Rathenau Instituut

Onderzoek en dialoog, Informatiefunctie

Rathenau Instituut

Postbus 95366

2509 CJ Den Haag

The Netherlands

Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and development (R&D) activities, and thereby provide information about the priority governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content.

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 (FM 2015, Chapter 12), which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020.

The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail (Commission Implementing Regulation (EU) 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 (europa.eu)).

Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

Distribution by socioeconomic objectives (SEO) is based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) at one digit level.

See below.

GBARD statistics are assumed to report detailed data on all the government's budget items that may support R&D activities and to measure or estimate their R&D content. For the purposes of GBARD, the Government sector comprises (a) the central (federal) government, (b) regional (state) government and (c) local (municipal) government subsectors (FM2015, Chapter 12).

Not requested.

National government departments

See below.

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.

Not requested.

Not requested. See point 5.

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.

Not requested.

a) Calendar year: 2021 (final expenditure)

See below.

Since the beginning of 2021, GBARD statistics are 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.  GBARD statistics were based until the end of 2020 on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

Not applicable

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

-          EBS Methodological Manual on R&D Statistics

Not requested.

Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.

A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.

a)       Confidentiality protection required by law: not applicable

b)       Confidentiality commitments of survey staff: not applicable

not applicable

Data are provided to Eurostat by 31 December every year: final expenditure for year t-1 and provisional expenditure for year t. As well as multi-annual forecast for the years t+1, t+2, t+3 and t+4

Publication of the data and a report is offered to the National Parliament in April every year. 

Not publicly available. 

The data are published open access on www.rathenau.nl and a mailing to inform users about the release is sent out to all known potential users of the data as well as national parliament.  

Annual dissemination and publication, in Dutch only.

Totale investeringen in wetenschap en innovatie (TWIN) 2021-2027 - Revisie | Rathenau Instituut

An English version of the report and the data file can be made accessible if requested. 

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

see below.

Not requested.

Totale investeringen in wetenschap en innovatie (TWIN) 2021-2027 - Revisie | Rathenau Instituut 

Totaaloverzicht (XLSX file)

See below.

See below.

Not requested.

1) Y – Yes, N - No 

See below.

Not requested.

See below.

Researchers working in the unit on GBARD statistics are specialised in quantitative data management and analysis, at minimum MA or MSc level.

Researchers working with the GBARD data receive training and support by the coordinator as required.

Researchers work always in couples on the GBARD project. 

We have recently introduced extra attention for the 'four eyes' principle at the departments delivering the data. 

We make use of a guideline (TWIN-Handleiding) for data collection, checks and analyses.

We do several checks on the data: historical and internal consistency, checks with new policy announcements, coalition agreements, etc.

We compare with matching government R&D expenditure based on data from R&D performers perspective, from the national statistics bureau. 

If in doubt, we contact the delivering departments to double check and provide clarification.

We send out reminders to ensure full coverage and response rates from all participating departments and if needed, we do a follow up with telephone calls and additional reminders. 

During and after data processing, we do cross-checks of aggregates and sub-totals, and spotchecks to validate the data.

The data and report are checked by supervisor before publication. The report and data within are checked by two collegues and the chief scientist of the institute, as well as by three external peers from three different relevant government departments. 

Compliance is monitored by the unit supervisor (coordinator), who has over 8 years working experience on the GBARD project in the Netherlands.

Every year after publication of the data en the report, the GBARD project subject to an internal evaluation in order to improve the process and quality if and where possible. Enhanced political interest in the data and report increasinly ensure that we and the officials at the data delivering departments are aware of the importance of correctness of the data.   

See below.

1)       Users' class codification

1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.

2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.

3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.

4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)

5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)

6- Other (User class defined for national purposes, different from the previous classes.)

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

See below.

Data are complete

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

1)  Criteria: Obligatory data (provisional budget and final budget). Only 'Very Good' = 100% and 'Very Poor' <100% apply.

2)  Criteria: Optional data (final budget). 'Very Good' = 100%; 'Good' = >75%;'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Stage: P - provisional, F - final. 

2) Availability of the data: No, data are not available, Y: Yes, data are available + start year.

3) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).

Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:

1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.

2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

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

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

1) High level of coverage (At least all national or federal ministries and the ministries and agencies responsible for R&D funding at state or regional level). High rate of response (>90%) in data collection. All figures broken down by NABS.  

2) If at least one out of the three criteria described above would not be fully met.

3) In the event that the rate of response would be lower than 80% even by meeting the two remaining criteria.

4) In the event that the average rate of response would be lower than 70% and at least one of the two remaining criteria would not be met.

5) If all the three criteria described above are not met.

Not requested.

Not requested.

Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.

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

a)       Description/assessment of coverage errors:

 The total target population is included in the sample.  

 Only exception is the ministry of finance. 

b)      Measures taken to reduce their effect:

 The ministry has been approached to discuss their potential participation. The ministry does not participate as they expect the contribution of the ministry to R&D expenditure is marginal. We will contact the ministry again in the near future to discuss if the situation may have changed.  

There is no over-coverage

Not requested.

Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values.

a)       Description/assessment of measurement errors: 

 It is not always easy for the officials at the ministries to estimate the expected expenditure on R&D in programmes with mixed activitities (e.g. where R&D is not the only activity taking place within the programme, and is combined with other activities, such as innovation activities without an R&D component or government service delivery activities, etc.  The same applies to the allocation of programmes or expenditure within programmes to specific NABS codes, especially where multiple socio-economic objectives are pursued with or within a programme.  

b)      Measures taken to reduce their effect:

 Together with the GBARD questionnaire, a Guideline (Handleiding) is sent to the respondents at the ministries who are supposed to collect the data. In addition, we offer guidance and support whenever officials feel they want to ask or discuss issues related to the data collection or allocation with the coordinator, who has over 8 years of experience with GBARD data.     

Non response errors: occur 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.

a) Problems in obtaining data from targeted information providers:

 Data are sometimes delivered after our deadline, but generally, we still manage to get all the data from all the population units just in time to complete our analysis and data delivery to Eurostat in time. 

b) Measures taken to reduce their effect:

 We mail and call to follow-up our data request.

c) Effect of non-response errors on the produced statistics:

 One ministry has indicated not to participate in the data delivery as they expect their contribution to R&D to be negligible. All other ministries participate and generally do deliver the requested data.

Not requested.

Not requested.

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.

a)       Data processing and editing processes:

          Data entry by the contactpersons at the departments is done in the same format as the analysis, which limits errors. Imputation is kept to a minimum as the departments are requested to fill in the R&D expenditure for all the years (t-1, t, t+1, t+2...upto t+5) that programmes are running.

The coefficient for GUF is ideally based on the (year closest to the) final expenditure year (t-1).  This coefficient is then also applied to the preliminary expenditure in (t) and the budget year (t+1).

 This may also apply to the percentage of programmes which is expected to be spent on R&D.  

b)      Description of errors:

It may occur that the coefficient based on year (t) leads to a small over- or under-estimation in the preliminary data (t), budget data (t+1) and multi-annual forecasts.   

The R&D-expenditure in a number of programmes is based on a judgement by the departmental respondents about the expected expenditure on R&D within the programme in the final year (t-1). This perentage is then mostly also applied to the consequetive years.       

c)       Measures taken to reduce their effect:

Mostly, the change in the coefficient between two consequetive years is rather small. Over the period between 1999 and 2000, the difference in the coeffcient between two consequetive years has on average been 0,3%. By using the coefficient closest to the final realisation year, the data for the final expenditure are as close to realisation as possible.

The data for t, t+1 and later years are corrected before they become final, based on the coefficient that has been calculated for that specific year. This also applies to the percentage of the programme funding which is spent on R&D.   

Model assumption errors occur when the assumptions made for the estimation of parameters, models, the testing of statistical hypotheses, etc., are violated. As a result, the quality of the resulting statistics is affected (e.g. degrees of confidence might be inflated).

Description/assessment: 

Not applicable

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.

Date of first release of national data: T+6 (June 2023)

Date of first release of national data: T+12 (June 2023)

Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.

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

The actual data release has been punctual.  

See below.

Not requested.

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

GBARD encompass all spending allocations met from sources of government revenue foreseen within the budget, such as taxation. Spending allocations by extra-budgetary government entities are within the scope only to the extent that their funds are allocated through the budgetary process (FM2015 §12.9). The following table lists a number of key methodological issues, which may affect the international comparability of national GBARD statistics.

See below.

See below.

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

See below.

Not requested.

Not requested.

This part compares GBARD statistics from the provisional and final budget for the reference year.

The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible. 

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

1)        T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)

Data are revised only in case we are informed of substantial changes in the realised expenditure data due to changes in one of the coefficients, which affect the amount or distribution of the funding.   

Not requested.

Not requested.

a)       Provisional data: Survey amongst officials at the ministries, based on the provisional expenditure figures in the latest National budget presented to parliament.   

b)      Final data: Survey amongst officials at the ministries, based on the final expenditure figures in the latest National budget presented to parliament. 

c)       General University Funds (GUF): Survey amongst officials at the ministries, based on the final expenditure figures in the latest National budget presented to parliament combined with research coefficient. The research coefficient is annually calculated by the National Statistics Bureau (CBS), which is based on the number of research fte divided by the total academic personnel fte (most recent realisation year). 

See 12.3.3.

See below.

1) Time of data collection (T+x): T is assumed to represent the end of reference period. x expresses the number of months after (positive) or before (negative) T when data is collected.

A research coefficient is used to determine the share of the national government contribition to the universities that is spent on research. This coefficient is calculated every year for the final year based on research fte divided by total academic fte, by our National Statistics Bureau (CBS).    

Annually, the coordinator checks that 100% of the ministries that are expected to deliver the data, are mailed with the data request, together with a letter, the questionnaire format and the manual for data collection (Handleiding). The ministries are being e-mailed with reminders and if necessary this is followed up by telephone calls, to ensure data delivery as requested. We do macro edits as well as mirco edits and always compare the statistics with the previous year's data collected for all the years for which such comparative data are available. Were possible, we compare the resulting GBARD data against data from our National Statistics Bureau based on the survey of R&D-performers. We also look at external policy documents and coalition agreement documents.  Any apparent inconsistencies in the statistics are further investigated and we may request the officials in the department to look into apparent inconsistencies as well. We are also offering guidance and support in case officials reponsible for the data collection have questions or ask for our advise.  

See below.

No imputation is done

See below.

Not requested.

Not requested.

Not applicable