1.1. Contact organisation
Institut National de la Statisitique et des Etudes Economiques - STATEC
1.2. Contact organisation unit
Structural Business Statistics - ENT3
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
STATEC
B.P. 10
L-4401 Belvaux
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
4 December 2025
2.2. Metadata last posted
4 December 2025
2.3. Metadata last update
4 December 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) |
Business enterprises covered by the R&D surveys are enterprises having at least 10 employees in the main sectors considered as active in R&D. To assess the need for additional survey coverage / verify the hypothesis, a question on R&D has been included in the SBS survey starting in the reference year 2010. Since the SBS sample covers around 3000 units, including a rotating sample of micro-enterprises, this question helps us to identify unknown R&D performers. This allows us to define the target population more accurately and to adjust the sample of future R&D surveys, if necessary. We also started including additional data sources to identify R&D performers (e.g. data on R&D subsidies). |
|---|---|
| Hospitals and clinics | Hospitals and clinics are not covered. |
| 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 | Personnel dedicated to administration and other supporting staff - i.e. Skilled and unskilled craftsmen, secretarial and clerical staff participating in R&D projects or directly associated with such projects" are collected under a separate heading in the questionnaire. |
|---|---|
| External R&D personnel | No information on external R&D personnel; data on the number of on-site consultants as well as related expenditures are collected under dedicated headings in the questionnaire. |
| Clinical trials: compliance with the recommendations in FM §2.61. | Clinical trials not included. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | For BES, funds from abroad are surveyed among:
|
|---|---|
| Payments to rest of the world by sector - availability | Payments to abroad are not available. |
| Intramural R&D expenditure in foreign-controlled enterprises – coverage | Not available |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Dedicated question on extramural RD. |
| Difficulties to distinguish intramural from extramural R&D expenditure | No information. Explanation added on how to distinguish between intramural and extramural R&D expenditure. |
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 | Financial year, 2003 onwards. |
|---|---|
| Source of funds | R&D data by source of funds are to be broken down, as percentages, between:
|
| Type of R&D | Intramural R&D expenditures are to be broken down as percentages of Basic Research, Applied Research and Experimental Development. |
| Type of costs | Total R&D expenditures are to be broken down, between:
|
| Economic activity of the unit | The results are broken down into the following based on the economic activity based on the NACE Rev. 2 from the national business register. |
| Economic activity of industry served (for enterprises in ISIC/NACE 72) | Not available. |
| Product field | Not available. |
| Defence R&D - method for obtaining data on R&D expenditure | There is no Defence R&D. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Financial year, 2003 onwards. |
|---|---|
| Function | Data available for Researchers, Technicians and other R&D personnel; data available since 2003. Consultants available separately starting 2012. |
| Qualification | Not available |
| Age | Not available |
| Citizenship | Not available |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Financial year, 2003 onwards. |
|---|---|
| Function | Data available for Researchers, Technicians and other R&D personnel; Consultants available separately starting 2012. |
| Qualification | Not available |
| Age | Not available |
| Citizenship | Not available |
3.4.2.3. FTE calculation
One FTE may be thought of as one person-year. A person who spends 30% of his or her time in R&D should be considered as 0.3 FTE. A full-time R&D worker employed for 6 months is a 0.5 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, if there are deviations please explain.
