1.1. Contact organisation
Ministry of education, science and innovation
1.2. Contact organisation unit
Directorate for science and research
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Vaka Đurovića bb, 81000 Podgorica, Montenegro
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
19 May 2026
2.1. Metadata last certified
19 May 2026
2.2. Metadata last posted
19 May 2026
2.3. Metadata last update
19 May 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.
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.Only activities fulfilling the Frascati criteria for R&D are included in the statistics.
"Given that the Ministry of Education, Science and Innovation became the official producer of R&D statistics in November 2024, additional methodological improvements and strengthening of the statistical production process are ongoing. Concepts currently marked as 'Not available' or 'Not applicable' will be updated in future publications as additional data and methodological evidence become available."
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
covered, according to Frascati Manual 2015 |
|---|---|
| Hospitals and clinics | private Hospitals included |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | no such cases |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Personnel directly engaged in R&D administration and related support activities are included in R&D personnel and corresponding labour costs. General administrative activities not directly related to R&D are excluded. No deviations from FM §2.122 are applied. |
|---|---|
| External R&D personnel | External R&D personnel directly contributing to R&D activities are included where identifiable. This includes consultants, external experts and personnel engaged through contractual arrangements. Internal and external R&D personnel are not separately available. |
| Clinical trials: compliance with the recommendations in FM §2.61. | Clinical trial activities fulfilling the Frascati Manual criteria for R&D are included in R&D statistics. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability |
|
|---|---|
| Payments to rest of the world by sector - availability | Extramural R&D expenditures are surveyed through the annual R&D survey. The questionnaire distinguishes expenditures on R&D services purchased from foreign:
|
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Foreign-controlled enterprises are covered if they perform R&D activities in Montenegro. Separate dissemination of R&D statistics for foreign-controlled enterprises is currently 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. The following classifications are distinguished: Domestic:
Foreign:
|
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Extramural R&D expenditures are collected separately from intramural R&D expenditures within the questionnaire. Definitions and explanatory notes based on the Frascati Manual are provided to respondents. Purchases of materials, software and services directly related to internal R&D projects are treated as intramural R&D expenditure. R&D assignments performed by external institutions are treated as extramural R&D expenditure. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Additional validation checks and follow-up communication with reporting units are carried out where necessary in order to avoid double counting and misclassification. |
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
| Coverage of years | Annual data collection is carried out for the reference year. |
|---|---|
| Source of funds | R&D expenditure is classified according to the source of funds. The questionnaire distinguishes:
|
| Type of R&D | R&D activities are classified into:
|
| Type of costs | R&D expenditure is classified into:
|
| Economic activity of the unit | Classification by main economic activity. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Not available. |
| Product field | Not available. |
| Defence R&D - method for obtaining data on R&D expenditure | Not available. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Annual data collection is carried out for the reference year. |
|---|---|
| Function | R&D personnel are classified into:
|
| Qualification | Researchers are classified according to ISCED 2011. |
| Age | Breakdowns by age groups are available. |
| Citizenship | Citizenship data are currently not available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Annual data collection is carried out for the reference year. |
|---|---|
| Function | R&D personnel are classified into:
|
| Qualification | Researchers are classified according to ISCED 2011. |
| Age | Breakdowns by age groups are available. |
| Citizenship | Citizenship data are currently not available. |
3.4.2.3. FTE calculation
Full-time equivalent (FTE) corresponds to the proportion of working time actually spent on R&D activities during the reference year. FTE are directly asked in the questionnaire. FTE is calculated in accordance with the OECD Frascati Manual 2015 methodology.
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.
The statistical unit is the enterprise.
The reporting unit is any enterprise in which at least one researcher worked on R&D activities during the reference year, either as an employee or as external personnel.
