1.1. Contact organisation
Croatian Bureau of Statistics
1.2. Contact organisation unit
Structural Business Statistics, Innovations, Science, Technologies and Investments Department,
Innovation, Science and Technologies Unit
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Šubićeva 29, 10 000 Zagreb, Croatia
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
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.
The R&D definition used in the survey is fully in line with the Frascati Manual definition.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
BES sector covers all enterprises and organizations as well as research and development units within the enterprise. All NACE Sections are covered (A to U) and code of enterprise is determined according to Enterprise Head unit. The region is determined at level of enterprise. |
|---|---|
| Hospitals and clinics | Private hospitals and clinics are included. |
| Inclusion of units that primarily do not belong to BES and the borderline cases. | Not applicable. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviations from Frascati Manual recommendations regarding R&D administration and other support activities. |
|---|---|
| External R&D personnel | External R&D personnel is collected separately for the breakdowns and included in the Total. |
| Clinical trials: compliance with the recommendations in FM §2.61. | Clinical trials are in full compliance with the recommendations in Frascati Manual. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Receipts from rest of the world by sector are available. |
|---|---|
| Payments to rest of the world by sector - availability | Payments to rest of the world by sector are available. Extramural R&D expenditures can be distinguished for institutions abroad by the following categories: business enterprises, government sector, private research institutes, universities and other higher education institutions, PNP organisations and international organisations. |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Intramural R&D expenditure in foreign-controlled enterprises is covered. Foreign-controlled enterprises are covered in the R&D data collection and data can be distinguished between foreign-controled and domestic enterprises. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Data on extramural expenditure is collected in separated table in the questionnaire. |
| Difficulties to distinguish intramural from extramural R&D expenditure | There are no difficulties to distinguish intramural from extramural R&D expenditure. There is no double counting. |
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 | Calendar year 2023 |
|---|---|
| Source of funds | Source of funds is available according to Frascati Manual. |
| Type of R&D | All three types of R&D are covered according to Frascati Manual and available in more detailed breakdown. |
| Type of costs | No deviations from Frascati Manual. Costs are divided into current costs (labour costs and other current costs) and capital expenditure. All categories are available in more detailed breakdown. |
| Economic activity of the unit | Classification of enterprises in NACE Rev. 2. A unit is classified according to their main NACE activity obtained from Statistical business register. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Classification of enterprises in NACE 72 according to the economic activity of industry served is obtained by survey. |
| Product field | Product field was collected by NACE classification at 2-digits. |
| Defence R&D - method for obtaining data on R&D expenditure | Data on R&D expenditure for defence is available and covered for all sectors according to NABS. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year 2023 |
|---|---|
| Function | Data on R&D personnel in head counts by function are available and broken down by four types of function (researchers, expert associates, technicians and other supporting staff). Distinction between "researchers" and "technicians" is difficult for enterprises because they perceive researchers belong to research institutes. |
| Qualification | Data on R&D personnel in head counts by qualification are collected and available according to ISCED-2011 at levels 0-8. No difficulties were encountered. |
| Age | Data on R&D personnel in head counts by age are collected and available for researchers. There is no discrepancies with Frascati Manual methodology nor quality issues. |
| Citizenship | Data on R&D personnel in head count by citizenship are collected and available for researchers. There is no discrepancies with Frascati Manual methodology nor quality issues. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year 2023 |
|---|---|
| Function | Data on R&D personnel in full-time equivalent by function are available and broken down by four types of function (researchers, expert associates, technicians and other supporting staff). The concept of calculating full-time equivalent was sometimes difficult to understand despite the provided instructions in the questionnaire. |
| Qualification | Data on R&D personnel in full-time equivalent by qualification are collected and available according to ISCED-2011 at levels 0-8. No difficulties were encountered. |
| Age | Data on R&D personnel in full-time equivalent by age are not available. |
| Citizenship | Data on R&D personnel in full-time equivalent by citizenship are not available. |
3.4.2.3. FTE calculation
In the questionnaire we ask reporting units to fill in the FTE directly. Also we provide instructions and examples for filling in the data on FTEs as help:
- 3 persons working on R&D half-time (50%) during entire year = 3 x 0.5 = 1.5
- 2 persons working on R&D part-time (20%) during entire year = 2 x 0.2 = 0.4
- 1 person working on R&D full-time (100%) half a year = 1 x 0.5 = 0.5
- 2 persons working on R&D part-time (25%) during 8 months = 2 x (8/12) x 0.25 = 0.3
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. There are no deviations.
