Government budget allocations for R&D (GBARD) (gba)

1.1. Contact organisation

Ministry of Science, Innovation and Universities

1.2. Contact organisation unit

Deputy Directorate of Planning, Monitoring and Evaluation on R&D&I. General Directorate of Planning, Coordination and Knowledge Transfer

1.3. Contact name

Confidential because of GDPR

1.4. Contact person function

Confidential because of GDPR

1.5. Contact mail address

Paseo de la Castellana, 162, 28046, Madrid

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

11 December 2025

2.2. Metadata last posted

13 November 2023

2.3. Metadata last update

11 December 2025

3.1. Data description

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.

3.2. Classification system

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.

3.2.1. National classification

National nomenclature of SEO used	2007 NABS classification.
Correspondence table with NABS	Not applicable

3.2.2. NABS classification

Deviations from NABS	Not applicable
Problems in identifying / separating NABS chapters and sub chapters	When the budget allocation is not addressed to an specific SEO, estimates are done. Estimates are generally based on the distribution of grants approved from the reference year or previous year.
Ability to distribute Non-oriented research and General University Funds (GUF) by fields of R&D	GUF sub-chapters are difficult to estimate and they are generally informed by regions based on standard practices. Non oriented research is often a residual category after computing the distributions of funds by socio economic objectives and consequently may not correspond strictly to the concept of non-oriented research. Mostly based on grants from the reference year or previous year.

3.3. Coverage - sector

All economic sectors are included.

3.3.1. General coverage

Definition of R&D	R&D is based on FM definitions.
Coverage of R&D or S&T in general	GBARD statistics cover R&D. When there are blurred limits between R&D and innovation estimates are applied.
Fields of R&D (FORD) covered	Based on FM
Socioeconomic objective (SEO by NABS)	Based on NABS at one digit level

3.3.2. Definition and coverage of government

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).

 

Levels of government	Definition	Included / Not included	Comments
Central (federal) government	Ministries (and their functional units) funding R&D expenditures from State Budget in the reference year and R&D performers (Public Research Institutions) receiving direct funding from the State General Budget in the reference year.	Included	Budget allocations funded by the Recovery and Resilience Mechanism are not included.
Regional (state) government	Regional departments (and their functional units) funding R&D expenditures from Regional General Budgets in the reference year and R&D performers (Public Research Institutions) receiving direct funding from the Regional General Budgets in the reference year.	Included	Budget allocations funded by the Recovery and Resilience Mechanism are not included. Regional data is informed by the regional department responsible for R&D&I, who coordinates with the other departments with R&D expenditures at regional level
Local (municipal) government		Not included

3.4. Statistical concepts and definitions

Not requested.

3.5. Statistical unit

Central government ministerial departments with R&D allocations, Regional administrative departments with R&D allocations and public research organizations with limited budgets included in the State General Budget or the Regional General Budgets

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.

 

Definition of the national target population	State ministerial departments (23)/Funding agencies with limited budget (2)/State Public Research Organizations (5)/Regions (17)
Estimation of the target population size	Not applicable

3.7. Reference area

Not requested.

3.8. Coverage - Time

Not requested. See point 5.

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.

Million euro.

a) Calendar year: 2023. Calendar year=Fiscal year

 

b) Fiscal year: 2023

    Start month: January

    End month: December

6.1. Institutional Mandate - legal acts and other agreements

See below.

6.1.1. European legislation

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.

6.1.2. National legislation

National Statistics Plan 2021-2024

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

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:

 Ley 12/1989 de Función Estadística Pública. However, no information in this survey is confidential.

b)       Confidentiality commitments of survey staff:

 Ley 12/1989 de Función Estadística Pública.

7.2. Confidentiality - data treatment

No information in this survey is confidential.

8.1. Release calendar

Twice a year: in june and december

8.2. Release calendar access

Twice a year.

8.3. Release policy - user access

Public access.

Twice a year.

10.1. Dissemination format - News release

See below.