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.
| 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 | The national target population consists of enterprises performing or potentially performing R&D activities in Montenegro. Reporting units include enterprises in which at least one researcher worked on R&D activities during the reference year, either as an employee or as external personnel. |
Administrative and auxiliary sources are used to identify actual and potential R&D performers and to support updating of the survey frame. |
| Estimation of the target population size | Given the relatively small size of the national R&D system, efforts are made to ensure the broadest possible coverage of R&D-performing enterprises. |
The target population is updated using business registers, administrative sources, lists of licensed scientific and research institutions and results from previous R&D surveys. |
| Size cut-off point | None | |
| Size classes covered (and if different for some industries/services) | All | |
| NACE/ISIC classes covered | All |
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 | The frame population is defined using a combination of business registers, administrative sources, previous R&D survey results and lists of licensed scientific and research institutions. The frame is continuously updated in order to improve the identification of R&D-performing enterprises. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Known or potential R&D performers are identified using: business registers, administrative records, previous R&D surveys, official lists of licensed scientific and research institutions and information collected through the national R&D survey. Additional information may be obtained during validation and follow-up communication with reporting units. |
| Inclusion of units that primarily do not belong to the frame population | No such units are currently identified. |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | The frame population is reviewed and updated annually. Additional potential R&D performers are identified through administrative sources, business registers, survey activities and validation procedures conducted during the annual R&D survey process. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | Exact information on the number of newly identified R&D-performing enterprises is currently not available. |
| Systematic exclusion of units from the process of updating the target population | No systematic exclusion based on economic activity or enterprise size is applied. All potential R&D performers are included. |
| Estimation of the frame population | The exact size of the frame population is currently not available. The frame population includes enterprises identified as actual or potential R&D performers through business registers, administrative sources and previous survey activities. |
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.
Calendar year.
The current transmission refers to the reference year 2023.
Please see sub-concepts 6.1 and 6.2.
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 | The legal basis for the production of official statistics in Montenegro is established by the Law on Official Statistics and Official Statistics System of Montenegro (“Official Gazette of Montenegro”, No. 18/12, 47/19 and 23/25). The Ministry of Education, Science and Innovation is designated as an official producer of R&D statistics within the national statistical system through the Programme of Official Statistics and the Annual Plan of Official Statistics. The production of R&D statistics is based on the national methodology aligned with the OECD Frascati Manual 2015 and Commission Implementing Regulation (EU) No 2020/1197. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes. In accordance with the Law on Official Statistics and Official Statistics System of Montenegro, reporting units are obliged to provide accurate and complete statistical data required for the implementation of official statistical surveys. Statistical confidentiality and protection of individual data are guaranteed by law. |
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:
Statistical confidentiality in Montenegro is regulated by the Law on Official Statistics and Official Statistics System of Montenegro (“Official Gazette of Montenegro”, No. 18/12, 47/19 and 23/25).
The Law defines the principles of confidentiality, professional independence and protection of individual data within the official statistical system. Individual data collected for statistical purposes are treated as confidential and may be used exclusively for statistical purposes. Confidential data may not be used for administrative, legal, tax or inspection purposes.
Statistical results are disseminated only in aggregated form, in a manner that prevents direct or indirect identification of reporting units. Access to confidential data is restricted exclusively to authorised persons involved in the production of official statistics.
The confidentiality framework is aligned with:
-
- Regulation (EC) No 223/2009 on European statistics;
- the European Statistics Code of Practice;
- ESS confidentiality principles.
- Confidentiality commitments of survey staff:
- Employees involved in the production, processing and dissemination of official statistics are legally obliged to protect confidential statistical data.
- Authorised staff members sign confidentiality statements and are granted access only to data necessary for performing their statistical tasks.
7.2. Confidentiality - data treatment
Data are disseminated only in aggregated form.
Statistical disclosure control measures are applied where necessary in order to prevent direct or indirect identification of reporting units.
Microdata are not publicly available.
During data validation and processing, access to individual records is restricted to authorised staff responsible for statistical production and quality control procedures.
Please see sub-concepts 8.1 to 8.3.
