of official statistics and also for the organization of the system of official statistics of the Republic of Serbia. Nevertheless, the Official Statistics Law, together with the five-year Statistical Programme over the period 2021 – 2025 and the annual implementation plans, provides the Statistical Office of the Republic of Serbia (SORS) with a clear and broad legal mandate to collect and access the data needed for the execution of the Statistical Programme and the Implementation Plan. In addition, pursuant to Article 18, paragraph 2 of the Law on Official Statistics (“Official Gazette of the RS”, No 104/09) and Article 42, paragraph 1 of the Law on Government Administration (“Official Gazette of the RS” No 55/05, 71/05‐corrigendum, 101/07, 65/08, 16/11, 68/2012 - decision of the Constitutional Court, 72/2012, 7/2014 - decision of the Constitutional Court, 44/2014 и 30/2018 - other law ), the Government adopts every year a regulation that defines the plan for official statistics. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | The protection of secret data and documents shall be 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. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | The Statistical Office of the Republic of Serbia has an agreement on cooperation with a large number of organizations and institutions, both nationally and internationally, with which it exchanges information, data and experiences. There is also intensive cooperation between different organizational units within the Institute itself. |
| Planned changes of legislation | None |
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.
The Statistical Office of the Republic of Serbia has an agreement on cooperation with a large number of organizations and institutions, both nationally and internationally, with which it exchanges information, data and experiences.
There is also intensive cooperation between different organizational units within the Institute itself.
7.1. Confidentiality - policy
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
a) Confidentiality protection required by law:
Pursuant to Article 46 of the Law on Official Statistics (“Official Gazette of RS”, number 104/09), Articles 7 and 35 of the Law on Government Administration (“Official Gazette of RS ”, No 79/05, 101/07, 95/2010, 99/2014, 47/2018 and 30/2018 - other law) and Articles 9, 15, 16 and 18 of the Law on Free Access to Information of Public Interest (“Official Gazette of RS”, No120/04, 54 /07 104/09, 36/10 and 105/21), Director of the SORS hereby adopt Rulebook on statistical data protection in the statistical office of the Republic of Serbia. The Rulebook lays down the measures to be implemented so as to protect data and information in the SORS.
In addition, Regulation (EC) No 223/2009 on European statistics (recital 24 and Article 20(4)) of 11 March 2009 (OJ L 87, p. 164), stipulates the need to establish common principles and guidelines ensuring the confidentiality of data used for the production of European statistics and the access to those confidential data with due account for technical developments and the requirements of users in a democratic society.
b) Confidentiality commitments of survey staff:
The protection of secret data and documents shall be 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.
7.2. Confidentiality - data treatment
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.
8.2. Release calendar access
External users can find the exact date of publication in the blackout calendar located on the Institute's website.
8.3. Release policy - user access
External users can find information in the Bulletin and in the Statistical Release on the SORS's website.
https://publikacije.stat.gov.rs/G2022/PdfE/G20225685.pdf
https://publikacije.stat.gov.rs/G2022/PdfE/G20221231.pdf
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.
Annual.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Y | Bulletin on Scientific Research. https://publikacije.stat.gov.rs/G2022/PdfE/G20225685.pdf Statistical Release: Research and development activity https://publikacije.stat.gov.rs/G2022/PdfE/G20221231.pdf |
| 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 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | Bulletin on Scientific Research. https://publikacije.stat.gov.rs/G2022/PdfE/G20225685.pdf Statistical Release: Research and development activity https://publikacije.stat.gov.rs/G2022/PdfE/G20221231.pdf |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
Y | Complex publications as Statistical Yearbook, Statistical pocketbook; Bulletin, this publication contains the final results of statistical survey in a field. Systematic data show the possibility of a detailed comparison of phenomena in time. It contains the general information about R&D statistics; Statistical releases (short and quick informations) - Government budget appropriations or outlays for R&D, 2021/2022 |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Only with main indicators.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | all agregated data are public available but Micro-data are accessible on demand (filling in Form of the request for the use of micro-data in scientific-research needs) of user/researcher and special approved by Collegium of directors |
| Access cost policy | free of charge |
| Micro-data anonymisation rules |
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 | ||
| Data prepared for individual ad hoc requests | Yes | ||
| Other |
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.
https://publikacije.stat.gov.rs/G2023/Pdf/G202320002.pdf , and the ESS Metadata Handler
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.) | Methodology, International classifications, Questionnaures, Guidelines etc. All methodological documentation can be found at the website www.stat.gov.rs |
| Request on further clarification, most problematic issues | Statistical Office of the Republic of Serbia is currently working on criteria for quality management in statistical surveys. |
| Measure to increase clarity | n/a |
| Impression of users on the clarity of the accompanying information to the data | n/a |
11.1. Quality assurance
Quality is provided by strict implementation of definitions and conceptual frameworks of European Statistics, Frascati methodology and through validation of data. Major deviations and inconsistencies were not observed.
