1.1. Contact organisation
Statistical Office of the Republic of Slovenia (SURS)
1.2. Contact organisation unit
Social Services Statistics Section
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Litostrojska cesta 54, 1000 Ljubljana, Slovenia
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
9 November 2023
2.2. Metadata last posted
9 November 2023
2.3. Metadata last update
9 November 2023
3.1. Data description
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- The distribution of principal economic activity and by product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
| N/A | |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | FM`s definition is used. |
| Fields of Research and Development (FORD) | FORD classification refers to six fields of research and development: Natural sciences, Engineering and Technology, Medical and Health Sciences, Agricultural Sciences, Social Sciences and Humanities. NSE and SSH are available separately. |
| Socioeconomic objective (SEO) | From reference year 2017 onwards socio-economic objectives are no longer monitored due to high reporting burden and lack of national interest. |
3.3.2. Sector institutional coverage
| Private non-profit sector | PNP is included separately, according to FM 2015 recommendations. |
| Inclusion of units that primarily do not belong to GOV | In PNP sector are not included units that primery not belong to it. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Management and administration support:
Library:
Computing departments:
|
| External R&D personnel | The coverage of external R&D personnel is in line with FM recommendations. |
| Clinical trials | Clinical trials phases 1, 2 and 3 are treated as R&D activities, while phase 4 of clinical trials is not. Phase 4 of clinical trials is not included in R&D, except in cases that contribute to further scientific of technological advances. However, in the methodological guidelines for reporting units there are no special instructions about it. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Receipts and payments from abroad (i.e. Rest of the World) by sector are available. |
| Payments to rest of the world by sector - availability | Payments to abroad by sector are available. Extramural R&D expenditures are inquired about and can be distinguished for institutions abroad by the following categories: to foreign affiliates, to other enterprises, to foreign universities and research institutions of HES, to other PNP research organisations, to international organisations and to others. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Y |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | In the questionnaire is included seperate question/table on extramural R&D expenditure and additional clarifications for reporting units. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Some difficulties were detected, mainly for the borderline cases. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
| Source of funds | R&D expenditures are classified by source of funds into five sectors: business enterprise, government, higher education, private non-profit and abroad. All these categories are broken down into sub-categories. |
| Type of R&D | All three types of R&D (basic research, applied research and experimental development) are included. |
| Type of costs | In line with FM 2015 four types of costs are distinguished: labour costs, other current costs and capital expenditures (land and buildings, instruments and equipments, capitalised computer software and other intellectual property products). All these categories are broken down into sub-categories. |
| Defence R&D - method for obtaining data on R&D expenditure | It is covered for all sectors of performance. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year. |
| Function | Data on R&D personnel by occupation (group) (researcher, technicians and equivalent staff, other supporting staff) in head counts (HC) and sex are available. |
| Qualification | Data on R&D personnel by qualification (level) (doctoral or equivalent, short cycle tertiary, bachelor, master or equivalent, other level of education) in head counts (HC) and sex are available for all occupation groups. |
| Age | Data on R&D personnel by age (group) in head counts (HC) are available only for researchers. |
| Citizenship | Data on R&D personnel by citizenship (group) in head counts (HC) are available only for researchers. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
| Function | Data on R&D personnel by occupation (group) (researcher, technicians and equivalent staff, other supporting staff) in full time equivalents (FTE) and sex are available. |
| Qualification | Data on R&D personnel by qualification in full time equivalents (FTE) are not available |
| Age | Data on R&D personnel by age (group) in full time equivalents (FTE) are not available. |
| Citizenship | Data on R&D personnel by citizenship (group) in full time equivalents (FTE) are not available. |
3.4.2.3. FTE calculation
There are some general examples (formulas) for FTE calculation:
- researcher working full-time (100%) in R&D: 1x1=1 FTE
- 3 researchers working half-time (50%) in R&D: 3x0.5=1.5 FTE
- 2 researchers working whole year 20% part-time in R&D: 2x0.2=0.4
- researcher working half a year full-time (100%): 1x(6/12)x1=0.5 FTE
2 researchers working 8 months a quarter (25%) of their working time in R&D: 2x(8/12)x0.25=0.33 FTE
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Data on R&D personnel cross-classified by occupation and qualification are available annually, but only in head counts (HC). | ||
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the PNP Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | All firms known or supposed to perform R&D, i.e. potential R&D performers, are surveyed. | Does not apply |
| Estimation of the target population size | 119 |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested.
