National Statistics Office

Public Finance Unit

NSO 

Lascaris

Valletta VLT2000

Malta

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.

R&D statistics are compiled for three institutional sectors of performance: Business Enterprise (BES), Government (GOV), and Higher Education (HES). Private Non-Profit (PNP) is considered to be negligible

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).

Main concepts and definitions used to produce R&D statistics are given by the Frascati Manual, the "Proposed standard practice for surveys of research and experimental development", OECD 2002, which is internationally recognized standard methodology for collecting R&D statistics.

"Research and experimental development (R&D) comprise creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society and the use of this stock of knowledge to devise new applications." (§ 63, Frascati Manual, OECD 2002).

"Intramural R&D expenditures are all expenditures for R&D performed within a statistical unit or sector of the economy during a specific period, whatever the source of funds." (§ 358, Frascati Manual, OECD 2002).

"R&D personnel include all persons employed directly on R&D, as well as those providing direct services such as R&D managers, administrators, and clerical staff. Those providing an indirect service, such as canteen and security staff, should be excluded." (§ 294 - 295, Frascati Manual, OECD 2002).

"Researchers are professionals engaged in the conception or creation of new knowledge, products, processes, methods and systems and also in the management of the projects concerned." (§ 301, Frascati Manual, OECD 2002).

The registered institutions are statistical units.

R&D statistics are compiled for R&D activity performed in the whole economy.

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.

The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level. 

Not requested.

R&D data for GOV and HES sector are  available from 2004 onwards

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.

Reference period is the calendar year.

a) Calendar year: Jan-Dec

b) Fiscal year:

    Start month: Jan

    End month: Dec

The Malta Statistics Authority (MSA) Act empowers the NSO to collect, compile, extract and release official statistics related to demographic, social, environment, economic and general activities and conditions of Malta.

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.

As a member of the European Union (EU), Malta has to observe the Council Regulations. Until 2003 R&D data were collected under gentleman's agreement. In 2003, Decision No 1608/2003/EC of the European parliament and of the Council of 22 July 2003 concerning the production and development of community statistics on science and technology was adopted. Between reference years 2003 to 2011 the data collection was based on the Commission Regulation No 753/2004 on statistics on science and technology (OJ L 118, page 23 from 23 April 2004), and as amended by the Commission Regulation (EC) No 973/2007 (OJL 216, page 10 from 21 August 2007). From the reference year 2012 onwards, the Commission Implementing Regulation on statistics science and technology No 995/2012 (OJ L 299, page 18 from 27 October 2012) applies

-          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:

b)       Confidentiality commitments of survey staff:

Data is disseminated in aggregate form and no statistical disclosure is applied onto it.

An advance release calendar is maintained by the NSO and published on the NSO website.  The calendar projects three months of news releases (including the current and two subsequent months).

https://nso.gov.mt/en/News_Releases/Release_Calendar/Pages/News-Release-Calendar.aspx

An internal policy on dissemination is in place to govern the dissemination of official statistics in an impartial, independent and timely manner, making them available simultaneously to all users.

The NSO’s primary channel for the dissemination of official statistics is the NSO website.  Tailored requests for statistical information may also be submitted through the NSO website.

Moreover, dedicated news releases are available in electronic format on the NSO website.

A news release is issued in June/July. This release is also uploaded on the NSO's website for future reference.

See below.

1) Y - Yes, N – No

A news release is published annually and all tables are available online on the NSO website.

Transmission tables sent to Eurostat are uploaded on the Eurobase under “Science and technology” at the following link: http://ec.europa.eu/eurostat/data/database

1) Y – Yes, N - No 

A news release is published annually and all tables are available online on the NSO website.

Transmission tables sent to Eurostat are uploaded on the Eurobase under “Science and technology” at the following link: http://ec.europa.eu/eurostat/data/database

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

Accompanying information has been uploaded on the NSO website including an explanation of the major fields of science, socio-economic objectives as well as transnational coordinated research. Methodological notes were also included in the questionnaire with definitions on what constitutes R&D and what should be excluded.

Not requested.

See below.

The NSO ensures that the statistical practices used to compile national R&D data follow the Frascati Manual recommendations.  

The NSO has developed an internal Quality Management Framework (QMF) which is built on common requirements of the ESS Code of Practice (ESS CoP).  A document was prepared to include a set of general quality guidelines spanning over all statistical domains.  Assuring methodological soundness is an integral part of the QMF, nonetheless, the document spans also on other areas related to institutional aspects.

Every five to seven years, the NSO participates in a Peer Review exercise through which the compliance of its operations with principles of the ESS CoP is assessed by an expert team.  Peer Reviews are indeed part of the European Statistical System (ESS) strategy to implement the ESS CoP.  Each NSI is expected to provide information as requested by a standard self-assessment questionnaire.  Following this, an expert team visits the office to meet NSI representatives and main stakeholders.  Peer Reviews result in a compliance report and the listing of a set of Improvement Actions which need to be followed up by the NSI. 

Malta's overall quality of the GOV R&D methodology is quite satisfactory. All data required by the commission is collected and transmitted on time. All entities in the HES provide us with high quality 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 completeness of both preliminary and final mandatory data is 100% satisfactory. 

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.

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:

 No coverage errors because the data is collected from the whole population, i.e. all Government Sector.

b)      Measures taken to reduce their effect:

 N/A

Not requested

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:

 Typical measurement errors occur during data entry. 

b)      Measures taken to reduce their effect:

 Data entry errors were reduced as the compiled questionnaire are being automatically uploaded in our system.  

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:

 Unit non-response may happen.  

b) Measures taken to reduce their effect:

 In the event of a unit non-response from an entity known that it provides R&D funding, we first chase and if unsuccessful we repeat last year's figures.  

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

 Minimal.

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 applicable. 

b)      Description of errors:

Not applicable. 

c)       Measures taken to reduce their effect:

The filled-in questionnaires are uploaded to our R&D IT system and the processing and outputs are generated within the system. 

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+12

Date of first release of national data: t+18

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.

GBARD data includes government funds towards private enterprises.

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)        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’)

At the NSO, there is currently no internal policy governing revisions that occur for all statistics produced.  Nonetheless, a revisions policy is being drafted to safeguard a coordinated revisions system across statistical domains.

This policy will take account of the need and causes for revisions; time and frequency of revisions; data and other statistical products affected by such revisions; and length of periods revised.

Data for a reference year are collected twice; the first time, provisional, at t+1 year, while final data are collected at t+2 years. Provisional data are subject to change, but revisions are very minimal.

No further revisions are collected for past years unless brought forward by the entity.

Not requested.

a)       Provisional data: An R&D annual questionnaire is sent out to all government ministries and departments.

                                      As regards government funding towards private enterprises for R&D, we get the data from 2 administrative sources; MEDE (for Scholarships) and Malta Council for Science and Technology (for the                                            National R&I Programme).

b)      Final data: R&D annual questionnaire

                           Data from MEDE and Malta Council for Science and Technology

c)       General University Funds (GUF): The R&D questionnaire specifically asks for R&D financed from General University Funds

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.

Not applicable

GBARD data obtained from the R&D survey is checked through an in-built validation procedure that checks the consistency of Question 14: General University Funds + Own Funds + Direct Government Fund (excluding GUF) is equal to Question 8 Total R&D financed by Government.  

The National R&I Programme data is checked for consistency and funds provided to the general government sector are eliminated to avoid double counting

See below.

Not applicable

See below.

Not requested.

Not requested.