11.2. Quality management - assessment
The HES R&D statistics methodology is completely in line with FM methodology. Minor improvements can be achieved looking up possible R&D performers not detected yet to increase the coverage.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1 | Parliament, Ministries, political parties, governmental agencies, offices, funds, local communities | Detailed data on capacity and trends of Serbian R&D performance for R&D and innovation and education policy decisions and strategy planning |
| 2 | Tertiary education institutions | Data for self-estimates and planning |
| 3 | Media for general public | Analysis of changes in Serbian R&D performance together with international comparesins |
| 4 | Researchers and students | Statistics, analysis and access to microdata |
1) Users' class codification
1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes.)
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | Statistical Office conducts the Survey on user satisfaction generally, not for particular indicator. |
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | Up to now we didn't use user satisfaction surveys, but we plan to do so in the future. The national data delivered to the internationally requested data on the Eurostat/OECD harmonised R&D data collection. Practically there are no deviations in the classification of major fields of science and technology or variable deviations because the methodology is completely in line with Frascati methodology. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Not applicable.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | ||||||
| Obligatory data on R&D expenditure | X | |||||
| Optional data on R&D expenditure | X | |||||
| Obligatory data on R&D personnel | X | |||||
| Optional data on R&D personnel | X | |||||
| Regional data on R&D expenditure and R&D personnel | X |
Criteria:
A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.
B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Y from 2010 | year | ||||
| Type of R&D | Y from 2010 | year | ||||
| Type of costs | Y from 2010 | year | ||||
| Socioeconomic objective | N | |||||
| Region | Y from 2010 | year | ||||
| FORD | Y from 2010 | year | ||||
| Type of institution | Y from 2010 | year |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y from 2007 | year | ||||
| Function | Y from 2007 | year | ||||
| Qualification | Y from 2007 | year |
||||
| Age | Y from 2007 | year | ||||
| Citizenship | Y from 2007 | year | ||||
| Region | Y from 2007 | year | ||||
| FORD | Y from 2007 | year | ||||
| Type of institution | Y from 2007 | year |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y from 2007 | year | ||||
| Function | Y from 2007 | year | ||||
| Qualification | Y from 2007 | year | ||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y from 2007 | year | ||||
| FORD | Y from 2007 | year | ||||
| Type of institution | Y from 2007 | year |
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 |
| None |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ |
| Total R&D personnel in FTE | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ |
| Researchers in FTE | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ | ‘-‘ |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1 |
4 (Good)2 |
3 (Satisfactory)3 |
2 (Poor)4 |
1 (Very poor)5 |
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = In the event that at least one out of the three criteria above described would not be fully met.
3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
Not applicable.
13.2.1.1. Variance Estimation Method
Not applicable.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | Not applicable. Census survey. |
| Government | Not applicable. Census survey. |
| Higher education | Not applicable. Census survey. |
| Private non-profit | Not applicable. Census survey. |
| Rest of the world | Not applicable. Census survey. |
| Total | Not applicable. Census survey. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | Not applicable. Census survey. |
| Technicians | Not applicable. Census survey. | |
| Other support staff | Not applicable. Census survey. | |
| Qualification | ISCED 8 | Not applicable. Census survey. |
| ISCED 5-7 | Not applicable. Census survey. | |
| ISCED 4 and below | Not applicable. Census survey. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors: SORS is using the census method
b) Measures taken to reduce their effect:
13.3.1.1. Over-coverage - rate
Not applicable.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors: SORS is using the census method
b) 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)
Not applicable.
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
| Not applicable |
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
Not applicable.
13.3.3.2.1. Un-weighted item non-response rate
| R&D variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| Not applicable. |
13.3.3.3. Measures to increase response rate
Not applicable.
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.
Not applicable.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | For the survey an interactive web questionnaire is used to collect the data. Plausibility checks are carried out during the completion of the questionnaire by the respondent. Then another round of plausibility checks is done to identify implausible or missing data, which are clarified in direct contact with the units. Most of the respondents report via web questionnaire and the data is imported into a database. In case of paper questionaires the data are entered manually. Plausibility checks are carried out and respondents are contacted for necessary clarifications. |
| Estimates of data entry errors | Not applicable |
| Variables for which coding was performed | Not applicable |
| Estimates of coding errors | Not applicable |
| Editing process and method | Not applicable |
| Procedure used to correct errors | Not applicable |
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.