In BES, the statistical unit is the enterprise, but the responding unit and the observation unit is still the legal unit.
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 | Target population in BES are all R&D performing units located in the Republic of Croatia (occasional and continuous, known and unknown, in all branches and size classes). Funders of R&D only are not included in the target population. The composition of legal units included in the net sample of enterprises is 95% (the legal unit is the statistical unit). | No administrative data or pre-compiled statistics were used. |
| Estimation of the target population size | All legal units known or supposed to perform R&D. The number of units in the target population for BES was 1534 legal units | No administrative data or pre-compiled statistics were used. |
| Size cut-off point | All size classes are included. | No administrative data or pre-compiled statistics were used. |
| Size classes covered (and if different for some industries/services) | All size classes are covered. No deviations from the mandatory size-classes break-down. | No administrative data or pre-compiled statistics were used. |
| NACE/ISIC classes covered | All NACE Rev. 2 classes are covered. No deviation from the mandatory NACE break-down. | No administrative data or pre-compiled statistics were used. |
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 all legal units wich are registered in the official Statistical Business Register. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Various sources are used to indentify R&D performers:
|
| Inclusion of units that primarily do not belong to the frame population | No units that primarily do not belong to the frame population were included. |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | There were no such activities undertaken for the reference year. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | We have included 518 new units in BES target population for the reference year, out of which 119 unit reported R&D activities. |
| Systematic exclusion of units from the process of updating the target population | Units excluded from the target population were enterprises deleted from the Statistical Business Register (e.g. merged, ceased, bankrupt eneterprises) or in liquidation and enterprises reported non-R&D core business three years in a row. |
| Estimation of the frame population | Estimation of the frame population is unknown. |
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 2023.
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 | No |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: Statistical data collected in this survey, according to the Official Statistics Act and Ordinance on Access to Confidential Data of the CBS within the System of Official Statistics is confidential and its purpose is restricted exclusively to statistical usage (with exception of registered researchers under specified conditions). Authorized interviewers are obligated to respect these restrictions. The results are published in a cumulative form which prevents displaying data on individuals.
- Confidentiality commitments of survey staff: According to Code of practice of European Statistics, all employees upon employment are informed of the rules and duties pertaining to confidential information and its treatment and are obliged sign statistical confidentiality statement.
7.2. Confidentiality - data treatment
Data are published in aggregated form which does not allow identification of the reporting unit. All collected data are confidential and are used only for statistical purposes.
The following rules are used to identify sensitive cells in tabular data:
- Threshold rule: The cell is considered sensitive if the cell frequency is less than a pre-specified threshold value. In practice this means if data in certain cell in the table relates to less than a pre-specified number of reporting units, the cell is primary sensitive.
- Dominance rule: The cell is considered sensitive if the value of 1 largest contributor in the cell exceeds a pre-specified percentage of total value for that cell.
When a data cell in a table is suppressed by dropping its value based on a primary cell suppression rule, the value of that cell can still be calculated if the table provides totals. Secondary cell suppression is therefore needed to avoid such disclosures. Those values under primary and secondary protection are therefore suppressed for use.
8.1. Release calendar
Release policy and release calendar are available and publicly accessible on CBS website.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
At national level this is: Calendar of Statistical Data Issues 2023 and Publishing Programme 2023.
8.3. Release policy - user access
According to the Release Date announced in the Publishing Programme and in the Calendar of Statistical Data Issues, publications of the Croatian Bureau of Statistics are released at 11:00 a.m. precisely, thus abiding by the Principle of Timeliness of the European Statistics Code of Practice, i.e. standard daily time set for the release.
All users access the data at the precise time and no other users have access to data prior the release. Detailed breakdowns that are transmitted to Eurostat with a confidentiality flag are not disseminated nationally for confidentiality reasons.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
At national level the frequency of R&D data dissemination is also yearly, at the beginning of November as First Release and at the beginning of July as PC-Axis data base.
