1.1. Contact organisation
National Statistics Office (NSO)
1.2. Contact organisation unit
Business Register, Research and Innovation Unit
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
National Statistics Office
Business Register, Research and Innovation Unit
Lascaris
Valletta
VLT 2000
Malta
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
22 October 2025
2.2. Metadata last posted
31 October 2023
2.3. Metadata last update
20 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.
3.3.2. Sector institutional coverage
| Business enterprise sector (BES) |
Covering including all NACE codes and employment size classes, hence no threshold. Reporting of enterprise NACE code was according to the initial population. |
|---|---|
| Hospitals and clinics | 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 | Included |
|---|---|
| External R&D personnel | Included |
| Clinical trials: compliance with the recommendations in FM §2.61. | Included |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | The sources of funds in the Frascati Manual are identified in the R&D survey |
|---|---|
| Payments to rest of the world by sector - availability | Not available |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Not covered |
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) | No |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Not applicable |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not applicable |
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 | One calendar year |
|---|---|
| Source of funds | All sources of funds are in line with the Frascati Manual and distinguished |
| Type of R&D | All 3 types of R&D are in line with the Frascati Manual and distinguished |
| Type of costs | All type of costs are in line with the Frascati Manual and distinguished |
| Economic activity of the unit | Obtained from the Statistical Business Register but also asked in the survey as a double check |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Data obtained through survey |
| Product field | Collected by NACE classification |
| Defence R&D - method for obtaining data on R&D expenditure | Not applicable to Malta |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year |
|---|---|
| Function | No difficulties encountered |
| Qualification | No difficulties encountered |
| Age | Age is asked for researchers only |
| Citizenship | Citizenship is asked for researchers only |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year |
|---|---|
| Function | No difficulties encountered |
| Qualification | No difficulties encountered |
| Age | Age is not asked in the survey |
| Citizenship | Citizenship is not asked in the survey |
3.4.2.3. FTE calculation
In the survey we ask for PT (part time) and FTE. Method used: 2 PT = 1 FTE.
3.5. Statistical unit
The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993. The statistical unit is the enterprise but the responding unit and the observation unit is still the legal unit. The census unit was the enterprise. No specific impact was observed.
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 | All known R&D performing enterprises regardless of their amount of involvement in R&D | |
| Estimation of the target population size | 203 units | |
| Size cut-off point | All employment size classes are included | |
| Size classes covered (and if different for some industries/services) | All | |
| NACE/ISIC classes covered | All |
3.6.2. Frame population – Description
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.
| Method used to define the frame population | The frame population is the Statisticsal Business Register, specifically all business enterprises active in the reference period. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Main identifier is the Community Innovation Survey (CIS) which is a census of enterprises with 10 or more employees. Other data sources include enterprises reporting R&D activities in previous R&D surveys, the tax department, Xjenza Malta (responsible for promoting and coordinating scientific research, technological innovation and science communication) and Malta Enterprise. |
| Inclusion of units that primarily do not belong to the frame population | Yes |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | Every 2 years through the CIS, and from news articles. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | 11 enterprises were newly identified and included in the 2023 target population vis a vis the 2022 target population |
| Systematic exclusion of units from the process of updating the target population | 142 enterprises were available in the 2022 target population but not included in the 2023 target population. This is due to companies which ceased operations and those that reported nil for 3 years in a row. |
| Estimation of the frame population | The size of the frame population with reference to the statistical business register was 61165. |
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 is the reference year.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on 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 | There is no specific R&D statistical legislation. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes, according to the Malta Statistics Authority Act XXIV of 2000, which is a general legislation. |
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:
The NSO requests information for the compilation of official statistics according to the articles of the MSA Act – Cap. 422 and the Data Protection Act – Cap. 586 of the Laws of Malta implementing the General Data Protection Regulations (GDPR).
Article 40 of the MSA Act stipulates the restrictions on the use of information while Article 41 stipulates the prohibition of disclosure of information. Furthermore, Section IX of the Act (Offences and Penalties) lays down the measures to be taken in case of unlawful exercise of any officer of statistics regarding confidentiality of data.