8.1. Release calendar
The dissemination of official statistics is carried out in accordance with the Statistical Release Calendar adopted within the national statistical system.
Release dates are defined in advance and made publicly available.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
Montenegro: Kalendar2026_eng_210125.pdf
8.3. Release policy - user access
Official statistics are made available to all users simultaneously.
Data dissemination is carried out in accordance with the principles of transparency, equal treatment and impartiality defined by the Law on Official Statistics and Official Statistics System of Montenegro.
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 | N | |
| 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 | N | |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Online Free Database
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | Access to microdata is restricted. |
|---|---|
| Access cost policy | There is a fee. |
| Micro-data anonymisation rules | Microdata are not publicly available. |
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 | N | ||
| Data prepared for individual ad hoc requests | N | ||
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Documentation on methodology accompany the data
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.) | Quality management is based on:
|
|---|---|
| Requests on further clarification, most problematic issues | There are currently no specific issues that regularly require additional clarification from users. As the national R&D statistical system is under further development and harmonisation with international standards, occasional methodological clarifications may be provided regarding concepts such as FTE, distinction between intramural and extramural R&D expenditure and sector classifications. |
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).
The national methodology for R&D statistics is aligned with OECD Frascati Manual 2015 and Commission Implementing Regulation (EU) No 2020/1197.
11.2. Quality management - assessment
The overall quality of Business Enterprise R&D (BERD) statistics in Montenegro is assessed as satisfactory, taking into account the current stage of development and consolidation of the national R&D statistical system.
The production of R&D statistics is based on internationally recognised methodological standards, primarily the OECD Frascati Manual 2015, Commission Implementing Regulation (EU) No 2020/1197 and the European Business Statistics Methodological Manual on R&D Statistics.
The statistical process is supported by:
- annual R&D surveys;
- use of multiple administrative and statistical sources;
- business registers;
- previous R&D survey results;
- lists of licensed scientific and research institutions.
Special attention is devoted to:
- identification of R&D performers;
- validation of R&D expenditure and personnel data;
- consistency checks between labour costs, personnel and FTE data;
- distinction between intramural and extramural R&D expenditure;
- plausibility checks and follow-up communication with reporting units.
Methodological coordination and quality monitoring are carried out in cooperation with the Statistical Office of Montenegro (MONSTAT), as the central coordinator of the national statistical system.
Given that the Ministry of Education, Science and Innovation became the official producer of R&D statistics in 2024, additional methodological improvements and strengthening of the statistical production process are ongoing.
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’ class |
Description of users |
Users’ needs |
|---|---|---|
| 1 |
Ministry of Education, Science and Innovation |
Detailed information on R&D activities, monitoring of national science and innovation policies, preparation of strategic documents and international reporting obligations. |
| 1 |
Statistical Office of Montenegro (MONSTAT) |
Coordination within the official statistical system, methodological harmonisation and international statistical reporting. |
| 1 |
Eurostat |
Harmonised R&D statistics in accordance with EU legislation and ESS standards. |
| 1 |
OECD |
Internationally comparable R&D indicators and methodological compliance with the Frascati Manual. |
| 1 |
Other ministries and public institutions |
Information on R&D expenditure, human resources in science and research, sectoral and policy-related analyses. |
| 1 |
Universities and public research institutions |
Information on researchers, scientific fields, R&D expenditure and research capacities. |
| 1 |
Regional and local public institutions |
Information relevant for regional and local development policies and monitoring of innovation activities. |
| 2 |
Chamber of Economy of Montenegro and business associations |
Information on business R&D activities, innovation capacity and competitiveness indicators. |
| 3 |
Media |
Official statistical indicators and analytical information related to science, research and innovation activities. |
| 4 |
Researchers, analysts and students |
Access to aggregated R&D data, metadata and internationally comparable indicators for research and academic purposes. |
| 5 |
Enterprises and consultancy companies |
Benchmarking, market analysis and information on R&D trends and business innovation activities. |
| 6 |
General public |
General information on research and innovation activities and development trends in Montenegro. |
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 user satisfaction survey for R&D statistics has yet been conducted. User needs and feedback are monitored through regular communication with national institutions. |
|---|---|
| User satisfaction survey specific for R&D statistics | No specific user satisfaction survey for R&D statistics is currently undertaken. |
| Short description of the feedback received | Not applicable |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
All available compulsory data were delivered.