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.5 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y | First Release Research and Development, 2023 PC-Axis Database 2023 Statistical Information (Research and development chapter) Women and Men in Croatia (Employment and Earnings chapter) |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | Y | First Release Research and Development, 2023 PC-Axis Database 2023 Statistical Information (Research and development chapter) Women and Men in Croatia (Employment and Earnings chapter) |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
R&D database in PC-Axis is available on CBS website.
Annexes:
PC-Axis Database - Research and Development 2023
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 | Microdata are not disseminated. They can only be accessed in the secure room or via remote access. CBS prepares individual microdata databases by removing identifiers that could with large probability disclose the observed unit. More information on microdata access is available at CBS website Data for scientific purposes. |
|---|---|
| Access cost policy | Access cost policy is defined by the Ordinance on Determining the Fee Amount for the Provision of Statistical Data Processing Services and published in the Price List of Publications and Products of the Croatian Bureau of Statistics. |
| Micro-data anonymisation rules | The micro-data are anonymized by CBS according to the rules of statistical data dissemination policy. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1) | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | aggregate figures | Data on R&D are published in the First Release and at a more detailed level in the PC-Axis database. |
| Data prepared for individual ad hoc requests | Y | micro-data / aggregate figures | Research and development data not available on the CBS website can be prepared for individual ad-hoc requests that guarantee their anonymization and confidentiality. |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Methodological documents are published as a part of First Release and are available on the website of the Croatian Bureau of Statistics.
The meta-information available together with the data published in official First Release and PC-Axis Database – part “Notes on methodology” are information about Data sources, comparability and short interpretation and analysis of results.
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.) | R&D data are accompanied with Quality Report for Statistical Survey Research and Development for 2023, Notes on Methodology published separatedly and within First Release. Further explanations are given to users if requested. |
|---|---|
| Requests on further clarification, most problematic issues | Users generally have no additional questions or requests for further clarifications. |
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).
Croatian Bureau of Statistics uses the model of total quality management which comprises European Code of Practice. In order to ensure this, a quality system has been established. The CBS regularly submits quality reports according to the templates prescribed for each area of statistics by the corresponding organizational unit of Eurostat. A template was developed based on the ESMS, ESQRS and SIMS structures. In order to produce complete reports on quality, considering all quality indicators, the CBS has prepared a Manual for the calculation of quality indicators. Quality reports for individual statistical surveys are available on the website of the CBS.
The POMI quality database offers many opportunities as well as DESAP questionnaire for doing self-assessment. As already mentioned before the developed tools like POMI quality and application database in combination with the GSBPM give the opportunity for each statistical survey to be improved if necessary. An independent Internal Audit unit conducts internal audits in the CBS, gives professional opinion and has an advisory role for improving CBS business operation, estimate systems, processes and the internal controlling system based on the risk management, carries out internal audits in accordance with the best professional practice and internal audit standards in line with the International standards on internal auditing and the Ethics Code of the Internal Auditors.
Quality Assurance Framework of The European Statistical System
Quality Report for Statistical Survey Research and Development for 2023
11.2. Quality management - assessment
In the period between data collection and the beginning of the statistical analysis based on the obtained statistics, data have to be processed in a certain way. The data collection instrument is an electronic questionnaire in Excel with embedded controls and notes on methodology. Additional controls have been introduced with regard to the collection of primary data, which, along with repeated contacting of reporting units, had the effect of reducing the non-response rate for certain items. The switch to electronic data collection improved data processing, data editing and tabulation. A part of data verification procedures is built in the Excel questionnaire, while other rules are defined in the expert unit, which also corrects existing errors found on the material and, if necessary, contacts reporting units to get complete and accurate data. It is not possible to set too many data verification procedures into the form itself; instead, after obtaining all reports from the field, data are entered into the Survey Processor in which they are then processed based on the prepared project request.
Timeliness of final results is T + 10 months. Data were released 15 days after the planned deadline according to the Calendar of Statistical Data Issues 2024.
For the reference year 2023 we improved the survey coverage which enabled us to identify unknown R&D performing units. The sources we have used are explained in section 2.1. Data description. The analysis of the mentioned sources resulted in adding 518 units to the basic list of BES reporting units, out of which 119 units reported R&D activities. Total BES population was 1534 units.
