1.1. Contact organisation
Centraal Bureau voor de Statistiek (Statistics Netherlands)
1.2. Contact organisation unit
Statistiekproductie Bedrijfseconomische Statistieken
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Centraal Bureau voor de Statistiek
Postbus 4481
6401 CZ HEERLEN
The Netherlands
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
5 February 2024
2.2. Metadata last posted
5 February 2024
2.3. Metadata last update
5 February 2024
3.1. Data description
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
| not applicable | |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | As recommended by Frascati Manual. |
| Fields of Research and Development (FORD) | As recommended by Frascati Manual. |
| Socioeconomic objective (SEO by NABS) | As recommended by Frascati Manual. |
3.3.2. Sector institutional coverage
| Government sector | This sector includes Government institutes, TNO institutes, other semi-government institutes. The Dutch research council, NWO (Netherlands Organisation for Scientific Research) devoted to university research is part of the R&D figures for the Government sector and not the higher education sector (the researchers are on the payroll of NWO). The same procedure holds for the other main (semi-) government organisations devoted to (university) research: Royal Netherlands Academy of Arts and Sciences (KNAW). However, as from 2000, newly-recruited researchers on the payroll of the Netherlands Organisation for Scientific Research (NWO), previously included in the Government sector, are now included with personnel in the higher education sector. |
| Hospitals and clinics | not applicable |
| Inclusion of units that primarily do not belong to GOV | PNP is included in GOV. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | As recommended by Frascati Manual. |
| External R&D personnel | As recommended by Frascati Manual. |
| Clinical trials | not applicable |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Yes, however, national foreign governments and international organisations are not separate categories. |
| Payments to rest of the world by sector - availability | Yes, however, national foreign governments and international organisations are not separate categories. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes. |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | There is a separate question on the survey asking for the expenditure on outsourced R&D (with domestic and foreign as separate categories). |
| Difficulties to distinguish intramural from extramural R&D expenditure | not available |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar years. |
| Source of funds | As recommended by Frascati Manual. |
| Type of R&D | As recommended by Frascati Manual. |
| Type of costs | As recommended by Frascati Manual. |
| Defence R&D - method for obtaining data on R&D expenditure | not available |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar years. |
| Function | R&D personnel can be divided into researchers and other R&D staff. Technicians cannot be separated. |
| Qualification | not available |
| Age | not available |
| Citizenship | not available |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar years. |
| Function | R&D personnel can be divided into researchers and other R&D staff. Technicians cannot be separated. |
| Qualification | not available |
| Age | not available |
| Citizenship | not available |
3.4.2.3. FTE calculation
Not calculated afterwards but explicitly asked on questionaire.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| not applicable | ||
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | This sector includes Government institutes, TNO institutes (Netherlands Organisation for Applied Scientific Research), other semi-government institutes. The Dutch research council, NWO (Netherlands Organisation for Scientific Research) devoted to university research is part of the R&D figures for the Government sector and not the higher education sector (the researchers are on the payroll of NWO). The same procedure holds for the other main (semi-) government organisations devoted to (university) research: Royal Netherlands Academy of Arts and Sciences (KNAW). However, as from 2000, newly recruited researchers on the payroll of the Netherlands Organisation for Scientific Research (NWO), previously included in the Government sector, are now included with personnel in the higher education sector. | |
| Estimation of the target population size |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | The frame population is based on the national business register of the Netherlands. |
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Data survey for enterprises with 10 or more persons employed, supplemented with external data source (WBSO; tax creditfor R&D) for enterprises with less than 10 persons employed. |
| Inclusion of units that primarily do not belong to the frame population | PNP is oncluded in GOV. |
| Systematic exclusion of units from the process of updating the target population | Enterprises with less than 10 persons employed are excluded from the survey population and estimated based on Dutch WBSO (tax credit for R&D) data. |
| Estimation of the frame population |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. 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.
Thousands of Euro, number of persons/FTE.