Please see note in Concept 3.3.1.
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 | All available compulsory data were delivered. |
| Obligatory data on R&D expenditure | Available data were delivered in accordance with current national availability. |
| Optional data on R&D expenditure | Some optional breakdowns are currently not available, including certain regional and international classifications. |
| Obligatory data on R&D personnel | Available compulsory personnel data were delivered. |
| Optional data on R&D personnel | A distinction between internal and external personnel is currently not separately available. |
| Regional data on R&D expenditure and R&D personnel | Regional breakdown according to NUTS classification is currently not available. |
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 | Annual | |||||
| Type of R&D | Annual | |||||
| Type of costs | Annual | |||||
| Socioeconomic objective | N | |||||
| Region | N | |||||
| FORD | Annual | |||||
| Type of institution | 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 | Annual | |||||
| Function | Annual | |||||
| Qualification | Annual | |||||
| Age | Annual | |||||
| Citizenship | N | |||||
| Region | N | |||||
| FORD | N | |||||
| Type of institution | N | |||||
| Economic activity | N | |||||
| Product field | N | |||||
| Employment size class | N |
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 | Annual | |||||
| Function | Annual | |||||
| Qualification | Annual | |||||
| Age | Annual | |||||
| Citizenship | N | |||||
| Region | N | |||||
| FORD | N | |||||
| Type of institution | N | |||||
| Economic activity | N | |||||
| Product field | N | |||||
| Employment size class | N |
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 |
|---|---|---|---|---|---|
| Extramural R&D expenditure |
Annual | Domestic / abroad |
|||
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 |
|---|---|---|
| Function x sex x qualification available |
Headcounts, FTE | Annual |
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non- response errors | ||||
| Total intramural R&D expenditure | Not applicable |
5 | 4 | ||||
| Total R&D personnel in FTE | Not applicable |
5 | 4 | ||||
| Researchers in FTE | Not applicable |
5 | 4 | ||||
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | x | ||||
| Total R&D personnel in FTE | x | ||||
| Researchers in FTE | x |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (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 applicable. |
||
| R&D personnel (FTE) | Not applicable. |
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 applicable. |
||||
| R&D personnel (FTE) | Not applicable. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors (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.
a) Description/assessment of coverage errors:
Coverage errors may arise due to differences between the target population and the frame population.
Potential coverage errors are mainly related to:
- occasional or previously unidentified R&D performers;
- newly established enterprises;
- enterprises whose R&D activities are not visible through available administrative sources;
- difficulties in identifying enterprises performing small-scale or irregular R&D activities.
The frame population is established using:
- business registers;
- administrative sources;
- lists of licensed scientific and research institutions;
- results from previous R&D surveys.
The target population includes enterprises identified as actual or potential R&D performers during the reference year.
Given the relatively small size of the national R&D system, the impact of coverage errors is considered limited, although some undercoverage of smaller or occasional R&D performers may still exist.
b) Measures taken to reduce their effect:
Additional checks are carried out during data validation in order to identify enterprises potentially performing R&D activities that were not previously included in the target population.
Information obtained during the survey process is additionally used to improve and update the statistical frame for future survey rounds.
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.