- Confidentiality commitments of survey staff: Upon employment, all NSO employees are informed of the rules and duties pertaining to confidential information and its treatment. In line with stipulations of the MSA Act, before commencing work, every employee is required to take an oath of secrecy whose text is included in the same Act.
7.2. Confidentiality - data treatment
No particular confidentiality procedure is applied. The enterprises captured in the R&D population are treated as confidential and as such data related to R&D is published since it does not lead to the identification of enterprises.
8.1. Release calendar
An advance release calendar is maintained by the NSO and published on the NSO website. The calendar projects six months of news releases.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu).
At national level this is: Release calendar - National Statistics Office
8.3. Release policy - user access
An internal policy on dissemination is in place to govern the dissemination of official statistics in an impartial, independent, and timely manner, making them available simultaneously to all users.
The NSO's primary channel for the dissemination of official statistics is the NSO website. Tailored requests for statistical information may also be submitted through the said website. The Office also makes use of social media venues as a platform to communicate with its users and to present its output. The public is free to use, copy and quote the information published provided that the NSO is quoted as the source.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
R&D data at national level is disseminated annually in July.
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 | Research & Development 2023 release |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | N | |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Statistics on Research and Development may be found on the Eurostat's database through the following link: Eurostat database
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to micro-data | Micro-data is not accessible to outside users. |
|---|---|
| Access cost policy | Not applicable. |
| Micro-data anonymisation rules | Not applicable. |
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 prepared for individual ad hoc requests | Y | Aggregate figures | |
| Other |
1) Y – Yes, N - No
10.6. Documentation on methodology
Documentation on methodology at national level can be accessed at: Metadata at NSO
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.) | Metadata is available on the NSO website: Metadata at NSO |
|---|---|
| Requests on further clarification, most problematic issues | No further clarifications are needed |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
The NSO has developed an internal Quality Management Framework (QMF) which is built on common requirements of the ESS Code of Practice (ESS CoP). A document was prepared to include a set of general quality guidelines spanning over all statistical domains. Assuring methodological soundness is an integral part of the QMF, nonetheless, the document spans also on other areas related to institutional aspects.
Every five to seven years, the NSO participates in a Peer Review exercise through which the compliance of its operations with principles of the ESS CoP is assessed by an expert team. Peer Reviews are indeed part of the European Statistical System (ESS) strategy to implement the ESS CoP. Each NSI is expected to provide information as requested by a standard self-assessment questionnaire. Following this an expert team visits the office to meet NSI representatives and main stakeholders. Peer Reviews result in a compliance report and the listing of a set of Improvement Actions which need to be followed up by the NSI.
11.2. Quality management - assessment
The coverage is across all NACE sections and employment size classes of active known R&D enterprises. The questionnaires and guidelines were sent to the target respondents by email. Questionnaires are vetted individually once received by email. Any missing information is requested via telephone or e-mail. Data is also compared with previous years to ensure consistency of results. Any queries are raised with the enterprise and, if available, with the companies' financial statements.
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 - European Level | Eurostat | To compile European Innovation Scoreboard |
| 1 - National Level | Xjenza Malta | To set out national policy |
| 1 - National Level | Malta Enterprise | To enhance aid |
| 4 - Researchers and students | Researchers and students | To substantiate their studies |
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 | A national user satisfaction survey was held in March 2025, however it was not related to R&D. |
|---|---|
| User satisfaction survey specific for R&D statistics | No national user satisfaction survey was conducted specifially for R&D |
| Short description of the feedback received | From the feedback we receive, users are satisfied |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
All mandatory data was sent to Eurostat.