Expenditure: Euro (€)
R&D personnel: number of persons, full time equivalent
Reference period is 2021.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
| Legal acts / agreements | Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Yes, production of national R&D statistics is conducted on the basis of the National Statistics Act (OJ RS, No 45/95 and No 9/01) and on the basis of the current Annual Programme of Statistical Surveys (LPSR) (available only in Slovene). |
| Legal acts | N/A |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Yes |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Obligation to collect data and obligation of respondents to transmit R&D data to SURS. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Yes, it is aid down in the National Statistics Act (OJ RS, No 45/95 and No 9/01) and in the current Annual Programme of Statistical Surveys (LPSR). Collected data may only be used for statistical purposes. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Individual data collected by national statistics for statistical processing are strictly confidential and can be used exclusively for statistical purposes. SURS enables researchers to access data for the purpose of research in line with National Statistics Act. |
| Planned changes of legislation | Changes are not planned. |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
a) Confidentiality protection required by law:
The National Statistics Act (hereinafter the ZDSta) stipulates within fundamental principles of national statistics in Article 2 that “national statistics shall be implemented on the principles of … confidentiality …”
b) Confidentiality commitments of survey staff:
All employees are obliged to protect the content of personal and individual data and data on reporting units which they learn during their work as official secrecy. All employees sign a statement of data protection and thus confirm that they are informed about the issue. The obligation to protect the official secrecy continues after the termination of employment.
7.2. Confidentiality - data treatment
All R&D data collected are treated as confidential and are used only for statistical purposes. By following general statistical confidentiality framework used in SURS, statistics can be published only in aggregate form, i.e. in a manner that does not allow recognition of the unit to which data relate. Identifiable information can be published only exceptionally in cases where the unit gives its written consent to publish it. More information on statistical confidentiality is available at https://www.stat.si/StatWeb/en/FundamentalPrinciples/StatConf.
With data for 2021 statistical data protection was also done to the survey. Same applies to the data delivered to Eurostat. Confidential cells are flagged with "C".
8.1. Release calendar
Release calendar is publicly accessible.
8.2. Release calendar access
Release calendar is publicly accessible.
8.3. Release policy - user access
All data and metadata information are published and are accessible to users through various channels; among them is the most important website. The information on the website is available free of charge and is also available at English, for the widest possible use that is actively promoted.
The release policy determines the dissemination of statistical data to all users at the same time.
The data are published yearly. In November the preliminary R&D are published and in March the final R&D data.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Y | Link to news releases:
Link to SiStat Database: https://pxweb.stat.si/SiStat/en/Podrocja/Index/88/development-and-technology (Development and technology/ Research development and innovation/ Research and development (R&D) activity/ Expenditure on R&D or R&D personnel). |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | The data are published annually:
|
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Link to SiStat Database: https://pxweb.stat.si/SiStat/en/Podrocja/Index/88/development-and-technology
(Development and technology/ Research development and innovation/ Research and development (R&D) activity/ Expenditure on R&D),
(Development and technology/ Research development and innovation/ Research and development (R&D) activity/ R&D personnel).
