1.1. Contact organisation
National Statistics Office
1.2. Contact organisation unit
Public Finance Unit
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
NSO
Lascaris
Valletta VLT2000
Malta
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
31 October 2025
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The 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, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
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.
3.2. Classification system
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011.
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Tertiary education institution | Yes |
|---|---|
| University and colleges: core of the sector | Yes |
| University hospitals and clinics | No |
| Inclusion of units that primarily do not belong to HES and the borderline cases |
No |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Corresponds to the Frascati Manual |
|---|---|
| External R&D personnel | Postgraduate students are included if they are on the university payroll or if they are employed as university assistants or as other scientific staff to work on particular research projects and paid for by research grants |
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | Corresponds to Frascati Manual |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | The source of Funds in the FM are identified in the R&D surveys |
|---|---|
| Payments to rest of the world by sector - availability | Not available |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | No |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Does not apply |
| Difficulties to distinguish intramural from extramural R&D expenditure | Does not apply |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
|---|---|
| Source of funds | All sources of funds are distinguished |
| Type of R&D | The 3 types of R&D are distinguished |
| Type of costs | The 4 types of costs according to the FM are distinguished |
| Defence R&D - method for obtaining data on R&D expenditureNN | Not applicable to MT |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year |
|---|---|
| Function | No difficulties encountered |
| Qualification | No difficulties encountered |
| Age | We do not ask for the age of the researchers |
| Citizenship | This applies to Researchers only: National Citizenship Citizenship of the EU Member States Citizenship of other European Countries Citizenship of North America Citizenship of Central and South America Citizenship of Asia Citizenship of Africa Other citizenship |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar Year |
|---|---|
| Function | No difficulties encountered |
| Qualification | No difficulties encountered |
| Age | We do not ask for the age of the researchers |
| Citizenship | This applies to Researchers only: National Citizenship Citizenship of the EU Member States Citizenship of other European Countries Citizenship of North America Citizenship of Central and South America Citizenship of Asia Citizenship of Africa Other citizenship |
3.4.2.3. FTE calculation
The FTE is calculated by dividing the PT employees by 3. It’s a ratio that was established at the NSO
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
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 of institutional units.
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 HES Sector should consist of all R&D performing institutional 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.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | All institutions in the Higher Education Sector | |
| Estimation of the target population size | 5 units |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
R&D data for GOV and HES sector are available from 2004 onwards
3.9. Base period
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.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FT), in head count (PS), as a percentage of total employment and as a percentage of active population.
Reference period is the calendar year and the survey is produced annually
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.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the 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. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Malta Statistics Legislation. However the R&D statistics is not referred to in this legislation. It is a general legislation |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
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.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
a) Confidentiality protection required by law:
Article 40 of the MSA Act stipulates the restrictions on the use of information while Article 41 stipulates the prohibition of disclosure of information. Furthermore, Section IX of the Act (Offences and Penalties) lays down the measures to be taken in case of unlawful exercise of any officer of statistics regarding confidentiality of data.
b) Confidentiality commitments of survey staff:
Upon employment, all NSO employees are informed of the rules and duties pertaining to confidential information and its treatment. In line with stipulations of the MSA Act, before commencing work, every employee is required to take an oath of secrecy whose text is included in the same Act.
7.2. Confidentiality - data treatment
Data is disseminated in aggregate form and no statistical disclosure is applied onto it.
8.1. Release calendar
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)
8.2. Release calendar access
8.3. Release policy - user access
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
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Y | Regular release R&D Data |
| 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 | Links |
|---|---|---|
| General publication/article | Y | |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
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: Eurostat's online database
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
No micro-data access is available to outside users.
10.4.1. Provisions affecting the access
| Access rights to the micro-data | 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 | Yes | Aggregate figures | A news release is issued in June/July. This release is also uploaded on the NSO’s website for future reference |
| Data prepared for individual ad hoc requests | No | ||
| Other | No |
1) Y – Yes, N - No
10.6. Documentation on methodology
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.
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, quality reports, etc.) | 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 |
|---|---|
| Requests on further clarification, most problematic issues | Requests on further clarifications are quite limited however they are dealt with personally over the phone or by email. These vary from time-to-time and cannot be attributed to one specific issue. |
11.1. Quality assurance
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
11.2. Quality management - assessment
Malta's overall quality of the R&D methodology is quite satisfactory. All data required by the commission is collected and transmitted on time. Entities in the HES provide us with high quality data
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | Malta Council for Science and Technology | Public body established by the Central Government with the mandate of advising government on science and technology policy. Detailed data on capacity and trends of Malta's R&D performance for R&D and innovation and education policy decisions and strategy planning. |
| 1 | Parliament, Ministries, political parties, government departments, International Organisations | Aggregated R&D data |
| 3 | Media for general public | Analysis of changes in Malta’s R&D performance together with international comparisons |
| 4 | Researchers and students | Statistics, analysis and access to micro-data |
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 | The most recent User Satisfaction survey was carried out by the National Statistics Office in 2022. Occasionally we ask our main users to comment on the overall quality |
|---|---|
| User satisfaction survey specific for R&D statistics | No |
| Short description of the feedback received | Our main users were asked to comment on the overall quality of our R&D data published. Their feedback was that the data is useful, on time and in sufficient detail |
12.3. Completeness
Data completeness of both preliminary and final mandatory data is 100% satisfactory.