10.1.1. Availability of the releases

	Availability (Y/N)1	Content, format, links, ...
Regular releases	Y	Website link: Estadística GBARD (ciencia.gob.es)
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	Content, format, links, ...
General publication/article (paper, online)	Y	Online publication: Estadística GBARD (ciencia.gob.es) Tables (excel format) including initial and final R&D figures by NABS
Specific paper publication (paper, online)	N

1) Y – Yes, N - No 

10.3. Dissemination format - online database

Public report in the website of the Ministry of Science, Innovation and Universities: Estadística GBARD (ciencia.gob.es)

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	Not applicable
Access cost policy	Not applicable
Micro-data anonymisation rules	Not applicable

10.5. Dissemination format - other

See below.

10.5.1. Metadata - consultations

Not requested.

10.5.2. Availability of other dissemination means

Dissemination means	Availability (Y/N)1	Micro-data / Aggregate figures	Comments
Internet: main results available on the national statistical authority’s website	Y	Aggregate figures	Information is published in the Ministry of Science, Innovation and Universities website. Also available at eurostat webpage.
Data prepared for individual ad hoc requests	Y	Aggregate figures	There are very few ad-hoc queries, and only by internal units within the Administration.
Other	N

1) Y – Yes, N - No 

10.6. Documentation on methodology

Brief methology report is available in the website. For more detailed requirements, Frascati Manual can be consulted.

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, etc.)	Legal standards, the objectives of the statistical terms and key methodological issues, including citation of Frascati Manual recommendations.
Request on further clarification	No problems of clarification has been reported.
Measure to increase clarity	A brief methology report is published online. Furthermore, clarification and feedback can be done by mailing.
Impression of users on the clarity of the accompanying information to the data	No comments on this by users.

11.1. Quality assurance

No specific implementation of a general quality management system has been developed. However, data collection and integration process is done through SICTI (Information System on STI), where many validations are implemented. 

11.2. Quality management - assessment

Coordination with the reporting units at central and regional level is requiered, so permanent contact with them is maintained during data collection process.

Furthermore, decisions on methodology changes are communicated within the framework of SICTI (Information System on STI) Working Group, where all the reporting units are represented. For example, the metodology for estimates of GUF was revised by SICTI_WG in 2021. GUF estimates are calculated at university level, applying the coefficients obtained from the disaggregated data of R&D expenditure reported to HERD to the current transfer (general funds) that each university receives annually from the regional government buddgets.

GUF breakdown by socio economic objectives and subobjectives represents the distribution by major scientific fields in universities and it is reported at regional level.

NABS distribution is not the outcome of policy priorities as reflected in their budgets but are estimates provided by the reporting units as a result of the analysis of the sector or scientific domain of the funded projects and activities. When the budget allocation is not addressed to an specific SEO estimates are done, generally based on the distribution of grants approved from the reference year or the previous year.

To ensure data quality, data provided by the reporting units is cross-referenced with the administrative data available from the general budgets of State and regions and with data from previous years.

An additional complexity comes from the fact that it is not always easy to separate R&D expenditure from other expenditures such as innovation or digitalisation expenditures. In some cases, estimates have to be applied.

12.1. Relevance - User Needs

See below.

12.1.1. Needs at national level

Users’ class1	Description of users	Users’ needs
1-Instutions: National level	Policy makers from Ministries, Parliament or Senate; Other institutional bodies, e.g. funding agencies; Institutional information providers; Statistical departments	Interministerial comparisons and effectiveness measures; International comparisons on budgetary efforts; Main components of budgetary efforts at central and regional level; Public spending efficiency: comparisons between GBARD and R&D expenditures; Longitudinal analysis and evolution of efforts across socio economic objectives; Directionality: strategic positioning vs final budget allocations;R&D policy design and decision making.
1-Institutions: Regional level	Regional governments; regional statistical units; Other institutional agents	Regional/national comparisons; R&D regional effort; R&D regional policy/ decision making; R&D efficiency; Directionality of R&D: socio economic priorities evaluation.
2-Social actors	Unions; Scientific associations; Business associations	Policy analysis and monitoring; Evaluation of R&D decisions; Impact of budgetary allocations on sectors and scientific domain.
3-Media	National media specialized in Science and Tecnology policies	Reliable data for international and regional comparisons to track the evolution of public spending and policies.
4-Researchers and students	Universities; Public Research Organizations; Other R&D organizations	Knowledge processing & analysis; Policy research.