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | Microdata are not disseminated. They can only be accessed in the secure room or via remote access. SURS prepares individual microdata databases by removing identifiers that could with large probability disclose the observed unit. More information on microdata access is available at https://www.stat.si/StatWeb/en/StaticPages/Index/For-Researchers |
| Access cost policy | None. |
| Micro-data anonymisation rules | Data under the General Data Protection Regulation (GDPR): Data can be considered 'anonymised' because individuals are no longer identifiable. |
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 | Y | Link to SiStat Database: https://pxweb.stat.si/SiStat/en/Podrocja/Index/88/development-and-technology (Development and technology/ Research development and innovation/ Research and development (R&D) activity/ Expenditure on R&D), (Development and technology/ Research development and innovation/ Research and development (R&D) activity/ R&D personnel). |
| Data prepared for individual ad hoc requests | Y | Y | send to them ad-hoc |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Methodological materials on SURS’s website are available at https://www.stat.si/statweb/en/Methods/QuestionnairesMethodologicalExplanationsQualityReports.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | First and Electornic Release (absolute figures, share in GDP (%), tabular and graphical presentation). Detailed statistics in tables in the SiStat database (absolute figures). |
| Request on further clarification, most problematic issues | Level (detail) of dissemination: NUTS 1 (country), NUTS 2 (cohesion region), NUTS 3 (statistical region). Sector of performance/employment, source of funds, type of financial institution, type of costs, type of research, field of research and development, size class of enterprise, main industry activity, industry orientation, occupation, sex, age group, educational attainment, citizenship. |
| Measure to increase clarity | Yes, we constantly update and improve the methodological explanations according to user's feedback. |
| Impression of users on the clarity of the accompanying information to the data | The clarity of the accompanying information to the data is good. |
11.1. Quality assurance
See 11.2.
11.2. Quality management - assessment
Overall quality of R&D statistics is good.
The coverage of reporting units is full. R&D statistics domain follow general quality management activities used in SURS (including the European Statistics Code of Practice and the Fundamental Principles of Official Statistics). Detailed methodological instructions for completing questionnaire are available. Data validation is performed in collaboration with reporting units. Discussion of results, substantive matters and key issues are points on the agenda or the subjects under discussion of the Research and Development, and Technology Statistics Advisory Committee.
However, there are still some aspects to be improved at R&D statistics. Despite the available methodological guidelines and instructions reporting units still have some problems with recognizing their R&D performance, understanding the R&D definitions, identifying an capturing the real/proper R&D content of activities and corresponding items. Most of the reporting units do not have records tailored to survey reporting, so they often make use of estimates without considering the substantive relevance between the items.
In the R&D statistics domain’s quality assurance activities are guaranteed through:
- clear and well-structured survey questionnaire with detailed methodological instructions for its completion;
- single point for communicating with business entities regarding the submission of data (i.e. Contact Center);
- good competences of Call Center staff and personnel responsible for data editing (training before the questionnaires are sent to the reporting units);
- good cooperation with the reporting units during data collection phase;
- computer control programs for input data;
- feedback from key reporting units and data users;
continuous updating and improvement of methodological instructions in the light of past experience.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1-Institutions at national level | State administration (Government, Prime minister, Ministries, Government offices), representative bodies, agencies and other public bodies, Bank of Slovenia, bodies of local communities | Detailed data on scope and key trends of Slovenian R&D performance for R&D and innovation and education policy decisions and strategy planning or for in-depth analysis, key R&D indicators |
| 1-Institutions at European level | European Commission, Council | European research policy and international benchmarking |
| 1-International organizations | OECD, UNESCO | International benchmarking |
| 2-Social actors | Employers’ associations, trade unions, lobbies | Key features of Slovenian R&D performance with international comparison for specific purposes (negotiating, budgeting) |
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 | SURS measured general user satisfaction in 2021. Respondents assessed general satisfaction with SURS with the average score of 8.3 (on a scale from 1 – disagree completely to 10 – agree completely). |
| User satisfaction survey specific for R&D statistics | It is not. |
| Short description of the feedback received | R&D statistics falls within the scope of the Statistical Advisory Committee on Research and Development Activities and Technologies. More information on the operation of the Committe is available on the following website https://www.stat.si/StatWeb/NationalStatistics/AdvCommitteesDescription/83 (in Slovene only). With data for 2021 statistical data protection was also done to the R-RD-IZV survey. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Not available.
12.3.2. Data availability
Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D):
1%
12.3.2.1. Incorporation of PNP sector in another sector
| Incorporation of PNP in another sector | No |
| Reasons for not producing separate R&D statistics for the PNP sector | |
| Share of PNP expenditure in the total expenditure of the other sector | 1 % |
| Share of PNP R&D Personnel in the respective figure of the other sector | 1 % |
12.3.2.2. Non-collection of R&D data for the PNP sector
| Reasons for not compiling R&D statistics for the PNP sector | |
| PNP R&D expenditure/ GERD*100) | |
| Share of PNP R&D Personnel in the respective figure of the total national economy |
12.3.2.3. Data availability on more detail level
| Additional dimension/variable available at national level1) | Availability2 | Frequency of data collection | Breakdown variables |
Combinations of breakdown variables | Level of detail |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
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.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors:
The coverage errors were not detected.