3.6. Statistical population
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
3.6.1. National target population
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective the target population for the national R&D survey of the Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | The target population is made of resident businesses carrying out market activities according to the statistical classification of economic activities (NACE Rev.2) on or from the Luxembourgish economic territory during the observation period; currently, is the same as the one used for the CIS survey, but of course, only the R&D performers are taken into account. To assess the need for additional survey coverage and to test this hypothesis, a question on R&D activities was introduced in the SBS survey starting with the reference year 2010. Given that the SBS sample comprises approximately 3000 units, including a rotating sample of micro-enterprises, this question helps identify previously unknown R&D performers. This approach enables us to define the target population more precisely and, where necessary, adjust the sample for future R&D surveys. We have also begun integrating additional data sources, such as information on R&D subsidies, to further improve the identification of R&D-performing units. |
|
| Estimation of the target population size | Around 2000 enterprises. | |
| Size cut-off point | Enterprises with less than 10 employees are not covered. |
|
| Size classes covered (and if different for some industries/services) | 10 - 49 50 - 249 250+ |
|
| NACE/ISIC classes covered | All NACE classes are covered in the register of enterprises with known or presumed R&D activities. For the stratified random sample drawn from the remaining enterprises, only the more R&D-intensive NACE sectors were generally included. |
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 used for the sample it is based on the most recent SBS preliminary data available at that moment but is adjusted using business register data in order to take into account the most recent figures for turnover and employment. However, the frame population for the final data production uses the same snapshot as the SBS final results for the same reference year. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Important R&D performers identified in previous surveys are included in the sample. Starting with the 2012 CIS-R&D survey, we also use administrative data to include all recipients of R&D subsidies for the given period in the sample. |
| Inclusion of units that primarily do not belong to the frame population | Not included. |
| Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D | All respondents to the CIS-R&D (even not known performers) survey are also required to answer if they performed R&D and complete the R&D part if this is the case. |
| Number of “new”1) R&D enterprises that have been identified and included in the target population | No information. |
| Systematic exclusion of units from the process of updating the target population | Yes. Public research centres are excluded from BES since they are included in GOV statistics. Enterprises with less than 10 persons employed are currently not covered. |
| Estimation of the frame population | Around 2000 enterprises. |
1) i.e. enterprises previously not known or not supposed to perform R&D
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3. (data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
Calendar year 2023.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Governed by the general national statistical legislation. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- EBS Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law:
STATEC guarantees the confidential treatment of the individual data of the enterprises, which are used exclusively for the compilation of statistics or in the carrying out of scientific studies.
Regulation (EC) No 223/2009 on European statistics (recital 24 and Article 20(4)) of 11 March 2009 (OJ L 87, p. 164), stipulates the need to establish common principles and guidelines ensuring the confidentiality of data used for the production of European statistics and the access to those confidential data with due account for technical developments and the requirements of users in a democratic society.
- Confidentiality commitments of survey staff: Yes.
7.2. Confidentiality - data treatment
Restricted from publication
8.1. Release calendar
Not available.
8.2. Release calendar access
At Eurostat level this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
Not available.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
10.1. Dissemination format - News release
Please see the sub-concepts 10.1 to 10.5 in the full metadata view.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | N | |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1) | Links |
|---|---|---|
| General publication/article | Y | The results are presented and discussed via our national publications and/or an information letter. Selected results will also appear in the annual statistical yearbook, “Luxembourg in figures” and in “Un portrait chiffré des entreprises au Luxembourg”. |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Data on Science and Technology
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 access for research purposes is governed by our national statistical law. |
|---|---|
| Access cost policy | Free |
| Micro-data anonymisation rules | No |
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 | Aggregated figures | The results are published on the Luxembourg statistics portal: Data on Science and Technology This portal is dedicated to inform the public free of charge. |
| Data prepared for individual ad hoc requests | Y | ||
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Definitions and precisions given in the R&D questionnaire. An extensive set of editing controls (to check the coherence and quality of the data, e.g. during the online coding of the data).
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
Please see the sub-concept 10.7.1 in the full metadata view.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Metadata is available on the statistics portal; links are provided in the table. Quality documentation is generally in FR and be found in ad-hoc publications or data publications. Quality reports are only available in EN: Documentation on Methodology for Users |
|---|---|
| Requests on further clarification, most problematic issues | Assistance is offered to the users. In most cases, users have direct contacts with the R&D data providers. |
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).
11.2. Quality management - assessment
A weak point in the national methodology is that some economic activities, as well as micro-enterprises, are currently not surveyed, and based on the hypothesis that R&D in these activities/size-classes is negligible.
To assess the need for additional survey coverage and to test this hypothesis, a question on R&D activities was introduced in the SBS survey starting with the reference year 2010.
Given that the SBS sample comprises approximately 3000 units, including a rotating sample of micro-enterprises, this question helps identify previously unknown R&D performers.