12.3.1. Data completeness - rate
Not applicable
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. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | Not applicable |
| Obligatory data on R&D expenditure | Not applicable |
| Optional data on R&D expenditure | Not applicable |
| Obligatory data on R&D personnel | Not applicable |
| Optional data on R&D personnel | Not applicable |
| Regional data on R&D expenditure and R&D personnel | Extra-Regio NUTS 1 and NUTS 2 are not applicable for Malta |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | 2004 | Annual | ||||
| Type of R&D | 2005 | Annual | ||||
| Type of costs | 2004 | Annual | ||||
| Socioeconomic objective | 2004 | Annual | ||||
| Region | 2004 | Annual | ||||
| FORD | 2004 | Annual | ||||
| Type of institution | 2004 | Annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | 2004 | Annual | ||||
| Function | 2004 | Annual | ||||
| Qualification | 2004 | Annual | ||||
| Age | Not available | Annual | ||||
| Citizenship | 2016 | Annual | ||||
| Region | 2004 | Annual | ||||
| FORD | 2004 | Annual | ||||
| Type of institution | 2004 | Annual |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | 2004 | Annual | ||||
| Function | 2004 | Annual | ||||
| Qualification | 2004 | Annual | ||||
| Age | Not available | Annual | ||||
| Citizenship | 2016 | Annual | ||||
| Region | 2004 | Annual | ||||
| FORD | 2004 | Annual | ||||
| Type of institution | 2004 | Annual |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| None | |||||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| By function and qualification | Headcount | Annual |
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:
- 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.
- 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 errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Total R&D personnel in FTE | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
| Researchers in FTE | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
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 (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
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 R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not be met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
Not applicable
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not applicable |
| Government | Not applicable |
| Higher education | Not applicable |
| Private non-profit | Not applicable |
| Rest of the world | Not applicable |
| Total | Not applicable |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not applicable |
| Technicians | Not applicable | |
| Other support staff | Not applicable | |
| Qualification | ISCED 8 | Not applicable |
| ISCED 5-7 | Not applicable | |
| ISCED 4 and below | Not applicable |
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:
No coverage errors observed
b) Measures taken to reduce their effect:
Not applicable
13.3.1.1. Over-coverage - rate
Not requested.
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 (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors:
No errors were identified
b) Measures taken to reduce their effect:
Not applicable
13.3.3. Non response error
Non-response occurs 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.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 5 | 5 | 0 |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item Non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | Not applicable | Not applicable | Not applicable |
| Comments | Not applicable | Not applicable | Not applicable |
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.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | The electronic filled in questionnaire is uploaded in the R&D IT system. Data entry errors are non-existant. Expenditure data is uploaded in units |
|---|---|
| Estimates of data entry errors | The questionnaire is uploaded directly in the R&D IT system in order to eliminate data entry errors |
| Variables for which coding was performed | No codes are used; not applicable |
| Estimates of coding errors | No codes are used; not applicable |
| Editing process and method | The questionnaire has in-built checks to ensure consistency between the different tables, moreover after uploading the questionnaire into the R&D IT system the first step is a validation process that checks the questionnaire was filled in properly. |
| Procedure used to correct errors | In case of logical inconsistencies or suspicious data values the respondent is re-contacted by phone or e-mail for data editing |
13.3.5. Model assumption error
Not requested.