According to the Transmission Programme, annual data should be transmitted to Eurostat within 6 months after the end of the reference year, as previous data (t+6), and t+12 month, as final data.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
a) End of reference period: 2021
b) Date of first release of national data: 31.8. 2022. national data, sent to Eurostat, t+10
c) Lag (days): 300
First and final results are the same.
14.1.2. Time lag - final result
a) End of reference period: 2021
b) Date of first release of national data: 31.8. 2022. national data, sent to Eurostat, t+18
c) Lag (days): 540
First and final results are the same.
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.
The SORS submits the data within the prescribed time limit.
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)
Not applicable.
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
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
Not applicable.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
| R&D personnel | FM2015 Chapter 5 (mainly paragraph 5.2). | No | |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat'EBS Methodological Manual on R&D Statistics). | No | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | No | |
| Survey questionnaire / data collection form | n/a | |
| Cooperation with respondents | n/a | |
| Coverage of external funds | n/a | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | n/a | |
| Data processing methods | No | |
| Treatment of non-response | n/a | |
| Variance estimation | n/a | |
| Method of deriving R&D coefficients | n/a | |
| Quality of R&D coefficients | n/a | |
| Data compilation of final and preliminary data | No | The first results are also final |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
| R&D personnel (HC) | 2008 | ||
| Function | 2008 | Due to a changes in methodology and to program for data processing comparability over time is fully possiblefrom 2008. | |
| Qua lification | 2008 | ||
| R&D personnel (FTE) | 2008 | ||
| Function | 2008 | ||
| Qualification | 2008 | ||
| R&D expenditure | 2008 | ||
| Source of funds | 2008 | ||
| Type of costs | 2008 | ||
| Type of R&D | 2008 | ||
| 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
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
Indicators are coherent with macroeconomic indicators.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not applicable.
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
| Not available. There are no other statistics for which data from HES can be compared with. |
15.3.4. Coherence – Education statistics
The data for UOE have been collected through regular surveys conducted by SORS.
The R&D expenditure data in the education statistics are not available and we can not make a comparison.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | Data are consistent. | Data are consistent. | Data are consistent. |
| Final data (delivered T+18) | Data are consistent. | Data are consistent. | Data are consistent. |
| Difference (of final data) | Data are consistent. | Data are consistent. | Data are consistent. |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not available |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available |
(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.
Not available.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | n/a | n/a |
| Data collection costs | n/a | n/a |
| Other costs | n/a | n/a |
| Total costs | n/a | n/a |
| Comments on costs | ||
| n/a | ||
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) | 112 | |
| Average Time required to complete the questionnaire in hours (T)1 | Not available | |
| Average hourly cost (in national currency) of a respondent (C) | Not available | |
| Total cost | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
National data on investments are revised according to national schedule. General Revision policy as an official document is available on the SORS website. Revised data are available at SORS online database as soon as they become validated.
17.2. Data revision - practice
Not requested.
The published data should be regarded as final, unless otherwise stated. Corrections and revisions might occur.
Major changes in methodology are usually announced in advance and users are informed of revisions and major changes in methodology on the SORS website.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
The main data sources are: human resources records on employees appointed to R&D, accounting records on realised receipts and calculated investments in R&D, as well as records of specialised services on the result of R&D activities – projects, works etc.