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 | New OECD requirements were not included. |
| Obligatory data on R&D personnel | Not applicable |
| Optional data on R&D personnel | New OECD requirements were not included. |
| 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 - 2005 | Annual | ||||
| Type of R&D | Y - 2005 | Annual | ||||
| Type of costs | Y - 2005 | Annual | ||||
| Socioeconomic objective | Y - 2025 | Annual | ||||
| Region | Y - 2022 | Annual | ||||
| FORD | Y - 2005 | Annual | ||||
| Type of institution | Y - 2004 | Annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y - 2004 | Annual | ||||
| Function | Y - 2002 | Annual | ||||
| Qualification | Y - 2005 | Annual | ||||
| Age | Y - 2013 | Annual | Females and total researchers | |||
| Citizenship | Y - 2013 | Annual | Females and total researchers | |||
| Region | Y - 2022 | Annual | ||||
| FORD | Y - 2005 | Annual | ||||
| Type of institution | Y - 2004 | Annual | ||||
| Economic activity | Y - 2002 | Annual | ||||
| Product field | Y - 2005 | Annual | ||||
| Employment size class | Y - 2002 | Annual |
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 - 2004 | Annual | ||||
| Function | Y - 2002 | Annual | ||||
| Qualification | Y - 2005 | Annual | ||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y - 2022 | Annual | ||||
| FORD | Y - 2005 | Annual | ||||
| Type of institution | Y - 2004 | Annual | ||||
| Economic activity | Y - 2002 | Annual | ||||
| Product field | Y - 2005 | Annual | ||||
| Employment size class | Y - 2002 | Annual |
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 exp | 2004 | Every two years | |||
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 |
|---|---|---|
| Personnel | Both HC and FTE | Every year |
| Expenditure | In thousand Euro | Every 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:
- 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 | 2 | 1 | 2 | 1 | +/- |
| Total R&D personnel in FTE | Not applicable | 1 | 2 | 1 | 2 | 1 | + |
| Researchers in FTE | Not applicable | 1 | 2 | 1 | 2 | 1 | + |
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
A census was carried out.
13.2.1.2. Confidence interval for key variables by NACE
| Industry sector1) | Services sector2) | TOTAL | |
|---|---|---|---|
| R&D expenditure | Not applicable* | Not applicable* | Not applicable* |
| R&D personnel (FTE) | Not applicable* | Not applicable* | Not applicable* |
1) Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)
2) Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
* A census was carried out.
13.2.1.3. Confidence interval for key variables by Size Class
| 0-9 employees and self-employed persons (optional) | 10-49 employees and self-employed persons | 50-249 employees and self-employed persons | 250- and more employees and self-employed persons | TOTAL | |
|---|---|---|---|---|---|
| R&D expenditure | Not applicable* | Not applicable* | Not applicable* | Not applicable* | Not applicable* |
| R&D personnel (FTE) | Not applicable* | Not applicable* | Not applicable* | Not applicable* | Not applicable* |
* A census was carried out.
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: no such error.
- Measures taken to reduce their effect: not applicable.
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) | Not applicable* | Not applicable* | Not applicable* | Not applicable* | |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | Not applicable* | Not applicable* | Not applicable* | Not applicable* | |
| Misclassification rate | Not applicable* | Not applicable* | Not applicable* | Not applicable* | |
| 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) | Not applicable* | Not applicable* | Not applicable* | Not applicable* | |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | Not applicable* | Not applicable* | Not applicable* | Not applicable* | |
| Misclassification rate | Not applicable* | Not applicable* | Not applicable* | Not applicable* |
* No thresholds are applied in the R&D survey both on employment and NACE coverage. So no issues emerge if an enterprise has an update on employment or NACE code.
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: Not applicable.
- Measures taken to reduce their effect: Not applicable.