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 user' satisfaction survey is done, but users can make comments through an email address available in the website.
User satisfaction survey specific for GBARD statistics	No specific survey for GBARD is done. Feedback from reporting units is received within the SICTI WG.
Short description of the feedback received	There is a lack of matching between GBARD requirements and the budgetary structure of some regional governments, in particular in relation to the non-separatability of R&D and innovation allocations and the destination of funds to general objectives.

12.3. Completeness

See below.

12.3.1. Data completeness - rate

100%

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.

	5 (Very Good)	4 (Good)	3 (Satisfactory)	2  (Poor)	1 (Very poor)	Reasons for missing cells
Provisional budget statistics1	X
Obligatory final budget statistics1	X
Optional final budget statistics2			X

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%.

12.3.3. Data availability

See below.

12.3.3.1. Data availability – Provisional data

	Availability1	Frequency of data collection	Gap years – years with missing data	Time of compilation (T+x)2	Comments
Total GBARD	Y-1980	Yearly	None	T+ 6 months
NABS Chapter level	Y-1981	Yearly	None	T+ 6 months
NABS Sub-chapter level	Y-1991	Yearly	None	T+ 6 months
Special categories - Biotech	N/A	N/A	N/A	N/A
Special categories - Nanotech	N/A	N/A	N/A	N/A
Special categories - Security	N/A	N/A	N/A	N/A

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

12.3.3.2. Data availability – Final data

	Availability1	Frequency of data collection	Gap years – years with missing data	Time of compilation (T+x)2	Comments
Total GBARD	Y-1980	Yearly	None	T+9 to 12 months	Only total GBARD for 1980 –1984. No gaps years: since 1995 Before 1995: N/A
NABS Chapter level	Y-1981	Yearly	None	T+9 to 12 months	No gaps years: since 1995 Before 1995: N/A
NABS Sub-chapter level	Y-1991	Yearly	None	T+9 to 12 months	No gaps years: since 1995 Before 1995: N/A
Special categories - Biotech	N/A	N/A	N/A	N/A
Special categories - Nanotech	N/A	N/A	N/A	N/A
Special categories - Security	N/A	N/A	N/A	N/A

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

12.3.3.3. Data availability – Other special categories

Special categories	Stage1	Availability1	Frequency of data colletion	Gap years – years with missing data	Time of compilation (T+x)3	Comments
N/A	N/A	N/A	N/A	N/A	N/A

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

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:

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.

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
	-	5	-	-	5	+/-

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.

13.1.2. Assessment of the accuracy

Indicators	5 (Very Good)1	4 (Good)2	3 (Satisfactory)3	2 (Poor)4	1 (Very poor)5
GBARD	X
National public funding to transnationally coordinated R & D		X

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.

13.2. Sampling error

Not requested.

13.2.1. Sampling error - indicators

Not requested.

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.

a)      Description/assessment of coverage errors: Local funds are not covered. It is possible that some local institutions in specific regions have significant budgeds in R&D

b)      Measures taken to reduce their effect: Revision pending

 

13.3.1.1. Over-coverage - rate

No data from units not included in the FM2015 definition (e.g. public-owned enterprises) are taken into account.

In case any allocation is reported twice the doubled item is immediately deleted.

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. The survey questionnaire used for data collection may have led to the recording of wrong values.

 a)       Description/assessment of measurement errors: GBARD data is based on administrative records provided by reporting units. Errors could come by calculating estimates, e.g. by desagregating R&D expenses from innovation expenses.

 b)      Measures taken to reduce their effect: Validation against administrative public data and comparison with previous years data

 

13.3.3. Non response error

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: Only for very specific appropiations for final data (definitive budget)

b) Measures taken to reduce their effect: Administrative public data is consulted and applyed when available. If final apppropiations are ot available at the needed desagregated level, then initial appropiations are applyed.

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

 

13.3.3.1. Unit non-response - rate

Not requested.

13.3.3.2. Item non-response - rate

Not requested.

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.

 a)       Data processing and editing processes: Not detected. Reviewing of data integration and validations through the system (SICTI) prevent processing errors

 b)      Description of errors: Not applicable

 c)       Measures taken to reduce their effect: Not applicable

13.3.5. Model assumption error

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 detected.