The data are not comparable to the data from previous years due to the fact that the 2023 survey was for the first time carried out on the statistical unit “enterprise”. Until 2023, the survey was conducted on legal units.
12.1. Relevance - User Needs
Please see the sub-concept 12.1.1 in the full metadata view.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 - Institutions at European level | Eurostat, European Commission | Data analysis, publishing, international comparisons |
| 1 - Institutions at the national level | Ministry of Science and Education, Ministry of Economy, other ministries, Croatian Bureau of Statistics (other units) | Data analysis, sectoral comparisons, policy documents, strategies and reports, progress evaluation, analysis for national accounts, gross investment, IFATS, UOE and ETER statistics |
| 1 - International organisations | OECD | Data analysis |
| 3 - Media | Media | Data publishing and analysis |
| 4 - Researchers and students | Researchers and students | Analysis, ad hoc data requests |
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 | User satisfaction survey was conducted in 2015 and 2022. |
|---|---|
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | The survey was not R&D statistics only. Therefore there is no feedback received. |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
The survey covers all mandatory and optional variables laid down in Commission Regulation (EC) No 995/2012 of 26 October 2012 implementing Decision No 1608/2003/EC of the European Parliament and of the Council concerning the production and development of Community statistics on science and technology. All mandatory and voluntary variables were collected. All statistics produced on R&D are available.
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 | not applicable |
| Obligatory data on R&D expenditure | not applicable |
| Optional data on R&D expenditure | not applicable |
| Obligatory data on R&D personnel | not applicable |
| Optional data on R&D personnel | not applicable |
| Regional data on R&D expenditure and R&D personnel | not applicable |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y - 1997 | annual | 0 | |||
| Type of R&D | Y - 1997 | annual | 0 | |||
| Type of costs | Y - 1997 | annual | 0 | |||
| Socioeconomic objective | Y - 1997 | annual | 0 | |||
| Region | Y - 2008 | annual | 0 | |||
| FORD | Y - 1997 | annual | 0 | |||
| Type of institution | Y - 2016 | annual | 0 |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y - 1997 | annual | 0 | |||
| Function | Y - 1997 | annual | 0 | |||
| Qualification | Y - 1997 | annual | 0 | |||
| Age | Y - 1997 (researchers only) | annual | 0 | |||
| Citizenship | Y - 2005 (researchers only) | annual | 0 | |||
| Region | Y - 2008 | annual | 0 | |||
| FORD | Y - 1997 | annual | 0 | |||
| Type of institution | Y - 2016 | annual | 0 | |||
| Economic activity | Y - 1997 | annual | 0 | |||
| Product field | Y - 2016 | annual | 0 | |||
| Employment size class | Y - 2005 | annual | 0 |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y - 1997 | annual | 0 | |||
| Function | Y - 1997 | annual | 0 | |||
| Qualification | Y - 2005 | annual | 0 | |||
| Age | N | annual | 0 | |||
| Citizenship | N | annual | 0 | |||
| Region | Y - 2008 | annual | 0 | |||
| FORD | Y - 1997 | annual | 0 | |||
| Type of institution | Y - 2016 | annual | 0 | |||
| Economic activity | Y - 1997 | annual | 0 | |||
| Product field | Y - 2016 | annual | 0 | |||
| Employment size class | Y - 2005 | annual | 0 |
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 | Y - 2016 | annual | domestic and international | Data is collected for categories: business enterprises, government sector, private research institutes, universities and other higher education institutions, PNP organizations and international organizations. | |
| NACE | Y - 2005 | annual | The activities of the NKD 2007. classification have been classified into specially formed groups in such a manner that some sections, groups or classes are grouped together. | ||
| Planned R&D expenditure | Y - 2005 | annual | Total planned expenditure only. | ||
| Planned number of researchers | Y - 2005 | annual | Total number of planned researchers only. |
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 |
|---|---|---|
| Yes | both HC and 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:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| 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 | 1 | 3 | 5 | 4 | : | +/- |
| Total R&D personnel in FTE | Not applicable | 1 | 3 | 5 | 4 | : | +/- |
| Researchers in FTE | Not applicable | 1 | 3 | 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 | Non applicable | Non applicable | Non applicable |
| R&D personnel (FTE) | Non applicable | Non applicable | Non 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 | Non applicable | Non applicable | Non applicable | Non applicable | Non applicable |
| R&D personnel (FTE) | Non applicable | Non applicable | Non applicable | Non applicable | Non 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.