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 | 44 | 73 | 34 | 10 | 161 |
| Total number of units in the sample | 58 | 88 | 41 | 16 | 203 |
| Unit Non-response rate (un-weighted) | 0.24 | 0.17 | 0.17 | 0.38 | 0.21 |
| 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 | 53 | 108 | 161 |
| Total number of units in the sample | 66 | 137 | 203 |
| Unit Non-response rate (un-weighted) | 0.20 | 0.21 | 0.21 |
| Unit Non-response rate (weighted) | 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
Total of three reminders were sent by email, together with numerous telephone calls.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | A unit non-response survey was not performed. However, the respondents received several reminders to submit the filled in questionnaire. |
|---|---|
| Selection of the sample of non-respondents | A unit non-response survey was not performed |
| Data collection method employed | A unit non-response survey was not performed |
| Response rate of this type of survey | A unit non-response survey was not performed |
| The main reasons of non-response identified | They do not perform R&D and see the questionnaire as irrelevant for them |
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) (%) | 16.53 | 16.53 | 16.53 |
| Imputation (Y/N) | Y | Y | Y |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used | Same data as previous year was taken. | Same data as previous year was taken. | Same data as previous year was taken. |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | 19.7 |
| Total R&D personnel in FTE | 22.0 |
| Researchers in FTE | 27.2 |
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 keying. |
|---|---|
| Estimates of data entry errors | Negligible data entry errors are expected since the data is totally processed by a single experienced person from the mailing stage to the reporting stage. Also, the program does not allow data entry errors as each total needs to tally with other sections. |
| Variables for which coding was performed | NACE coding for the R&D activity area and industry served. |
| Estimates of coding errors | Data is totally processed by a single experienced person from the Statistical Business Register. |
| Editing process and method | No specific data editing made but a company by company approach is used. No editing rates are available. |
| Procedure used to correct errors | The procedure done during the vetting is that questionnaires are checked as received and if necessary, companies are contacted directly through a telephone conversation or email. Survey non-replies are estimated based on the data provided in previous R&D questionnaires as well as data available in the Statistical Business Register, where applicable. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
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: T+10 (provisional data)
- Lag (days): 300
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: T+10 (provisional data)
- Lag (days): 300
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
This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No particular issues.
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). | Any 2 part-timers are equivalent to 1 full-timer | |
| 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 | Requested the sector of their research. |
| 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 | Enteprises requesting government assistance and possibility known to be R&D active are specifically targeted. |
| 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 | All variables are collected on an annual basis. |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | All variables are collected on an annual basis. |
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 | Non-response are estimated based on CIS data and previous year data |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | No weighting |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | Not applicable |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Survey type | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | Census for R&D active enterprises |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | Not a sample |
| 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) | 2010 | Coverage increased to include all NACE codes and employment size classes | |
| Function | 2005 | For the period 2002 to 2005, Malta holds a derogation for most R&D variables, in accordance with section 2 of the annex of the Commission Regulation No 1450/2004 | |
| Qualification | 2005 | For the period 2002 to 2005, Malta holds a derogation for most R&D variables, in accordance with section 2 of the annex of the Commission Regulation No 1450/2004 | |
| R&D personnel (FTE) | |||
| Function | 2005 | For the period 2002 to 2005, Malta holds a derogation for most R&D variables, in accordance with section 2 of the annex of the Commission Regulation No 1450/2004 | |
| Qualification | 2005 | For the period 2002 to 2005, Malta holds a derogation for most R&D variables, in accordance with section 2 of the annex of the Commission Regulation No 1450/2004 | |
| R&D expenditure | 2010 | Coverage increased to include all NACE codes and employment size classes | |
| Source of funds | 2005 | Not available prior to 2005 | |
| Type of costs | 2005 | Not available prior to 2005 | |
| Type of R&D | 2005 | For the period 2002 to 2005, Malta holds a derogation for most R&D variables, in accordance with section 2 of the annex of the Commission Regulation No 1450/2004 | |
| Other | 2005, 2004, 2003 | The data source for R&D personnel (HC) and total R&D expenditure was the Structural Business Statistics (SBS) for 2003, the Community Innovation Survey (CIS) for 2004, and the R&D Survey from 2005 onwards. |
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
The same 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 main data sources for the national accounts’ R&D estimate are the R&D expenditure presented in the Frascati tables. The data are compiled in line with the Frascati Manual by the Business Register, Research and Innovation Unit and Public Finance Unit. To ensure consistency between Frascati table and national accounts concepts, the tables that have been recommended by Eurostat were 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 |
|---|---|---|---|---|---|
| R&D expenditure | Euro | CIS | The difference is zero when comparing CIS 2022 data with R&D 2022 data, for the employment size classes and NACE coverage common to both domains | ||
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) | 70936.243 | 1373 | 595 |
| Final data (delivered T+18) | 77653.869 | 1435 | 623 |
| Difference (of final data) | 6717.626 | 62 | 28 |
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) | €37,881 | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | €22,335 |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use / person / day | |
|---|---|---|
| Staff costs | Not separately available | 0 |
| Data collection costs | Not separately available | 0 |
| Other costs | Not separately available | 0 |
| Total costs | Not separately available | 0 |
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 :
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | Not availble | Not available |
| Average Time required to complete the questionnaire in hours (T)1 | Not available | Not pissible to estimate - respondents were not asked for the time taken to fill in the questionnaire |
| Average hourly cost (in national currency) of a respondent (C) | Not available | Not available |
| Total cost | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
A census of all known R&D active enterprises was carried out. All mandatory variables in the Eurostat and/or OECD common core questionnaire are collected in this survey. The survey was launched in July 2024 and finalised by June 2025.