This approach enables us to define the target population more precisely and, where necessary, adjust the sample for future R&D surveys.
We have also begun integrating additional data sources, such as information on R&D subsidies, to further improve the identification of R&D-performing units.as well as micro-enterprises, are currently not surveyed, and based on the hypothesis that R&D in these activities/size-classes is negligible.
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 - International level | EU Commission (including EUROSTAT), OECD | |
| 1 - National level | Ministry of Higher Education and Research | |
| 1 - National level | Ministry of Economy and Foreign Trade | |
| 3 - Media | National media | |
| 4 - Researchers and students | The research department at STATEC (RED) |
1) Users' class codification
1- Institutions:
- European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
- in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
- International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes. )
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | No satisfaction survey has been carried out. |
|---|---|
| User satisfaction survey specific for R&D statistics | There is no survey led at the national level to assess the user's satisfaction on the data quality on the R&D in enterprises. |
| Short description of the feedback received | Not available. |
12.3. Completeness
Please see the sub-concept 12.3.2 in the full metadata view.
12.3.1. Data completeness - rate
All mandatory datasets were transmitted.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | not applicable |
| Obligatory data on R&D expenditure | not applicable |
| Optional data on R&D expenditure | not collected |
| Obligatory data on R&D personnel | not applicable |
| Optional data on R&D personnel | not collected |
| 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-2003 | Biennial | ||||
| Type of R&D | Y-2003 | Biennial | ||||
| Type of costs | Y-2003 | Yearly | ||||
| Socioeconomic objective | N | |||||
| Region | Not applicable | |||||
| FORD | N | |||||
| Type of institution | Available for surveys combined with CIS. | Biennial |
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-2003 | Yearly | ||||
| Function | Y-2003 | Yearly | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Not applicable | |||||
| FORD | N | |||||
| Type of institution | Available for surveys combined with CIS | Biennial | ||||
| Economic activity | Y-2003 | Yearly | ||||
| Product field | N | |||||
| Employment size class | Y-2003 | Yearly |
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-2003 | Yearly | ||||
| Function | Y-2003 | Yearly | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Not applicable | |||||
| FORD | N | |||||
| Type of institution | Available for surveys combined with CIS | Biennial | ||||
| Economic activity | Y-2003 | Yearly | ||||
| Product field | N | |||||
| Employment size class | Y-2003 | Yearly |
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 |
|---|---|---|---|---|---|
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 |
|---|---|---|
| Not available | ||
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 | 4 | 1 | 2 | 5 | 3 | +/- | |
| Total R&D personnel in FTE | 4 | 1 | 2 | 5 | 3 | +/- | |
| Researchers in FTE | 4 | 1 | 2 | 5 | 3 | +/- | |
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
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
13.2.1.1. Variance Estimation Method
Variance was calculated using a regression estimator.
13.2.1.2. Confidence interval for key variables by NACE
Restricted from publication
13.2.1.3. Confidence interval for key variables by Size Class
Restricted from publication
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: Only 0.6% of the initial gross sample selected turned out to be non-eligible enterprises that had to be dropped.
- Measures taken to reduce their effect: Multiple sources of information are used to update the register of known or assumed R&D performers. As they are the major contributors to the final R&D numbers, we feel confident in our coverage of the target population.
In addition, we evaluate our sources by detecting newcomers in each survey wave.
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 | 217 | 104 | 28 | 349 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | Not applicable | 3 | 4 | 0 | 7 |
| Misclassification rate | Not applicable | 1.3% | 3.8% | 0 | 2% |
| 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 | 1272 | 339 | 80 | 1691 |
| Number of surveyed enterprises that have changed stratum (after inspection of their characteristics) | Not applicable | 1 | 1 | 3 | 5 |
| Misclassification rate | Not applicable | 0.07% | 0.07% | 3.75% | 0.3% |
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: Given that the survey unit is normally the enterprise, it is possible that respondents sometimes respond only for parts of the enterprise as defined by the business register.