Calendar 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 Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | Mandatory. |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | The production of R&D statistics in the Netherlands is covered by the general Dutch national statistical legislation. |
| Legal acts | Wet op het Centraal Bureau voor de Statistiek (Statistics Netherlands Act). |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Yes. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Yes, and for enterprises that are sampled it is obligatory to provide data. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Yes. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Yes, but restricted to a limited number of research institutes and under certain restrictions (e.g. anonymisation of data). |
| Planned changes of legislation | not applicable |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development.
- European Business Statistics Methodological Manual on R&D Statistics.
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
- Confidentiality protection required by law:
Information about individual firms can never be disclosed.
- Confidentiality commitments of survey staff:
Confidentiality agreements regarding the data we work with are signed by all employees of Statistics Netherlands.
7.2. Confidentiality - data treatment
Our general rules of confidentiality apply to all data and are implemented by using TauArgus software (p%- rule with P = 15% and N = 2).
8.1. Release calendar
Data is published according to the deadlines set by Eurostat: preliminary data in October of year T+1 and definitive data in June of year T+2.
8.2. Release calendar access
not available
8.3. Release policy - user access
Statistics are published on the website of Statistics Netherlands and a select group of users can access the corresponding microdata through remote access facilities.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | No. | |
| Ad-hoc releases | No. |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
No. | |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
No. |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Tables are available in the online database of Statistics Netherlands: StatLine - Datasets by themes (cbs.nl) - R&D, Innovation and Patents.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | Microdata is available for research purposes only and under restrictions. External researchers have access to our microdata on-site or through remote access facilities. They cannot export any microdata from that environment, only aggregated data and statistical/analytical output. Information that outside users publish from our microdata is checked by Statistics Netherlands experts first in order to get permission to export the results. |
| 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 | Yes. | Aggregate figures. | |
| Data prepared for individual ad hoc requests | Yes. | Aggregate figures. | |
| Other | No. | not available |
1) Y – Yes, N - No
10.6. Documentation on methodology
A description of the statistical process is avalable on the Statistics Netherlands website (only in Dutch): Research & Development (cbs.nl).
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | - Table explanations in the online database. - Metadata documentation for researchers using the microdata. |
| Request on further clarification, most problematic issues | not applicable |
| Measure to increase clarity | not available |
| Impression of users on the clarity of the accompanying information to the data | The accompanying information is sufficiently clear to users. |
11.1. Quality assurance
Statistics Nethertlands is an ISO-certified insitution and the R&D statistics are produced under the same standards.
11.2. Quality management - assessment
We consider our R&D methodology to be of sufficient quality.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| Institutions. | Ministries and local governments. | Indicators for setting and assessing policy goals. |
| Media. | Newspapers, television programmes, websites and other media outlets. | Output based on relevant and up-to-date data. |
| Researchers and students. | Universities/higher education insitutions. | Primarily microdata for analyses/research. |
| Enterprises or businesses. | Enterprises/enterprise groups. | Indicators for market assessment. |
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 | not available |
| User satisfaction survey specific for R&D statistics | not available |
| Short description of the feedback received | not available |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
All required, obligatory data cells are provided.
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 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | X | |||||
| Obligatory data on R&D expenditure | X | |||||
| Optional data on R&D expenditure | X | Not enough funds/capacity within Statistics Netherlands for producing and delivering optional data. | ||||
| Obligatory data on R&D personnel | X | |||||
| Optional data on R&D personnel | X | Not enough funds/capacity within Statistics Netherlands for producing and delivering optional data. | ||||
| Regional data on R&D expenditure and R&D personnel | X |
Criteria:
A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.
B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Yes. | Annual. | ||||
| Type of R&D | Yes. | Annual. | ||||
| Type of costs | Yes. | Annual. | ||||
| Socioeconomic objective | No. | |||||
| Region | Yes. | Annual. | ||||
| FORD | Yes. | Annual. | ||||
| Type of institution | No. |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Yes. | Annual. | ||||
| Function | Yes. | Annual. | ||||
| Qualification | No. | |||||
| Age | No. | |||||
| Citizenship | No. | |||||
| Region | Yes. | Annual. | ||||
| FORD | Yes. | Annual. | ||||
| Type of institution | No. |
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Yes. | Annual. | ||||
| Function | Yes. | Annual. | ||||
| Qualification | No. | |||||
| Age | No. | |||||
| Citizenship | No. | |||||
| Region | Yes. | Annual. | ||||
| FORD | Yes. | Annual. | ||||
| Type of institution | No. |
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 |
| not available | |||||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
- 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 errors | 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 | 3 | 2 | - | 1 | +/- | ||
| Total R&D personnel in FTE | 3 | 2 | - | 1 | +/- | ||
| Researchers in FTE | 3 | 2 | - | 1 | +/- | ||
- Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.
