Directorate General of Education and Science Statistics
(DGEEC - Direção Geral de Estatísticas da Educação e Ciência)

Directorate of Science and Technology and Information Society Statistics
(DSECTSI - Direção de Serviços de Estatística da Ciência e Tecnologia e da Sociedade de Informação)

Av. 24 de julho, 134
1399-054 Lisboa, PORTUGAL

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.

Relevant government units within the scope of GBARD - ministries and other government units funding R&D.

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: Reference year (January to December) 

b) Fiscal year: Not applicable

    Start month:

    End month:

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.

Although, there is not GBARD specific statistical legislation, the Protocol of delegation of statistical functions signed by DGEEC and Statistics Portugal is the legal instrument that gives to DGEEC the status of official statistical authority with obligations to comply with legal and regulatory provisions of the Portuguese National Statistical System (Law Nr. 22/2008 of May 13).

-          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: DGEEC proceeds according to the National Statistical System (Law Nr. 22/2008 of May 13th) that regulates statistical confidentiality (Article 6).

b)       Confidentiality commitments of survey staff: The staff working directly with statistical production have to sign commitment document, which ensures the acknowledge of the confidentiality issues and data protection law.

Not applicable

The calendars of statistical operations and statistical publications are publicly available (in January) on the DGEEC's website

https://estatistica.dgeec.mec.pt/cdeo.php

GBARD data is available to all users on the DGEEC’s website.
The Chief of Staff of the Minister of Science, Technology and Higher Education, 24 hours before the scheduled of release, can receive information under embargo, as it is publicly described in the European Statistics Code of Practice and in accordance with point 15, chapter B, of Statistics Portugal (INE).

Yearly.
https://www.dgeec.mec.pt/np4/209/

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

Along with the PDF publication, it is provided an Excel with all collected data, by institution (micro-data), for the reference year; general data (GBARD in million euros and in % of national budget), from 2002 onwards.
https://www.dgeec.mec.pt/np4/209/

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

In 2018, DGEEC introduced a methodological revision of the calculation of Budgetary appropriations for R&D, which essentially consisted of the breakdown of appropriations from national State Budget funds. The new methodology was applied retrospectively to the series of all national GBARD data from 2002 onwards, and previous publications have been reviewed and republished on the DGEEC website. By applying the new methodology to data from the year 2002, and not just in the most recent year, it pretends to avoid a time series break, while still allowing for a consistent analysis of the evolution of national long-term S&T and R&D allocations from that year on, the longest period possible. The GBARD methodology is described in national publications (https://www.dgeec.mec.pt/np4/209/).

Not requested.

See below.

DGEEC as the institution responsible for the production of official statistics, must respect and be governed by national and international statistical quality standards, in accordance with article 7 of Law Nr. 22/2008 of May 13, and by the European Statistics Code of Practice. In the case of the GBARD statistics, DGEEC also follows Eurostat's and OECD methodological recommendations.
Several data validation processes take place before the indicators are released. This validation procedures have been updated over the years that, complemented with the use of new database management tools, have resulted in more efficient and effective data management. The major improvements were obtained through the use of technology and information systems, through a wider range of administrative databases, through more efficient communication between organizations, but also due to the expertise staff involved in these statistical operations, who greatly contributed to the quality of the survey responses.
A thorough validation procedure of data collected is carried out, consisting in finding and correcting inconsistencies and by comparing with data from previous years or with other data sources. All efforts are made to reduce errors, to identify and correct them. Assistance/helpdesk is provided to respondents during data collection.
During the validation process, entities are contacted for further clarifications or for correcting errors.

DGEEC had also implemented a Quality Management System for improving the quality of its services by monitoring and measuring its services, processes and procedures. As part of this system, procedures are included in matters such as document management, resource management, human resources skills improvement on statistical production. The Quality Manual, as well as the commitment to quality could be also noticed in DGEEC’s annual programmes that are publicly available on its website. https://www.dgeec.mec.pt/np4/36/

The methodology used by Portugal to produce GBARD data is based on official sources. The mainly source is the annual Government General Budget (information provided by the national Directorate-General for the Budget (DGO) and Institute of Education Financial Management (IGEFE)).
The GBARD data is also complemented by the information requested by DGEEC to other national entities responsible for financing S&T/R&D.

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.

Not applicable

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: Not applicable

b)      Measures taken to reduce their effect: Not applicable

Not applicable

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:

b)      Measures taken to reduce their effect:

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:

b) Measures taken to reduce their effect:

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

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:

b)      Description of errors:

c)       Measures taken to reduce their effect:

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: 

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+10 (the 2021 provisional results were published at national level, in the 2nd semester). 

Date of first release of national data: T+10

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.

There might be some comparability problems because in Government-financed GERD it is possible that the R&D units include funds from Structural Programs financed by EU (Community funds).

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’)

Not applicable

Not requested.

Not requested.

a)       Provisional data: State Budget (estimated initial budget) + R&D survey (T-1) + Administrative data of public institutions budget (higher education and other entities from Ministry of Science) + official-letter requesting the data to some units.

b)      Final data: Provisional data + R&D survey (T-0).

c)       General University Funds (GUF): Administrative data of higher education institutions public budget (provided by Instituto de Gestão Financeira da Educação, I.P. (IGEFE, acronym in Portuguese))

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.

The General University Funds (GUF) are calculated through the amounts enrolled in the Portuguese state budget to institutions of higher education. Instituto de Gestão Financeira da Educação, I.P. (IGEFE, acronym in Portuguese) is the entity from the Ministry of Science, Technology and Higher Education, responsible for this information, which is provided to DGEEC.

A thorough validation procedure of data collected is carried out, mainly analysing inconsistencies among data from previous years. During the validation process, entities are contacted for further clarifications or for correcting errors. It's provided assistance to respondents during data collection.

See below.

Not applicable

See below.

Not requested.

Not requested.