18.1.2. Sample/census survey information
| Sampling unit | The enterprise |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | 203 |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | Identification of R&D performance through the CIS and administrative sources (Xjenza Malta, IRD and Malta Enterprise) |
| Sample design | Census only |
| Sample size | Census only |
| Survey frame quality | Not applicable |
| Variables the survey contributes to | - |
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 | Not applicable |
| Reference period, in relation to the variables the administrative source contributes to | Not applicable |
| Variables the administrative source contributes to | Not applicable |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.
18.3.1. Data collection overview
| Realised sample size (per stratum) | 203 (population) |
|---|---|
| Mode of data collection | Survey was sent via email. If no email was available, survey was sent by post. |
| Incentives used for increasing response | We let them know that their data is vital for both local and international policy making decisions. |
| Follow-up of non-respondents | 3 reminders, emails and telephone calls. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Previous year data is left constant if not available from the financial statements. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 83.4% |
| 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 file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | R&D Survey 2023.doc |
| R&D national questionnaire and explanatory notes in the national language: | |
| Other relevant documentation of national methodology in English: | R&D Survey 2023 NACE_72.doc |
| Other relevant documentation of national methodology in the national language: |
Annexes:
R&D Survey 2023
R&D Survey 2023 NACE_72
18.4. Data validation
Completed questionnaires received are vetted by BRRI Unit personnel. During vetting, the logic of the questionnaire is checked. The data entry application includes in-built validations which also cater for the logic of the questionnaire. A second round of vetting is done more attentively during the reporting through the year-to-year checks and data from the Community Innovation Survey (CIS). At this stage data is also compared with previous years for consistency and should any queries arise, the enterprise is contacted by telephone.
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) | 2.7% | % | 2.7% | % |
| 10-49 employees and self-employed persons | 6.91% | % | 6.91% | % |
| 50-249 employees and self-employed persons | 3.72% | % | 3.72% | % |
| 250-and more employees and self-employed persons | 3.2% | % | 3.2% | % |
| TOTAL | 16.53% | % | 16.53% | % |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | 7% | % | 7% | % |
| Services2) | 9.6% | % | % | % |
| TOTAL | % | % | 9.6% | % |
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 | Annual survey is carried out |
|---|---|
| Data compilation method - Preliminary data | For units that did not respond within the 10-month period, data is retained as constant |
18.5.3. Measurement issues
| Method of derivation of regional data | NUTS 0, 1 and 2 are equal to the national level so no deviation. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | For the BES sector, 2 PT are taken as 1 FT. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT 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) | Active R&D enterprises from previous rounds, the Community Innovation Survey (CIS), as it addresses a particular question to R&D; and other administrative sources for which enterprises apply to benefit from funds or tax rebates related to R&D. |
| Variables used for weighting | Not applicable |
| Calibration method and the software used | Not applicable |
| Estimation | If a unit engaged in R&D fails to report its data, we pursue it until the data is sent to us. In some instances, if a unit did not send us its data, we keep the data constant for the respective year. |
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.
20 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. The statistical unit is the enterprise but the responding unit and the observation unit is still the legal unit. The census unit was the enterprise. No specific impact was observed.
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 is the reference 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:
- 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.
R&D data at national level is disseminated annually in July.
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.