13.3.1.1. Over-coverage - rate
The coverage errors were not detected.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors:
Data error detection controls are focused on the consistency of the totals derived from different breakdowns. In case an important intra-annual change in figures is identified, one or more contacts with the reporting unit are made in order to obtain additional explanatory notes on data deviation or to arrange data retransmission.
The main reasons that cause measurement errors are: the questionnaire is filled in by several persons or organisational units or person that is not so informed on R&D projects, non-compliance with the methodological instructions, subjective and often unreliable and inconsistent assessment of funds as data can not be derived directly from reporting unit's records.
b) Measures taken to reduce their effect:
If some errors are detected by the person responsible at SURS for data editing, it is first determined whether an error is remedied without contacting the reporting units, or the error is unclear and requires additional explanations form the reporting units. The reporting unit is always contacted when it is not clear from the reported data whether they are correct or not. Is also applies to the reporting unit when the reported data are very different form the data reported by the same reporting units for previous years.
In order to reduce the number of errors, it is very important that we regularly get feedback from reporting units by recontacting them. It is important to "educate" persons responsible for reporting, provide them methodological support, the reporting units in order to correctly and accurately fill in the questionnaire. The number of measurement errors would be reduced by using a clear, comprehensible questionnaire and clear, short and precise methodological guidelines for completing it.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non-Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)
13.3.3.2. Item non-response - rate
Not requested.
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.
13.3.5. 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
b) Date of first release of national data: 4 November (T+10)
c) Lag (in months): 0
14.1.2. Time lag - final result
a) End of reference period: 31 December
b) Date of first release of national data: 3 March (T +14)
c) Lag (days): 0
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
National R&D statistics is produced in line with the Frascati methodology.
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 paragraphs and the EBS Methodological Manual on R&D Statistics with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
| R&D personnel | FM2015 Chapter 5 (mainly paragraph 5.2). | No | |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Intramural R&D expenditure | FM2015,Chapter 4 (mainly paragraph 4.2). | No | |
| Statistical unit | FM2015, § 10.40-10.42 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015, § 10.40-10.42 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015, § 10.2-10.8 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Reference period for the main data | Reg. 2020/1197: Annex 1, Table 18 | No | |
| Reference period for all data | Reg. 2020/1197: Annex 1, Table 18 | No |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | / | |
| Survey questionnaire / data collection form | / | |
| Cooperation with respondents | No | |
| Data processing methods | No | |
| Treatment of non-response | No | |
| Data compilation of final and preliminary data | / |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
| R&D personnel (HC) | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Function | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Qualification | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| R&D personnel (FTE) | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Function | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Qualification | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| R&D expenditure | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
||
| Source of funds | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Type of costs | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Type of R&D | 2011,2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Other | Does not apply. | Does not apply. |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Data are produced in the same way every year.