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

Date of first release of national data: 20th June

14.1.2. Time lag - final result

Date of first release of national data: 20th December

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)	6	12
Actual date of transmission of the data (T+x months)	6	12
Delay (days)	None	None
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. Survey Concepts Issues

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.

 

Concept / Issue	Reference to recommendations	Deviation from recommendations	National definition / Treatment / Deviations from recommendations
Research and development	FM2015 Chapter 2 (mainly paragraphs 2.3 and 2.4).	No deviation	The definition of research and development follows FM recommendaion. However some difficulties are found by some reporting units (mainly regions) for distinguishing between funds applied to R&D or innovation projects
Coverage of levels of government	FM2015, §12.5 to 12.9	No deviation
Socioeconomic objectives coverage and breakdown	Reg. 2020/1197: Annex 1, Table 20	Yes	In some cases, R&D budget distribution by NABS is not defined from the begining and estimates are reported by the reporting units. Several recommendations are stablished in order to calculate estimates, but can happen that socio economic objectives may be identified according to different criteria for some reporting units.
Reference period	Reg. 2020/1197: Annex 1, Table 20	No deviation

15.1.3. Deviations from recommendations

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.

Methodological issues	Reference to recommendations	Deviation from recommendations	National definition / Treatment / Deviations from recommendations
Definition of GBARD	FM § 12.9	No deviation	GBARD includes budget allocations to R&D activities of governments (central and regional) and public funding agencies excluding operating costs (personnel and non personnel) and reimbursable loans. Figures reflect basically capital expenditures in R&D and R&D grants and transfers. The same principles apply to regional budgets. Public Research Istitutions (performers) also provide information on operating costs.
Stages of data collection	FM2015 §12.41	No deviation
Gross / net approach, net principle	FM2015 §12.20 and 12.21	No deviation	Central and Regional governments budgets exclude allocations according to the net principle. However, some Public Research Organisations have difficulties in separating net and gross budget.
EU/other funds	Eurostat's EBS Methodological Manual on R&D Statistics	No deviation	EU funds are not included. Only the co-financed amount is reported. RRM funds are not included.
Types of expenditure	FM2015 §12.15 to 12.18	No deviation
Current and capital expenditure	FM §12.15	No deviation	Capital expenditures are included and reported by funding bodies when referred to R&D investments. Current expenditures for management and operational costs in funding agencies and public bodies are not included. Current expenditures from R&D performers (Public Research Organizatios) are included (Frascati, 82).
Extra budgetary funds	FM §12.8, 12.20, 12.38	No deviation	Data provided are based on consolidated figures. Extra budgetary funds are excluded.
Loans	FM §12.31, 12.32, 12.34	No deviation	Repayable loans are not included after methodological revision conducted in 2011.
Indirect funding, tax rebates, etc.	FM §12.31 - 12.38	No deviation	R&D budget does not include indirect funding, appropriations on tax rebates, etc.
Treatment of multi-annual projects	FM2015 §12.44	No deviation	Expenses are reported as they appear in the general budgets.
Treatment of GBARD going to R&D abroad	FM2015 §12.19	No deviation	National and regional public allocations for R&D performed abroad are included in GBARD, and they are also reported separatedly.
Criterion for distribution by socioeconomic objective	FM2015 §12.50 to 12.71	No deviation
Method of identification of primary objective	Eurostat's EBS Methodological Manual on R&D Statistics, topic 2, statement B.6	No deviation