- Description/assessment of coverage errors: Data error checks are focused on consistency within tables and in comparison with previous years. In the event that a large difference in data is identified, we contact the reporting unit by phone or email to correct the data.The main reasons that cause measurement errors are: the person completing the questionnaire has changed, the person completing the questionnaire does not read the instructions for completing the questionnaire, lack of interest in completing the questionnaire.
- Measures taken to reduce their effect: When an error is detected, we first contact the reporting unit. In order to reduce the number of errors, we regularly try to educate reporting units on how to fill out the questionnaire. As well, we provide methodological support and technical assistance in filling out the questionnaire.
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.
| 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) | 18 | 56 | 66 | 51 | 191 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 0 | 0 | 0 | 0 | 0 |
| Misclassification rate | 0% | 0% | 0% | 0% | 0% |
| 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) | 118 | 78 | 63 | 19 | 278 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | 0 | 0 | 0 | 0 | 0 |
| Misclassification rate | 0% | 0% | 0% | 0% | 0% |
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
- Description/assessment of measurement errors:
One of the most important impacts was the change of currency in the reporting year 2023. Not only it was the first year the currency changed but the expenditure unit as well. In previous years R&D expenditure was expressed as thousands of kuna while in 2023 it was in euro. If the expenditure values differ from the previous years or the figures were to small, one or more contacts with the reporting units were made. Another reason was the persons filling in the questionnaire didn't read the instructions below the table and continue to report expenditure as in previous years.
Data error checks are focused on consistency within tables and in comparison with previous years. In the event that a large difference in data is identified, we contact the reporting unit by phone or email to correct the data. The main reasons that cause measurement errors are: the person completing the questionnaire has changed, the person completing the questionnaire does not read the instructions for completing the questionnaire, lack of interest in completing the questionnaire.
- Measures taken to reduce their effect: The reporting units are always contacted to correct the error, either via e-mail or phone call. Only after several unsuccessful attempts to correct the error, the data is corrected by the responsible persons at CBS. Every year we try to enhance the instructions visually and contextually to help reporting units fill in the questionnaire and to reduce the number of errors. On top of everything, we are constantly available for any methodological or technical support.
When an error is detected, we first contact the reporting unit. In order to reduce the number of errors, we regularly try to educate reporting units on how to fill out the questionnaire. As well, we provide methodological support and technical assistance in filling out the questionnaire. Measurement errors are reduced to minimum due to qualified and experienced personnel responsible for the survey. Each questionnaire is thoroughly checked and reviewed. Intensive follow-up activities are carried out and in case of illogical, inconsistent, unclear or missing data respondents are contacted to either correct or confirm the data. Data are also compared to the respondents data from previous years. Reporting units are provided with methodological instructions and classifications needed to fill in the questionnaire. However, large amount of time is spent in contact with the reporting units providing additional methodological support in order to reduce the number of errors.