- Measures taken to reduce their effect: Respondents are informed about the scope of the enterprise as defined by the business register, i.e. a list of all the legal units to be included in their response.
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 | Not applicable | 203 | 242 | 67 | 512 |
| Total number of units in the sample | Not applicable | 214 | 249 | 68 | 531 |
| Unit Non-response rate (un-weighted) | Not applicable | 5.1% | 2.8% | 1.5% | 3.6% |
| Unit Non-response rate (weighted) | Not applicable |
13.3.3.1.2. Unit non-response rates by NACE
| Industry1) | Services2) | TOTAL | |
|---|---|---|---|
| Number of units with a response in the realised sample | 121 | 391 | 512 |
| Total number of units in the sample | 121 | 410 | 531 |
| Unit Non-response rate (un-weighted) | 0 | 4.6% | 3.6% |
| Unit Non-response rate (weighted) |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
13.3.3.1.3. Recalls/Reminders description
Generally, 3 reminders were sent out. For important units, this was a registered letter.
13.3.3.1.4. Unit non-response survey
| Conduction of a non-response survey | No. |
|---|---|
| Selection of the sample of non-respondents | Not available |
| Data collection method employed | Not available |
| Response rate of this type of survey | Not available |
| The main reasons of non-response identified | Not available |
13.3.3.2. Item non-response - rate
Definition: Un-weighted Item non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 0 | 0 | 0 |
| Imputation (Y/N) | N | N | N |
| If imputed, describe method used, mentioning which auxiliary information or stratification is used |
13.3.3.3. Magnitude of errors due to non-response
| Magnitude of error (%) due to non-response | |
|---|---|
| Total intramural R&D expenditure | Unknown |
| Total R&D personnel in FTE | Unknown |
| Researchers in FTE | Unknown |
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 | Check for coding errors, control of internal and external plausibility. |
|---|---|
| Estimates of data entry errors | Not available |
| Variables for which coding was performed | Not available |
| Estimates of coding errors | No errors. |
| Editing process and method | Manual editing and computing editing methods are used. |
| Procedure used to correct errors | Re-contact the respondents for clarifications, deductive imputation. |
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: end 2023
- Date of first release of national data: December 2024
- Lag (days): 350
14.1.2. Time lag - final result
- End of reference period: end 2023
- Date of first release of national data: November 2025
- Lag (days): 680
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
The Luxembourg R&D data collection also considers the smaller R&D firms, but not micro-enterprises (1-9 employees).
Nevertheless, the survey results indicate a concentration of R&D among very few firms: in 2003 for example, about 5% of the R&D firms conduct approximately two-third of the R&D of this sector.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | |
| Researcher | FM2015, §5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, §5.49-5.57 (in combination with Eurostat’s EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015 Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Special treatment for NACE 72 enterprises | FM2015, § 7.59. | Not classified by product group. | |
| Statistical unit | FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Identification of not known R&D performing or supposed to perform R&D enterprises | FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| NACE coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Enterprise size coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | Only enterprises >10 employees are included. | |
| Reference period for the main data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period for all data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection preparation activities | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | |
| Data collection method | FM2015 Chapter 7 (mainly sub-chapter 7.7). | Web survey (optional paper questionnaire). | |
| 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 | 3 reminders are sent by mail and as well phone calls are used. |
| Data processing methods | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Treatment of non-response | FM2015 Chapter 6 (mainly sub-chapter 6.7). | No deviation | |
| Data weighting | FM2015 Chapter 7 (mainly sub-chapter 7.7). | No deviation | Weights are calibrated. |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | Variance is estimated using a regression estimator in order to take into account calibration. | |
| 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). | Combination of census and sample. | |
| Sample design | FM2015 Chapter 6 (mainly sub-chapter 6.4). | No deviation | |
| Survey questionnaire | FM2015 Chapter 6 (mainly sub-chapter 6.4). | Multiple languages (French, English and Geman). |
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) | 2012 | In 2012, the break reflects the difficulties in measuring R&D spending in the financial sector, which was overestimated before 2012, in particular, due to enterprises that took into account internal IT development costs as R&D expenditure. | |
| Function | N/A | ||
| Qualification | N/A | ||
| R&D personnel (FTE) | In 2012, the break reflects the difficulties in measuring R&D spending in the financial sector, which was overestimated before 2012, in particular, due to enterprises that took into account internal IT development costs as R&D expenditure. | ||
| Function | N/A | ||
| Qualification | N/A | ||
| R&D expenditure | In 2012, the break reflects the difficulties in measuring R&D spending in the financial sector, which was overestimated before 2012, in particular, due to enterprises that took into account internal IT development costs as R&D expenditure. | ||
| Source of funds | N/A | ||
| Type of costs | N/A | ||
| Type of R&D | N/A | ||
| Other | N/A |
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
In the even years the data are collected using a combined R&D-CIS questionnaire. Less detailed, "Source of funds" and "type of R&D" information are not included.