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
Provisional data		2020, 2017, 1998, 1997, 1995, 1994	2020: Change in the metodology for calculating the coefficient for estimating GUF. 2017: Change in the process to collect data. Data is collected at microdata level (at budgetary allocation level). 1998: the defence objective in GBAORD included significant exceptional contributions by the Ministry for Industry and Energy, resulting in an increase of the defence budget of 300% over three years. 1997: Analysis of the previously reported data shows that since 1997 the inclusion in GBAORD of loans to be repaid has produced a break in series. Also, the inclusion of Spain’s contribution to CERN resulted in a significant change with regard to the energy objective. 1994 and 1995: Change in the coefficient for estimating GUF
Final data		2020, 2017, 2012, 2008-2004, 1998, 1997, 1995, 1994	2020: Change in the metodology for calculating the coefficient for estimating GUF. 2017: Change in the process to collect data. Data is collected at microdata level (at budgetary allocation level). 2012: Final data show separatedly R&D budget spent abroad. Potential differences due to higher levels of desagregation for information gathering. 2008-2004: Main criterion for the breakdown of time series from 2004 to 2008 of the final budgets for R&D has been the exclusion of refundable credits (loans) to allow for international comparability. 1998: the defence objective in GBAORD included significant exceptional contributions by the Ministry for Industry and Energy, resulting in an increase of the defence budget of 300% over three years. 1997: Analysis of the previously reported data shows that since 1997 the inclusion in GBAORD of loans to be repaid has produced a break in series. Also, the inclusion of Spain’s contribution to CERN resulted in a significant change with regard to the energy objective. 1994 and 1995: Change in the coefficient for estimating GUF.

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

15.3. Coherence - cross domain

Not requested.

15.3.1. Coherence - sub annual and annual statistics

Not requested.

15.3.2. Coherence - National Accounts

Not requested.

15.4. Coherence - internal

This part compares GBARD statistics from the provisional and final budget for the reference year. Unit: Million euro

15.4.1. Comparison between provisional and final data according to NABS 2007

	R&D allocations in the provisional budget delivered at T+6	R&D allocations in the final budget delivered at T+12	Difference (of final data)
Exploration and exploitation of the Earth	79.14	77.21	-2.4%
Environment	274.93	285.57	3.9%
Exploration and exploitation of space	540.24	567.96	5.1%
Transport, telecommunication and other infrastructures	265.27	229.92	-13.3%
Energy	238.41	244.83	2.7%
Industrial production and technology	826.99	849.62	2.7%
Health	1052.30	1556.59	9.9%
Agriculture	507.46	543.26	7.1%
Education	185.04	181.52	-1.9%
Culture, recreation, religion and mass media	57.67	56.59	-1.9%
Political and social systems, structures and processes	87.65	86.96	-0.8%
General advancement of knowledge: R&D financed from General University Funds (GUF)	2663.05	2750.86	3.3%
General advancement of knowledge: R&D financed from other sources than GUF	1715.09	1646.62	-0.4%
Defence	289.38	319.05	10.3%
TOTAL GBARD	8782.62	8996.56	2.4%

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	31180	No subcontracting
Data collection costs	Not available separately	No subcontracting
Other costs	Not available separately	No subcontracting
Total costs	31180	No subcontracting
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)	21	Data is requested to 21 units.
Average Time required to complete the questionnaire in hours (T)1	38	Estimated
Average hourly cost (in national currency) of a respondent (C)	21	The average hourly cost in the services sector in 2023 (estimated)
Total cost	16.758	According to proposed formula

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

a)       Provisional data:

 Administrative data and data from units of Central and Regional Governments managing R&D funds and Public Research Organizations

b)      Final data:

  Administrative data and data from units of Central and Regional Governments managing R&D funds and Public Research Organizations

c)       General University Funds (GUF):

Administrative data, data from Regional Departments responsible for universities and statistical data from NSO

18.2. Frequency of data collection

See 12.3.3.

18.3. Data collection

See below.

18.3.1. Data collection overview

	Provisional data	Final data	Comments
Data collection method	Request to data sources based on administrative records. Data collection, coordination, revision and agregation are made by the Ministry of Science, Innovation and Universities.	Request to data sources based on administrative records. Data collection, coordination, revision and agregation are made by the Ministry of Science, Innovation and Universities.
Stage of data collection	Initial budget appropriations: figures as voted by the legislature for the coming year, including changes introduced in the parliamentary debate. [FM 2015, 12.41.4]	Final budget appropriations: figures as voted by parliament for the coming year, including additional votes during the year. [FM 2015, 12.41.5]
Reporting units	Central and Regional Governments managing R&D funds and Public Research Organizations	Central and Regional Governments managing R&D funds and Public Research Organizations
Basic variable	Budget appropiations	Budget appropiations
Time of data collection (T+x)1)	T + (3-6 months)	T+ (3-12 months)
Problems in the translation of budget items	None

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.