18.1.1. Data source – general information
| Survey name | "Annualy report for research and development" |
| Type of survey | census survey |
| Combination of sample survey and census data | |
| Combination of dedicated R&D and other survey(s) | |
| Sub-population A (covered by sampling) | |
| Sub-population B (covered by census) | |
| Variables the survey contributes to | all list variables |
| Survey timetable-most recent implementation | 2019 |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Not applicable | ||
| Stratification variables (if any - for sample surveys only) | Not applicable | ||
| Stratification variable classes | Not applicable | ||
| Population size | 112 reporting units. Census survey. | ||
| Planned sample size | 112 reporting units. Census survey. | ||
| Sample selection mechanism (for sample surveys only) | Not applicable | ||
| Survey frame | Census survey. Higher education sector covers universities and other institutions in which post secondary education takes place, irrespective of the source of financing (state and private). Research institues, experimental units under direct supervision of public higher education institutions are also covered. The main source used for identifying reporting units in HES is the survey on higher education and the Business register, Ministry for education, science and technological development. | ||
| Sample design | Not applicable | ||
| Sample size | Not applicable | ||
| Survey frame quality | Not applicable |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | |
| Description of collected data / statistics | Data are collected using the postal survey, questionnaries sent by email or web questionnaire which is available on the website of the Statistical Office of the Republic of Serbia www.stat.gov.rs. Manual for filling out the questionnarie, and questionnaire can be found at the website www.stat.gov.rs. |
| Reference period, in relation to the variables the survey contributes to | Year |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | SORS |
| Description of collected information | Data are collected using the postal survey, questionnaries sent by email or web questionnaire. |
| Data collection method | census |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | The R&D survey is obligatory according to the Serbian national statistics act and national statistical programme. The questionnaire is sent to reporting units by post in the beginning od April. Enterprises that did not return answered questionnaires in time are reminded 2 times by remind-leters. Some important missing reporting units are reminded also by telephone. |
| 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. 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: | https://publikacije.stat.gov.rs/G2022/PdfE/G202224050.pdf |
| R&D national questionnaire and explanatory notes in the national language: | https://publikacije.stat.gov.rs/G2022/Pdf/G202224050.pdf |
| Other relevant documentation of national methodology in English: | https://publikacije.stat.gov.rs/G2022/PdfE/G202224051.pdf |
| Other relevant documentation of national methodology in the national language: | https://publikacije.stat.gov.rs/G2022/Pdf/G202224051.pdf |
18.4. Data validation
Data for science are checked for accuracy and completeness. Transmitted figures are screened both internally, by SORS and externally, by Eurostat.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Not applicable.
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | Not applicable |
| Data compilation method - Preliminary data | Not applicable |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | Not applicable. |
| Revision policy for the coefficients | Not applicable. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not applicable. |
18.5.4. Measurement issues
| Method of derivation of regional data | Micro data are processed to the level of NUTS 3, but published only on the level of NUTS 2 |
| 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 | |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | No differences |
18.5.5. 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 higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
4 sectors are covered by the survey: Business sector, Government sector, Higher education sector, Private non-profit sector
Type of R&D organisation: R&D organisations are institutions and other legal entities, as well as units incorporated in business entities and institutions, which activity is completely or predominantly R&D-related. All the organisations are classified into:
- independent R&D institutes;
- Centre of extraordinary values: the status of the centre can be acquired by an institute, i.e. tertiary education institution or their organisational part(s) if they have achieved in a five-year period ultimate and internationally cognised scientific and professional results in a specific scientific discipline based on what they have an extended international scientific, technical and technological co-operation.
- R&D units of business entities; - tertiary education institutions;
- Non-profit organisations/associations.
Sector of performance is determined according to the division of the economic activity in which R&D is performed. There are five sectors:
- Business sector covers business entities and organisations which primary activity is the market production of goods and services, and their sale at economically significant prices.
- This sector includes also private non-profit organisations, as well as incorporated R&D units.
- Government sector includes organisations, department offices and other bodies furnishing common services, other than tertiary education, which cannot be provided under market conditions and reflects the economical and social policy of the society. By definition, this sector covers: activities of the administration, defence and public order; health, education, culture, recreation and other social services; promotion of economic growth and living standard, and technological development. The legal, executive and institutional structure should be included in this sector, whether these are funded from regular or extraordinary budget.
- Non-profit sector covers non-market, private non-profit organisation serving households free of charge or at low cost. These organisations may be created by citizens’ associations in order to provide goods and services to the members of the association or for general purposes. This sector includes professional associations, humanitarian organisations, trade associations, consumers’ associations, etc.
- Tertiary education sector covers universities, faculties and academies, whatever their funding sources and legal status. This sector includes R&D institutes and clinics operating under the direct control of or administered by the tertiary education organisation.
- Sector “abroad” covers organisations and individuals located beyond the political boundaries of the country, as well as related land owned by these organisations. It also includes all international organisations, including their facilities on the national territory
31 October 2023
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
Tertiary educational institutions (faculties and arts academies), which activity, pursuant to the Law, is education and R&D-related.
See below.
Not requested. R&D statistics cover national and regional data.
Republic of Serbia (without data for Kosovo and Metohija).
The calendar year.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
Funds are presented in thousands of RSD. The data on employed staff and researchers relate to persons (shown as natural persons and as full-time equivalent).
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
The main data sources are: human resources records on employees appointed to R&D, accounting records on realised receipts and calculated investments in R&D, as well as records of specialised services on the result of R&D activities – projects, works etc.
Annual.
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.
According to the Transmission Programme, annual data should be transmitted to Eurostat within 6 months after the end of the reference year, as previous data (t+6), and t+12 month, as final data.
See below.
See below.