Please see note in Concept 3.3.1.
| 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.
a) Description/assessment of measurement errors:
Measurement errors may occur during data collection and reporting processes. Additional measurement difficulties may arise when respondents are not fully familiar with the Frascati Manual concepts and definitions. The national R&D survey is based on a structured questionnaire accompanied by methodological explanations and definitions aligned with OECD Frascati Manual 2015 recommendations. The impact of measurement errors is considered limited due to the application of validation procedures and follow-up communication with reporting units.
b) Measures taken to reduce their effect:
Where necessary, reporting units are re-contacted in order to clarify inconsistencies, missing values or implausible data. Continuous efforts are being made to improve questionnaire design, methodological instructions and validation procedures.
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 Concept 3.3.1.
| 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 | |||||
| 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 Concept 3.3.1.
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | |||
| 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
Follow-up communication with reporting units is carried out where questionnaires are incomplete, inconsistent or missing.
Reminders and clarification requests may be sent through e-mail communication and telephone contacts.
13.3.3.1.4. Unit non-response survey
Please see note in Concept 3.3.1.
| Conduction of a non-response survey | No. |
|---|---|
| Selection of the sample of non-respondents | Not applicable. |
| Data collection method employed | Not applicable. |
| Response rate of this type of survey | Not applicable. |
| The main reasons of non-response identified | 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
13.3.3.2.1. Un-weighted item non-response rate
Not available.
Please see note in Concept 3.3.1.
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | |||
| 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
Please see note in Concept 3.3.1.
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | |
| 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
| Data entry method applied | Data collection is primarily carried out through electronic questionnaires. |
|---|---|
| Estimates of data entry errors | Not available. |
| Variables for which coding was performed | Not available. |
| Estimates of coding errors | Not available. |
| Editing process and method | Not available. |
| Procedure used to correct errors | The main method used for correcting errors and completing missing values is follow-up communication with reporting units, primarily through e-mail and additional clarification requests. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
a) End of reference period: 31 December 2023
b) Date of first release of national data: 2026
c) Lag (days):
14.1.2. Time lag - final result
a) End of reference period: 31 December 2023
b) Date of first release of national data:
c) Lag (days):
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
14.2.1.1. Deadline and date of data transmission
| 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) | In accordance with transmission schedule. |
In accordance with transmission schedule. |
| Delay (days) | ||
| Reasoning for delay | Additional time may be required due to validation procedures, methodological harmonisation, retrospective data collection and consolidation of statistical data sources. |
Additional time may be required due to validation procedures, methodological harmonisation, retrospective data collection and consolidation of statistical data sources. |
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
No major comparability issues are currently known.
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.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation |
|
| Researcher | FM2015, §5.35-5.39. | No deviation |
|
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation |
|
| 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 deviation |
|
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | Partial deviation |
A separate distinction between internal and external R&D personnel is currently not fully available. |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No deviation |
|
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | No deviation |
|
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation |
|
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation |
|
| 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). | No deviation |
|
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation |
|
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
|
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
|
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
|
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation |
|
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation |
|
| Cooperation with respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation |
|
| Follow-up of non-respondents | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation |
|
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation |
|
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | Not applicable |
|
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | Not applicable |
|
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | Not applicable |
|
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
|
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation |
|
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation |
|
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation |
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
The period 2021–2023 was compiled retrospectively.
Please see note in Concept 3.3.1.
| 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
R&D statistical surveys in Montenegro are currently carried out on an annual basis.
For the establishment of continuous time series and alignment with EU reporting requirements, retrospective surveys were conducted for the period 2021–2023.
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
The production of R&D statistics is coordinated with MONSTAT in accordance with the principles of the official statistical system of Montenegro.
15.3.3. National Coherence Assessments
Please see note in Concept 3.3.1.
| 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 |
|---|---|---|---|---|---|
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.
Please see note in Concept 3.3.1.
| 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) | |||
| Final data (delivered T+18) | |||
| Difference (of final data) |
Comments :
At the current stage of development of the national R&D statistical system, preliminary and final data comparisons are limited.