18.3.2. General University Funds (GUF)

GUF estimates are based on proportion of costs devoted to R&D in universities.

18.3.3. Distribution by socioeconomic objectives (SEO)

Level of distribution of budgetary items – institution or programme/project	Programmes and projects are used for NABS distribution. In the case of appropiations with no specific SEO, NABS are estimated from data provided by the reporting units as a result of the analysis of the sector or specific domain of the funded projects or activities.
Criterion of distribution – purpose or content	It is difficult to distinguish between purpose or content and identify the robustness of the criteria used by respondents. There are some recommendations to distribute budgetary items according to SEO, however, this is difficult to implement because normally they are grouped by scientific and technical areas.
Method of identification of primary objectives	Reporting units are requered to follow the recommendations in Frascati Manual.
Difficulties of distribution	Central and Regional Governments do not use socioeconomic objectives (NABS) as a rule for budgetary allocation.

18.3.4. Questionnaire and other documents

Annex	Name of the file
GBARD national questionnaire and explanatory notes in English:
GBARD national questionnaire and explanatory notes in the national language:	Plantilla de créditos presupuestarios en I+D+I_2021 (xls); Validación de cálculo GBARD (xls); Manual de usuario de SICTI (pdf); Manual metodológico de créditos presupuestarios de I+D+I_SICTI (pdf); Nota metodológica para publicación web (pdf).
Other relevant documentation of national methodology in English:
Other relevant documentation of national methodology in the national language:

Annexes:
Questionnaire for reporting units (regions)
GBARD validation
Users´manual_RDI_budget allocations module_SICTI
GBARD methodological manual_SICTI
Explanatory notes

18.4. Data validation

The following validation activities are done: checking that population coverage and response rates are as required; comparing the statistics with previous cycles; confronting the statistics against administrative data (public budgets); checking inconsistencies and avoiding double counting in the statistics; performing micro and macro data editing.

18.5. Data compilation

See below.

18.5.1. Imputation - rate

Not applicable

18.5.2. Data compilation methods

See below.

18.5.2.1. Identifying R&D

Method(s) of separating R&D from non-R&D	Data are based on administrative records. Only when separation of R&D from non-R&D allocations is not clear, stimates are applied.
Description of the use of the coefficient (if applicable)	Estimation is done by the ministry/regional department concerned.
Coefficient estimation method	Not applicable
Frequency of updating of coefficients	Not applicable

18.5.2.2. General University Funds (GUF)

Method(s) of separating R&D from non-R&D	Estimates are based on consensus methodology.
Description of the use of the coefficient (if applicable)	GUF estimates are calculated by university, applying the coefficients obtained from the disaggregated data of R&D expenditure reported to HERD to the current transfers (general funds) that each university receives annually from the regional government.
Coefficient estimation method	Described in GBARD methological manual_annex
Frequency of updating of coefficients	Every two years

18.5.2.3. Other issues

Treatment of multi-annual programmes	Multi-annual programs are not reported in a single year. They are allocated to the years in which they are budgeted.
Possibility to classify budgetary items by COFOG functions	Yes, and specifically it can be classify according to Government Budget Function 46 (R&D)
Possibility to classify budgetary items by other nomenclatures e.g. NACE	No
Method of estimation of future budgets	Methods and estimates are not provided independently for R&D budget but for the overall budget. They are based on assumptions on growth rates and other macroeconomic variables.

18.6. Adjustment

Not requested.

18.6.1. Seasonal adjustment

Not requested.

No further comments.

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.

11 December 2025

Not requested.

Central government ministerial departments with R&D allocations, Regional administrative departments with R&D allocations and public research organizations with limited budgets included in the State General Budget or the Regional General Budgets

See below.

Not requested.

a) Calendar year: 2023. Calendar year=Fiscal year

 

b) Fiscal year: 2023

    Start month: January

    End month: December

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.

Million euro.

See below.

a)       Provisional data:

 Administrative data and data from units of Central and Regional Governments managing R&D funds and Public Research Organizations

b)      Final data:

  Administrative data and data from units of Central and Regional Governments managing R&D funds and Public Research Organizations

c)       General University Funds (GUF):

Administrative data, data from Regional Departments responsible for universities and statistical data from NSO

Twice a year.

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.