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
Not available
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 |
|---|---|---|---|---|---|
| Not available | |||||
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) | 379966 | 3072 | 1118 |
| Final data (delivered T+18) | 402181 | 3004 | 1167 |
| Difference (of final data) | 22215 | -68 | 49 |
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) | 93 304€ | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 108 948€ |
(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 available | Not available |
| Data collection costs | Not available | Not available |
| Other costs | Not available | Not available |
| Total costs | Not available | Not available |
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 : No comments.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | Not applicable | |
| Average Time required to complete the questionnaire in hours (T)1 | Not applicable | |
| Average hourly cost (in national currency) of a respondent (C) | Not applicable | |
| Total cost | Not applicable |
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
| Survey name | Survey on R&D 2023 (starting with the year 2015) |
|---|---|
| Type of survey | The survey is mandatory; web survey (optional paper questionnaire). |
| Combination of sample survey and census data | Both methods are used: Large and medium enterprises (> 50 employees and > 250 employees) are always censused, while SMEs are censused only when belonging to small size strata. Additionally, enterprises that received an R&D subsidy for the reference period are also censused. |
| Combination of dedicated R&D and other survey(s) | In the even years the data are collected using a combined R&D-CIS questionnaire. |
| Sub-population A (covered by sampling) | Apart from large enterprises (> 250 employees) and medium enterprises (50+), strata are sampled if strata sizes are sufficiently large. |
| Sub-population B (covered by census) | Large enterprises and medium enterprises (> 250 employees and >50 employees) are always censused. All other strata are only censused if strata sizes are too small. |
| Variables the survey contributes to | R&D expenditure by type of cost Types of R&D R&D personnel (HC and FTE) by function. R&D personnel (FTE) by sex R&D expenditure by source of funds |
| Survey timetable-most recent implementation | Data are collected between December and April, and checked, cleaned up, attributed and weighted from May to July. |
18.1.2. Sample/census survey information
| Sampling unit | Active firms registered with the national business register (STATEC). |
|---|---|
| Stratification variables (if any - for sample surveys only) | Size (10-49, 50-249, 250+) and economic activity. |
| Stratification variable classes | NACE and size classes. |
| Population size | Target population is 2039 enterprises. |
| Planned sample size | Gross sample is 531 (ineligibles are included and are removed post survey). |
| Sample selection mechanism (for sample surveys only) | The sampling scheme used is a stratified sample based on an optimal allocation approach. The sample is broken down by the size of the enterprise (10-49, 50-249, 250 or more) and the economic activity. |
| Survey frame | The samples were constructed from STATEC’s national register of Luxembourg businesses, according to the status of firms economically active. Establishments were broken down by sector, according to their main activity and their size. |
| Sample design | The sampling scheme used is a stratified sample. The sample is broken down by the size of the enterprise (10-49, 50-249, 250 or more) and the economic activity. |
| Sample size | 528 |
| Survey frame quality | Good |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | |
|---|---|
| Description of collected data / statistics | Not appplicable |
| Reference period, in relation to the variables the administrative source contributes to | Not applicable |
| Variables the administrative source contributes to | Starting with the reference year 2015 the R&D data are collected annually. |
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) | 528 |
|---|---|
| Mode of data collection | Online questionnaire with postal invitation. |
| Incentives used for increasing response | The survey is mandatory, non-respondents receive up to 3 reminders, with the third being a registered letter for enterprises with 50 or more employees as well as recipients of R&D subsidies. |
