Statistical Office of the Republic of Slovenia (SURS).

 Demography and Social Statistics Division, Social Services Statistics Section

Litostrojska cesta 54, 1000 Ljubljana, Slovenija

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 units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The results are related to the population of all R&D performing units 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 Revision 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.

Statistics on science, technology and innovation were collected based on Commission Implementing Regulation (EU) Regulation (EU) No 1197/2020 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.

• 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).

See below.

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).

See below.

See below.

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

The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, – if there are deviations please explain.

Business entities, their subsidiaries and other divisions of business entities are defined as statistical units regarding their R&D activity. If the principal economic activity of business entity is R&D, then statistical unit is business entity as a whole. If the principal economic activity of business entity is not R&D, then statistical unit is part of business entity performing R&D activity. However, in both cases the reporting unit is business entity (enterprise).

See below.

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.

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.

¹)       i.e. enterprises previously not known or not supposed to perform R&D.

Not requested. R&D statistics cover national and regional data.

Not requested. See point 3.4.

Not requested.

Expenditure: Euro (€)

R&D personnel: number of persons (HC), full time equivalent (FTE)

Reference period is 2021.

See below.

- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development

- EBS Methodological Manual on R&D Statistics

Not requested.

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.

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 introduced to the survey. Same applies to the data delivered to Eurostat. Confidential cells are flagged with "C" (in English) or "Z" in Slovenian).

Release calendar is publicly accessible. 

Release calendar is publicly accessible. 

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 deternines the dissemination of statistical data to all users at the same time.

The data are published yearly. The data are published:

• in November - preliminary (P),
• in March - final (F).

See below.

¹) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

Data on R&D are published at a more detailed level in the SiStat database.

Not requested.

See below.

See below.

Not requested.

¹) Y – Yes, N - No 

Methodological materials on SURS’s website are available at https://www.stat.si/statweb/en/Methods/QuestionnairesMethodologicalExplanationsQualityReports.

Theme: Development and Technology, Subtheme: Research, Development and Innovation

• Quality report for the survey:
  • Raziskovalno-razvojna dejavnost pri izvajalcih (R-RD-IZV)

Theme: Development and Technology, Subtheme: Research, Development and Innovation

• Methodological explanations:
  • Research and development activity by performers (R-RD-IZV)

Not requested.

See below.

Quality management is defined as systems and frameworks in place within an organisation to manage the quality of statistical products and processes. 

Check 11.2.

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.

Relevance is the degree to which statistics meet current and potential users’ needs. It includes the production of all needed statistics and the extent to which concepts used (definitions, classifications etc.) reflect user needs. The aim is to describe the extent to which the statistics are useful to, and used by, the broadest array of users. For this purpose, statisticians need to compile information, firstly about their users (who they are, how many they are, how important is each one of them), secondly on their needs, and finally to assess how far these needs are met.

See below.

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. )

To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.

See below.

Not available.

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.

Criteria:

A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.

B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) Y-start year, N – data not available

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

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.

1)  Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.

2)  The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.

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' = In the event that at least one out of the three criteria above described would not be fully met.

3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.

4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.

5) 'Very Poor' = If all the three criteria are not met.

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.

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)

Does not apply.

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)

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.

Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.

a)       Description/assessment of coverage errors:

 The coverage errors were not detected.

Some reporting units actively engaged in the R&D activity in the previous year that fully outsourced R&D activity in the reference period were included.

b)       Measures taken to reduce their effect: /

Magnitude of error (%) = (Observed Value-True Value)/ True Value (%) 

The coverage errors were not detected. 

Not requested.

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.

Does not apply to any of this category.

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.

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.

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) 
Weighted Unit Non- Response Rate = 1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)

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)

Units that did not submit data on time are reminded twice by electronic reminder, and all key units are additionally reminded by telephone call.

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

Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.

Not requested.

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.

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 2021

b) Date of first release of national data: 5 November 2022

c) Lag (days): T +10

a) End of reference period: 31. December 2021

b) Date of first release of national data: 2 March 2023

c) Lag (days): T +14

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.

Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).

Comparability aims at measuring the impact of differences in applied statistical concepts and definitions on the comparison of statistics between geographical areas, non-geographical domains or over time. It is the extent to which differences between statistics are attributed to differences between the true values of the statistical characteristics.
The factors that may cause two statistical figures to lose comparability are attributes of the surveys that produce them. These attributes may be grouped into two major categories: (a) concepts of the survey and (b) measurement / estimation methodology.
The two following sections present lists of key attributes. Information on some of the attributes will have already been reported in previous sections of this report but they are repeated here for completeness of the lists. We provide references to the relevant earlier sections and you do not need to provide the information again.

The coherence of statistics is their adequacy to be reliably combined in different ways and for various uses. It is, however, generally easier to show cases of incoherence than to prove coherence.

When originating from a single source, statistics are coherent in that elementary concepts can be combined reliably in more complex ways. When originating from different sources, and in particular from statistical surveys of different frequencies, statistics are coherent insofar as they are based on common definitions, classifications and methodological standards. The messages that statistics convey to users will then clearly relate to each other, or at least will not contradict each other. The coherence between statistics is orientated towards the comparison of different statistics, which are generally produced in different ways and for different primary uses.

The definition of coherence: The extent to which the statistical characteristics confirm with those in other statistics such that the statistics can be expected to be used together in conjunction with, or as an alternative to.

See below.

Not requested.

National R&D statistics is produced in line with the Frascati methodology.

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.

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.

See below.

See below.

1)       Breaks years are years for which data are not fully comparable to the previous period.

Data are produced in the same way every year.

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 2020 (CIS2020) (inn_cis12) (europa.eu) also collects the R&D expenditure of enterprises that form the coverage of the CIS2020 survey. 

Not requested.

Not available.

See below.

This part compares key R&D variables as preliminary and final data.

(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. 

1)       The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.

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’)

Not requested.

Not requested.

Not requested.

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.

See 12.3.3.

See below.

Validation activities are: comparing the statistics with previous cycles, investigating inconsistencies in the statistics; performing micro data editing.

See below.

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) / (Total number of possible records for x)*100

Does not apply.

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)

Does not apply.

Not requested.

Not requested.

With the final data of R&D we implementeted new definition of enterprises. Definition is "Enterprise is legal or natural persons which had turnover or employees or investments and were therefore active during at least a part of the reference period. An enterprise may consist of several ownership-related legal persons, as long as they operate on the market as one independent enterprise". Slovenia is really small country therefore the differences were not seen so much in data. 

**2017–2019 data revision**
Due to the inconsistent allocation[1] of data on expenditure on research and development by sources of funding by some reporting units of the business sector, in March 2022 we published together with the data for the reference year 2020 the data of the unplanned revision of R&D expenditure by sources of funding for the 2017–2019 period. The total amount of funding for R&D for these years remained unchanged and thus comparable in time. The revision affected the allocation of funds by sources of funding - a decrease in the volume of financial resources for R&D by companies and an increase in the volume of financial resources from abroad. For these two types of funding sources, the data from 2017 onwards are therefore not comparable with previous years.
[1] Not adequate with the OECD Frascati manual issued in 2015