15.4.2. Consistency between R&D personnel and expenditure
Please see note in Concept 3.3.1.
| Average remuneration per year (cost in national currency) | 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 separately available. |
No work sub-contracted to third parties. |
| Data collection costs | Not separately available. |
No work sub-contracted to third parties. |
| Other costs | Not separately available. |
No work sub-contracted to third parties. |
| Total costs | Not separately available. |
No work sub-contracted to third parties. |
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 :
At the current stage of development of the national R&D statistical system, separate cost accounting for individual R&D statistical activities is not available.
16.2. Components of burden and description of how these estimates were reached
Please see note in Concept 3.3.1.
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | Not available. |
|
| 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.
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
R&D data are collected through national R&D statistical surveys covering institutional sectors performing research and development activities in Montenegro.
Please see note in Concept 3.3.1.
18.1.2. Sample/census survey information
Please see note in Concept 3.3.1.
| Sampling unit | Institutional reporting unit / enterprise / organisation performing R&D activities |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable. |
| Stratification variable classes | Not applicable. |
| Population size | Not available. |
| Planned sample size | Not applicable. |
| Sample selection mechanism (for sample surveys only) | Not applicable. |
| Survey frame | Administrative sources, business register, institutional records and previous R&D surveys. |
| Sample design | Not available. |
| Sample size | Not available. |
| Survey frame quality | Not available. |
| Variables the survey contributes to | R&D expenditure, R&D personnel, researchers, FTE, sources of funds and related breakdowns. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Business register, administrative records and institutional databases |
|---|---|
| Description of collected data / statistics | Classification variables, institutional information and supporting administrative data |
| Reference period, in relation to the variables the administrative source contributes to | 2023 |
| Variables the administrative source contributes to | Not applicable |
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
| Realised sample size (per stratum) | Not available. |
|---|---|
| Mode of data collection | Electronic questionnaires, administrative data sources and follow-up communication with reporting units. |
| Incentives used for increasing response | No specific incentives are used. Reporting is carried out within the framework of official statistical activities. |
| Follow-up of non-respondents | Follow-up communication is carried out through e-mail, telephone contacts. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not available. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | Not available. |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Not available. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | National R&D questionnaire and methodological instructions |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | Not available |
18.4. Data validation
Validation procedures are carried out throughout the statistical production process.
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 Concept 3.3.1.
| 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 Concept 3.3.1.
| 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
| Data compilation method - Final data | Final data are based on validated survey results, administrative sources and consistency checks. |
|---|---|
| Data compilation method - Preliminary data | Preliminary estimates may be based on available survey information, administrative data and methodological estimation procedures. |
18.5.3. Measurement issues
| Method of derivation of regional data | Regional R&D data are currently limited. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Not applicable. |
18.5.4. Weighting and estimation methods
Not applicable.
Please see note in Concept 3.3.1.
| Weight calculation method | |
|---|---|
| Data source used for deriving population totals (universe description) | |
| Variables used for weighting | |
| Calibration method and the software used | |
| Estimation |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
The national R&D statistical system in Montenegro is currently undergoing further methodological development and consolidation in line with:
- OECD Frascati Manual 2015;
- Commission Implementing Regulation (EU) No 2020/1197;
- ESS standards.
Significant efforts have been undertaken in recent years in order to:
- establish a harmonised national methodological framework;
- strengthen institutional coordination;
- improve data quality and validation procedures;
- ensure continuity of annual R&D statistical reporting;
- improve international comparability of R&D indicators.
Particular attention has been devoted to:
- retrospective compilation of R&D data for the period 2021–2023;
- strengthening survey coverage;
- improvement of metadata and quality reporting;
- harmonisation with ESS reporting requirements.
Continuous methodological improvements and further strengthening of the national R&D statistical system are ongoing.
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.
19 May 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.
The statistical unit is the enterprise.
The reporting unit is any enterprise in which at least one researcher worked on R&D activities during the reference year, either as an employee or as external personnel.
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.
Calendar year.
The current transmission refers to the reference year 2023.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
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.