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
| 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 | 424 | 269 | 191 | 117 | 1001 |
| Total number of units in the sample | 553 | 299 | 210 | 128 | 1190 |
| Unit Non-response rate (un-weighted) | 23,3% | 10,0% | 9,0% | 8,6% | 15,9% |
| Unit Non-response rate (weighted) | - | - | - | - | - |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 340 | 661 | 1001 |
| Total number of units in the sample | 397 | 793 | 1190 |
| Unit Non-response rate (un-weighted) | 14,4% | 16,6% | 15,9% |
| Unit Non-response rate (weighted) | Not applicable | Not applicable | Not applicable |
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
One postal reminder is sent to enterprises about 4 weeks after the start of the data collection. After that, non-respondents are contacted by telephone, sometimes several calls are needed in order to receive the questionnaire or a note that a certain enterprise did not perform R&D in the reference period.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | Non-response survey was not conducted. |
|---|---|
| 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
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 0% | 0% | 0% |
| Imputation (Y/N) | N | N | 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
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | Not available. |
| Total R&D personnel in FTE | Not available. |
| Researchers in FTE | Not available. |
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 were collected by electronic questionnaire in Excel. After the data collection and control of each questionnaire, Excel files were imported as txt files in Survey Processor Application for further computer logical and mathematical control. |
|---|---|
| Estimates of data entry errors | Not known. |
| Variables for which coding was performed | Coding was not performed because the Excel questionnaire contained drop-down lists for NABS, industrial orientation and fields of science. |
| Estimates of coding errors | None. |
| Editing process and method | Since 2016, for the data collection we use electronic questionnaires in Excel. The questionnaire is designed in a way that respondents only have to enter data for specific categories and the built-in formulas calculate totals and subtotals. The questionnaire also has built-in logical control of the major categories in different tables (for example, the number of researchers has to be the same in all tables). The number of errors in the questionnaires was drastically reduced thanks to these built-in warnings and controls, which caused a reduction in errors in the computer editing phase. |
| Procedure used to correct errors | Procedure used to correct errors or missing values was re-contact with information provider. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023
- Date of first release of national data: 15 November 2024
- Lag (days): 320
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 15 November 2024
- Lag (days): 320
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) | 11 | 18 |
| Delay (days) | 15 | 0 |
| Reasoning for delay | late responses of large enterprises with considerable amounts of R&D expenditure | no delay |
15.1. Comparability - geographical
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
R&D statistics is fully conducted and produced according to the Frascati methodology.
The same statistical concepts applied in the entire national territory.
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 | No deviation | |
| 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). | No deviation | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | |
| 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
| Length of comparable time series | Break years1 | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| Function | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| Qualification | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| R&D personnel (FTE) | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| Function | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| Qualification | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| R&D expenditure | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| Source of funds | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| Type of costs | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| Type of R&D | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
| Other | 7 | 2016 | Improvement of a number of statistical production processes and methodology revision according to Frascati Manual 2015. |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Data in the even years are produced in the same way as in the odd years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).
The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
The changes in the new European system of national accounts ESA 2010 resulted for R&D to be classified as fixed assets, meaning the cost of producing of R&D or acquisition cost of R&D are recognized as gross fixed capital formation. Prior to these changes, expenses related to R&D were classified as intermediate consumption, but recognition of R&D contributing to productivity lead to the conclusions that such costs should be recorded as fixed assets.
The main data sources for R&D estimates in Croatia are annual reports collected from legal entities dealing with R&D in the reference year (forms IR-1, form IR-2 and form IR-3). These reports have been carried out on the basis of the international methodology – the Frascati Manual of OECD. Beside the statistical reports on R&D, the main administrative data for estimating GDP (Annual financial reports of entrepreneurs) have also been used. For the data on imports of R&D, the balance of payments has been used.
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 |
|---|---|---|---|---|---|
| Intramural R&D expenditure 2023 | 1 081 918 (in 1000 of euros) | 2 165 348 (in 1000 of euros) | CIS 2022 | -1 083 430 (in 1000 of euros) |
Difference is due to divergence in reference year and type and methodology of surveys (R&D is census survey and CIS is sample survey). |
| Extramural R&D expenditure 2023 | 4 303 (in 1000 of euros) | 259 791 (in 1000 of euros) | CIS 2022 | -255 488 (in 1000 of euros) | Difference is due to divergence in reference year and type and methodology of surveys (R&D is census survey and CIS is sample survey). |
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.
| 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) | 591 412 | 7200,9 | 2464,0 |
| Final data (delivered T+18) | 591 442 | 7200,9 | 2464,0 |
| Difference (of final data) | 0 | 0 | 0 |
Comments :
....
15.4.2. Consistency between R&D personnel and expenditure
| 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) | 31 596 euro | There is no consistency issues. |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 12 471 euro | There is no consistency issues. |
(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 | 66 180 euro | |
| Data collection costs | ||
| Other costs | 13 117 euro | |
| Total costs | 79 297 euro |
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 :
It is not possible to show costs broken down by sectors (BES, GOV, PNP and HES). The presented cost summary is for the overall R&D research.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 1001 | Number of reporting units as number of all persons involved in filling in the questionnaire is impossible to collect. |
| Average Time required to complete the questionnaire in hours (T)1 | 3,6 hours | Information is collected from a question at the end of a questionnaire. Average number is calculated from reporting units reporting the time needed to complete the questionnaire (time spent assembling information prior to completing the questionnaire was not included as well as the time taken up by subsequent contacts after submitting the questionnaire). |
| Average hourly cost (in national currency) of a respondent (C) | 9,00 | Average monthly gross earnings per hour in euro according to CBS Labour market survey. |
| Total cost | 32 432,4 |
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
The Research and Development survey is an annual census survey. Data are the result of statistical processing of annual reports collected from legal units dealing with R&D in the Republic of Croatia in the 2023 calendar year. The units from the business enterprise sector filled out the Annual Report on R&D for Enterprises (IR-1 form). The forms were sent to the reporting units via electronic mail, along with general instructions and attachments required to fill out the forms. The reporting units returned the completed forms in the same way.