- 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 |
- '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. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
- 'Good' = In the event that at least one out of the three criteria described above would not be fully met.
- 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
- 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.
- '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
not available
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | not available |
| Government | not available |
| Higher education | not available |
| Private non-profit | not available |
| Rest of the world | not available |
| Total | not available |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | not available |
| Technicians | not available | |
| other support staff | not available | |
| Qualification | ISCED 8 | not available |
| ISCED 5-7 | not available | |
| ISCED 4 and below | not available |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors: N/A.
- Measures taken to reduce their effect: N/A.
- Share of PNP (if PNP is included in GOV): N/A.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
Description/assessment of measurement errors: Difficulty to fill in the right values by respondents (e.g. distinguishing intramural from extramural R&D, making a regional division etc.)
Measures taken to reduce their effect: Via predefined rules the data are automatically subjected to a first error check. Then the data are manually checked for plausibility. In many cases contact with the respondent follows in order to verify and or clarify the data.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
| 545 | 583 | 7 |
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 variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| R&D Expenditure | 0% | |
| R&D Personnel (FTE) | 0% | |
| Researchers (FTE) | 0% |
13.3.3.3. Measures to increase response rate
Enterprises in the sample can receive up to three reminders. The first is sent via a letter, six weeks after sending the original questionnaire. The second is also sent as a letter, eight weeks after the original invitation to participate. The third reminder is done by means of a phone call.
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 | Electronic online questionnaires only. |
| Estimates of data entry errors | not available |
| Variables for which coding was performed | not available |
| Estimates of coding errors | not available |
| Editing process and method | Via predefined rules the data are automatically subjected to a first error check. Then the data are manually checked for plausibility. |
| Procedure used to correct errors | In many cases contact with the respondent follows in order to verify and or clarify the data. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data).
- End of reference period: 31 December of year T.
- Date of first release of national data: T+10 months.
- Lag (days): 0.
14.1.2. Time lag - final result
- End of reference period: 31 December of year T.
- Date of first release of national data: T+18 months.
- Lag (days): 0.
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay | not applicable | not applicable |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
PNP is included in GOV. No further divergence from Frascati Manual.
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, Frascati manual 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 paragraph 5.2). | No. | |
| Researcher | FM2015, § 5.35-5.39. | No. | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No. | |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No. | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No. | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No. | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No. | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No. | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | Yes. | PNP is included in GOV. |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No. | |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No. | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No. | |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No. | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No. |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | No. | |
| Survey questionnaire / data collection form | No. | |
| Cooperation with respondents | No. | |
| Data processing methods | No. | |
| Treatment of non-response | Yes. | No non-response survey. |
| Variance estimation | N/A. | |
| Data compilation of final and preliminary data | No. |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
| R&D personnel (HC) | 2013 - 2021 | 2013 | 2013: Revision of statistics on R&D. |
| Function | 2013 - 2021 | 2013, 2012 |
2013: Revision of statistics on R&D. 2012: As of 2012 technicians and other support staff are no separate categories anymore for BES and GOV. They are now one category ("other"). |
| Qualification | N/A. | ||
| R&D personnel (FTE) | 2013 - 2021 | 2013 | 2013: Revision of statistics on R&D. |
| Function | 2013 - 2021 | 2013, 2012 |
2013: Revision of statistics on R&D. 2012: As of 2012 technicians and other support staff are no separate categories anymore for BES and GOV. They are now one category ("other"). |
| Qualification | N/A. | 2013 | |
| R&D expenditure | 2013 - 2021 | 2013 | 2013: Revision of statistics on R&D. |
| Source of funds | 2013 - 2021 | 2013 | 2013: Revision of statistics on R&D. |
| Type of costs | 2013 - 2021 | 2013 | 2013: Revision of statistics on R&D. |
| Type of R&D | 2013 - 2021 | 2013 | 2013: Revision of statistics on R&D. |
| Other | 2013 - 2021 | 2013 | 2013: Revision of statistics on R&D. |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Data are produced in the same way in the odd and even 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.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
The microdata collected though the R&D survey is used by the department responsible for the System of National Accounts (SNA).