| Follow-up of non-respondents | Follow-up emails or phone calls are made to improve response rates. These calls are made to non-respondents and to units providing partial responses. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Total non-response are treated by weighting. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | The unweighted response rate is 96% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | To treat non-response, the initial sampling weight is first adjusted using the response rate for each stratum. Strata are defined by crossing the following size classes and NACE groupings. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | RD_2023_EN_2024_FINAL.pdf |
| R&D national questionnaire and explanatory notes in the national language: | RD_2023_FR_2024_FINAL.pdf RD_2023_DE_2024_FINAL.pdf |
| Other relevant documentation of national methodology in English: | N/A |
| Other relevant documentation of national methodology in the national language: | N/A |
Annexes:
RD_2023_EN_2024_FINAL
RD_2023_FR_2024_FINAL
RD_2023_DE_2024_FINAL
18.4. Data validation
Given the complexity of measuring R&D spending, identifying real activities of respondents' R&D needs careful validation, thus avoiding underestimating or overestimate R&D work.
Manual validation is applied to all companies having declared R&D activities. In addition, companies that declared no R&D activities, but for which there is historical information (e.g. previous studies) or administrative (e.g. R&D grants) in relation to R&D are assessed.
Error detection is an integral part of data collection and processing activities. Automated checks are applied to data records during on-line collection in order to identify declaration and entry errors. These checks make it possible to detect potential errors in totals and key ratios that exceed tolerance thresholds, as well as problems with consistency of the data collected (e.g. the total of a variable is not equal to the sum of its parts).
Other checks are used during data processing to detect automatically, to detect errors or inconsistencies that remain after collection.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)
18.5.1.1. Imputation rate by Size class
| Size class | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| 0-9 employees and self-employed persons (optional) | 0% | 0% | 0% | 0% |
| 10-49 employees and self-employed persons | 0% | 0% | 0% | 0% |
| 50-249 employees and self-employed persons | 0% | 0% | 0% | 0% |
| 250-and more employees and self-employed persons | 0% | 0% | 0% | 0% |
| TOTAL | 0% | 0% | 0% | 0% |
18.5.1.2. Imputation rate by NACE
| NACE | R&D Expenditure | R&D personnel (FTE) | ||
|---|---|---|---|---|
| Unweighted | Weighted | Unweighted | Weighted | |
| Industry1) | 0% | 0% | 0% | 0% |
| Services2) | 0% | 0% | 0% | 0% |
| TOTAL | 0% | 0% | 0% | 0% |
1) Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)
2) Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)
18.5.2. Data compilation methods
| Data compilation method - Final data | Starting with 2015 reference year data are collected annually. |
|---|---|
| Data compilation method - Preliminary data | Preliminary results in year T+1 are estimated using the evolution of employment for each enterprise. |
18.5.3. Measurement issues
| Method of derivation of regional data | Not relevant |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Not applicable |
18.5.4. Weighting and estimation methods
| Weight calculation method | A first weight is established in order to adjust for the sampling design and the unit-response rate. The inverse of the sampling fraction (number of enterprises) is used for that purpose. To treat non-response, the initial sampling weight is first adjusted using the response rate for each stratum. Strata are defined by crossing the following size classes and NACE groupings. In order to obtain reliable results for quantitative variables (that are in line with SBS totals) the corrected weights are calibrated using to the number of units, the total turnover and the total employment per stratum as auxiliary information. |
|---|---|
| Data source used for deriving population totals (universe description) | In order to derive the population totals the post-strata are used. |
| Variables used for weighting | The number of enterprises and the number of employees by strata. |
| Calibration method and the software used | Calibrate function of the R-package |
| Estimation | Does not appply |
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.
4 December 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, if there are deviations please explain.
Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.
Not requested. R&D statistics cover national and regional data.
Calendar year 2023.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.
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.