15.3. Coherence - cross domain
See below.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not available.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total PNP R&D expenditure (in 1000 of national currency) | Total PNP R&D personnel (in FTEs) | Total number of PNP researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | 9745 |
161 | 116 |
| Final data (delivered T+18) | 9255 | 157 | 114 |
| Difference (of final data) | -490 | -4 | -2 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not available. |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available. |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | ||
| Data collection costs | ||
| Other costs | ||
| Total costs | ||
| Comments on costs | ||
| Not available only for PNP sector. | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | 119 | |
| Average Time required to complete the questionnaire in hours (T)1 | 6,5 hours | |
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | Research and development activity by performers survey (R-RD-IZV). |
| Type of survey | Census survey among all private non-profit entities known or supposed to perform R&D. |
| Combination of sample survey and census data | Does not apply. |
| Combination of dedicated R&D and other survey(s) | Does not apply. |
| Sub-population A (covered by sampling) | Does not apply. |
| Sub-population B (covered by census) | Does not apply. |
| Variables the survey contributes to | All R&D mandatory variables and almost all optional variables requested by the regulation. |
| Survey timetable-most recent implementation | Questionnaire (for reference year 2021) in the Excel form available to the reporting units: April 2022. End of data collection: end of September 2022 Provisional data: November 2022 Final data: March 2023 |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Non-profit institutions providing services for households (S.15) | ||
| Stratification variables (if any - for sample surveys only) | Does not apply | ||
| Stratification variable classes | Does not apply | ||
| Population size | 97 | ||
| Planned sample size | 119 | ||
| Sample selection mechanism (for sample surveys only) | Does not apply | ||
| Survey frame |
|
||
| Sample design | |||
| Sample size | |||
| Survey frame quality | Coverage of the reference population is good. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Does not apply. |
| Description of collected data / statistics | Does not apply. |
| Reference period, in relation to the variables the survey contributes to | Does not apply. |
18.2. Frequency of data collection
Annual.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | PNP |
| Description of collected information | The R-RD-IZV survey is obligatory according to the National Statistics Act (OJ RS, No. 45/95 and 9/01) and Annual Programme of Statistical Surveys (LPSR) (only in Slovene). |
| Data collection method | Data are collected using survey questionnaire in the Excel form sent via eSTAT web application for electronic data reporting when completed. The deadline for transmitting (completed) survey questionnaire is approximately three weeks after the questionnaires are released out. If there exists an objective reason, then the deadline for submitting the data may be extended. In addition to electronic data reporting via eSTAT, the survey questionnaire can also be sent via e-mail address designed for the purpose of sending data (porocanje.surs@gov.si). However, there are also exceptions that provide a printed version of the completed survey questionnaire. |
| Time-use surveys for the calculation of R&D coefficients | No |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Excel questionnaire |
| Incentives used for increasing response | 2 x postal reminder, phone calls |
| Follow-up of non-respondents | No |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | Unit response rate was 86 % |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Non-response analysis is not done. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | Methodological explanation is available at https://www.stat.si/statweb/File/DocSysFile/9534/23-086-1-ME.pdf Quality report is available at https://www.stat.si/statweb/File/DocSysFile/12269/QR_R-RD-IZV_2021.pdf |
| R&D national questionnaire and explanatory notes in the national language: | Questionnaire (only in Slovene) is available at https://www.stat.si/statweb/File/DocSysFile/11889/R-RD-IZV_2021.pdf. Methodological explanation is available at https://www.stat.si/statweb/File/DocSysFile/9534/23-086-1-ME.pdf Quality report is available at https://www.stat.si/statweb/File/DocSysFile/12268/PK_R-RD-IZV_2021.pdf |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
Validation activities are: comparing the statistics with previous cycles, investigating inconsistencies in the statistics; performing micro data editing.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
The imputations were not made.
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | N/A |
| Data compilation method - Preliminary data | N/A |
18.5.3. Measurement issues
| Method of derivation of regional data | R&D performers are classified to the statistical and cohesion region on the basis of the address information (i.e. municipality) at which it is registered in Statistical Business Register. In cases where a unit in the entity reports data for the whole enterprise, the address of the headquarters is usually taken into account when deriving regional data. |
| Coefficients used for estimation of the R&D share of more general expenditure items | N/A |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation is excluded from R&D expenditures. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | N/A |
18.5.4. Weighting and estimation methods
| Description of weighting method | Weighting adjustment for non-response takes into account also units that do not answer the questionnaire (i.e. do not fill in it) when calculating statistics on R&D. Corresponding weights are calculated stratum by stratum given the activity and the enterprise size (considering the number of persons employed). Difference between provisional and final data arises mostly due to the additionally received data after the (formal) end of data collection. |
| Description of the estimation method | Does not apply. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
9 November 2023
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested. R&D statistics cover national and regional data.
Reference period is 2021.
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.
Expenditure: Euro (€)
R&D personnel: number of persons, full time equivalent
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
The data are published yearly. In November the preliminary R&D are published and in March the final R&D data.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