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
See below.
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 – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | 1084001 | 9651 | 6356 |
| Final data (delivered T+18) | 1080397 | 9567 | 6205 |
| Difference (of final data) | -3604 | -104 | -151 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | € 79.149 per FTE. |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | N/A. |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | ||
| Data collection costs | ||
| Other costs | ||
| Total costs | ||
| Comments on costs | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | ||
| Average Time required to complete the questionnaire in hours (T)1 | ||
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | R&D survey. |
| Type of survey | A combination of a census for enterprises with a least 500 persons employed and sample survey for enterprises with less than 500 persons employed, but more than 10. Enterprises with less than 10 persons employed are not surveyed. |
| Combination of sample survey and census data | Enterprises employing 500 or more employees are all included (cnesus). Enterprises with 10-499 employees are covered by sampling (sample survey). Enterprises with 0-10 employees are estimated by means of a secondary source. |
| Combination of dedicated R&D and other survey(s) | Combined with CIS for even reference years. |
| Sub-population A (covered by sampling) | |
| Sub-population B (covered by census) | |
| Variables the survey contributes to | All mandatory variables. |
| Survey timetable-most recent implementation | The survey is sent out in April year t+1, data collection is aimed to be completed by September t+1, and preliminary results are published by the end of October t+1. |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Enterprise. | ||
| Stratification variables (if any - for sample surveys only) | NACE, size class. | ||
| Stratification variable classes | |||
| Population size | A minimum of 8 enterprises in each NACE and size class combination is imperative. | ||
| Planned sample size | |||
| Sample selection mechanism (for sample surveys only) | Randon sample. | ||
| Survey frame | Enterprises with 10 to 499 persons employed are sampled from the general business register. Census for 500 persons employed or more. | ||
| Sample design | Combined sample for BES and GOV. The sampling method used is a random sampling technique according to sizeclass as well as an additional relative weighting factor derived from the number of enterprises occupying each stratum. The population is divided into 606 strata (101 NACE-groups by 6 sizeclasses). Enterprises with more than 500 persons employed are always included in the sample. | ||
| Sample size | 583. | ||
| Survey frame quality |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | WBSO (Dutch R&D tax credit) |
| Description of collected data / statistics | WBSO is the legal Act promoting research and development. This Act provides fiscal facilities for companies, knowledge centres and self-employed persons who carry out R&D work. The information is used to estimate the R&D of the smallest enterprises; 0-9 persons employed. |
| Reference period, in relation to the variables the survey contributes to | Year T. |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Micro. |
| Description of collected information | |
| Data collection method | Electronic questionnaire. |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Electronic questionnaire. |
| Incentives used for increasing response | No. |
| Follow-up of non-respondents | Up to 3 reminders by mail and/or phone call. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 93% |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | No. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | |
| R&D national questionnaire and explanatory notes in the national language: | |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
Validation is done in EDAMIS Acceptance.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
18%
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | Annual survey. |
| Data compilation method - Preliminary data | Annual survey. |
18.5.3. Measurement issues
| Method of derivation of regional data | In the survey, respondents are asked to provide a breakdown of R&D personnel HC by gender and province. This breakdown is used to estimate the other variables by gender and province. |
| Coefficients used for estimation of the R&D share of more general expenditure items | not available |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | not available |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | not available |
18.5.4. Weighting and estimation methods
| Description of weighting method | Weights are calculated per stratum (combination of NACE and sizeclass) as the ratio between the number of enterprises in the population and the number of enterprises that responded to the survey. |
| Description of the estimation method | not available |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No further comments.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
5 February 2024
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested.
Calendar year.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
- 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.
Thousands of Euro, number of persons/FTE.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
