Ministry of Science and Innovation

Deputy Directorate of Planning, Monitoring and Evaluation on R&D&I. General Directorate of Research Planification

Paseo de la Castellana, 162, 28046, Madrid

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.

All economic sectors are included.

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).

  3.4. Statistical concepts and definitions *

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 general state budget or the regional budgets.

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.

Million euro.

a) Calendar year: 2021. Calendar year=Fiscal year

b) Fiscal year: 2021

    Start month: January

    End month: December

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.

National Statistics Plan 2021-2024

-          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:

 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.

N/A - No information in this survey is confidential.

Twice a year: in june and december

Twice a year.

Public access.

Twice a year.

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

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

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

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

Not requested.

See below.

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. 

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 taken within the Working Group of SICTI (Information System on STI) where all the reporting units are represented. For example, the metodology for estimates of GUF has been revised by the WG recently. 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.

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.

100%

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: 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

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.

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: 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

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

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

 b)      Description of errors: N/A

 c)       Measures taken to reduce their effect: N/A

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.

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: 20th june

Date of first release of national data: 20th december

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)

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.

Not requested.

Not requested.

Not requested.

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

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’)

Not requested.

Not requested.

Not requested.

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

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.

Regional governments report GUF estimates based on proportion of costs devoted to R&D in universities.

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.

See below.

Not applicable

See below.

Not requested.

Not requested.

No further comments.