18.1.2. Sample/census survey information
| Sampling unit | census survey |
|---|---|
| Stratification variables (if any - for sample surveys only) | census survey |
| Stratification variable classes | census survey |
| Population size | census survey |
| Planned sample size | census survey |
| Sample selection mechanism (for sample surveys only) | census survey |
| Survey frame | census survey |
| Sample design | census survey |
| Sample size | census survey |
| Survey frame quality | census survey |
| Variables the survey contributes to | census survey |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | No such data collection is carried out. |
|---|---|
| Description of collected data / statistics | |
| Reference period, in relation to the variables the administrative source contributes to | |
| Variables the administrative source contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
| Realised sample size (per stratum) | census survey |
|---|---|
| Mode of data collection | Mode of data collection was Excel questionnaire sent to responding units via e-mail. |
| Incentives used for increasing response | Units that did not respond by the given date were contacted several times by phone, e-mail and postal reminder. In spite of all reminders, 229 legal units did not respond at all. |
| Follow-up of non-respondents | One postal reminder is sent to non-respondents, and after that they were contacted by telephone and e-mail. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Non-respondents are not replaced by proxy. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 84,1% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Not applicable. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | R&D national questionnaire and explanatory notes are not available in English. |
| R&D national questionnaire and explanatory notes in the national language: | IR-1-2023 questionnaire (only in Croatian language). |
| Other relevant documentation of national methodology in English: | Other relevant documentation of national methodology are not available in English. |
| Other relevant documentation of national methodology in the national language: | Other relevant documentation of national methodology in Croatian are not available. |
18.4. Data validation
Source data are checked by means of visual control (checking if all requested data are filled in, checking for logical or numerical inconsistencies - within a single table and several tables of the questionnaire). In case of incomplete, illogical or incorrect answers, we contact the respondents. Reported data are then compared with previous cycles, and in case of larger discrepancies we contact the respondents in order to verify the reported data. We compare data on employed personnel and R&D expenditures with data in the statistical business register. Even though Excel questionnaires have built-in controls to decrease data entry errors, each report is additionally checked in Survey Processor application. Aggregated data are checked again for inconsistencies and outliers.
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
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | 0% | 0% | 0% | 0% |
| 10-49 employees and self-employed persons | 0% | 0% | 0% | 0% |
| 50-249 employees and self-employed persons | 0% | 0% | 0% | 0% |
| 250-and more employees and self-employed persons | 0% | 0% | 0% | 0% |
| TOTAL | 0% | 0% | 0% | 0% |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | 0% | 0% | 0% | 0% |
| Services2) | 0% | 0% | 0% | 0% |
| TOTAL | 0% | 0% | 0% | 0% |
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 | The survey is annual. |
|---|---|
| Data compilation method - Preliminary data | The survey is annual. |
18.5.3. Measurement issues
| Method of derivation of regional data | R&D performers are classified to the statistical region according to the headquarters of the enterprise/unit. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Coefficients are not used. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT and depreciation are excluded from R&D expenditure. |
18.5.4. Weighting and estimation methods
| Weight calculation method | Not applicable |
|---|---|
| Data source used for deriving population totals (universe description) | Not applicable |
| Variables used for weighting | Not applicable |
| Calibration method and the software used | Not applicable |
| Estimation | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
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.
31 October 2025
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. There are no deviations.
In BES, the statistical unit is the enterprise, but the responding unit and the observation unit is still the legal unit.
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 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:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
At national level the frequency of R&D data dissemination is also yearly, at the beginning of November as First Release and at the beginning of July as PC-Axis data base.
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.