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
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
a) End of reference period: Dec
b) Date of first release of national data: t+18
c) Lag (days):
14.1.2. Time lag - final result
a) End of reference period: Dec
b) Date of first release of national data: t+18
c) Lag (days):
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) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No issues
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No | |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | Yes | When we calculate head counts we calculate the number of full-time and part-time workers together |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | Yes | In Malta we have this ratio: 1FT = 3PT. So we divide the part time employees by 3, and then add them to the full timers |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | Please note that the questionnaire is sent via email |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No | We do not double check with funders’ accounts. We just get data from the performer. |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | Yes | We started making a distinction between GUFs, “Own” Funds, and Direct Government Funds in our 2005-2006 collection of data |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | 100% response rate |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No | Not applicable |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | Not applicable |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | Not applicable |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | No |
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 | |
|---|---|---|---|
| R&D personnel (HC) | 2004 | ||
| Function | 2004 | ||
| Qualification | 2004 | 2004, 2003 | We had a difficulty classifying the personnel by ISCED 5A and 5B. For 2005 data we enquired about the difference between the 2 and classified the data accordingly |
| R&D personnel (FTE) | 2004 | ||
| Function | 2004 | ||
| Qualification | 2004 | 2004, 2003 | We had a difficulty classifying the personnel by ISCED 5A and 5B. For 2005 data we enquired about the difference between the 2 and classified the data accordingly |
| R&D expenditure | 2004 | ||
| Source of funds | 2004 | 2003 | Expenditure by sources of funds was never asked in the survey |
| Type of costs | 2004 | ||
| Type of R&D | 2004 | 2003 and 2002 for current intramural costs. 2003 for total intramural costs | Such expenditure by costs was never asked for 2002 and 2003 data |
| Other | 2004 | 2003 expenditure by fields of science. 2006 for GBARD data | Such expenditure was never asked for 2002 and 2003 data. In 2006, for GBARD data, we started excluding foreign funding |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
No differences
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual (FM) regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not applicable
15.3.3. Coherence – Education statistics
There are no other statistics for which data from HES can be compared with.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 53366.36 | 732 | 581 |
| Final data (delivered T+18) | 53366.36 | 732 | 581 |
| Difference (of final data) | 0 | 0 | 0 |
Comments:
Reference year 2023
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | €20,694.9 | Not applicable |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not applicable | Not applicable |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
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) | Cost for the NSI in time use/person/day | |
|---|---|---|
| Staff costs | Not available seperately | None |
| Data collection costs | Not available seperately | None |
| Other costs | Not available seperately | None |
| Total costs | Not available seperately | None |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 5 | |
| Average Time required to complete the questionnaire in hours (T)1) | Not possible to estimate - respondents were not asked for the time taken to fill in the questionnaire | |
| Average hourly cost (in national currency) of a respondent (C) | Not possible to estimate the hourly cost of a respondent | |
| Total cost | Not available |
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
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
PRELIMINARY Data for 2023:
Start of Survey - 18 April 2024
First Reminder - 14 May 2024
Second Reminder - 29 May 2024
FINAL Data for 2023:
Start of Survey - 28 April 2025
First Reminder - 15 May 2025
Second Reminder - 28 May 2025
18.1.2. Sample/census survey information
| Sampling unit | Not applicable |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | Not applicable |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | Not applicable |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Not applicable |
| Variables the survey contributes to | Not applicable |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | The HES comprises all units involved in the educational sector |
|---|---|
| Description of collected data / statistics | a) The number of R&D personnel, by FT/PT, by field of science, by categories of R&D personnel, by gender, by level of qualification in the end of year; b) The researches, by FT/PT, by gender, by citizenship in the end of year; c) The intramural expenditure devoted to R&D during year by field of science, by sources of financing (local and foreign sources further split into more sources), by type of costs, by type of R&D activities, by socio-economic objectives |
| Reference period, in relation to the variables the administrative source contributes to | 2023 |
| Variables the administrative source contributes to | Not applicable |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | All institutions in the Higher Education Sector |
|---|---|
| Description of collected information | a) The number of R&D personnel, by FT/PT, by field of science, by categories of R&D personnel, by gender, by level of qualification in the end of year; b) The researchers, by FT/PT, by gender, by citizenship in the end of year; c) The intramural expenditure devoted to R&D during year by field of science, by sources of financing (local and foreign sources further split into more sources), by type of costs, by type of R&D activities, by socio-economic objectives – only available for 2005). The FTE is calculated by dividing the PT employees by 3. It’s a ratio that was established at the NSO |
| Data collection method | A questionnaire in excel format is sent by email to enable the respondents to fill up the questionnaire electronically. Email reminders are sent if they do not reply on time and follow up by phone calls, if necessary |
| Time-use surveys for the calculation of R&D coefficients | None |
| Realised sample size (per stratum) | 5 |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | By email |
| Incentives used for increasing response | No incentives were used |
| Follow-up of non-respondents | Two reminders sent by email |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | For non-respondents of known R&D performers, the previous year's questionnaire is retained |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 100% |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Not applicable |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Survey of Research and Development in the Higher Education Sector.pdf |
| R&D national questionnaire and explanatory notes in the national language: | Not applicable |
| Other relevant documentation of national methodology in English: | Not applicable |
| Other relevant documentation of national methodology in the national language: | Not applicable |
18.4. Data validation
Upon receiving every questionnaire this is checked, primarily to make sure that the type of R&D reported is what is classified as R&D for NSO purposes. Secondly, the questionnaire is checked with what had been reported in the previous year and double check if any large discrepancies arise. Thirdly, we make sure that all tables are in line with one another.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
| Data compilation method - Final data | A survey is sent out every year |
|---|---|
| Data compilation method - Preliminary data | Data is collected twice for every year; in April 2024 we requested data for 2022 and 2023 and in April 2025 we requested data for 2023 and 2024 |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | No R&D coefficients are used |
|---|---|
| Revision policy for the coefficients | |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). |
18.5.4. Measurement issues
| Method of derivation of regional data | Regional data does not apply to MT"s |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | For the MT"s GOV and HES sectors, we use an established ratio of 1Full Time = 3 Part Time, to calculate the Full Time. For the BES sector 2Part Time are taken as 1Full Time |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT are excluded from R&D expenditure. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | So far we distinguish between direct government funds and own funds. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Not applicable |
|---|---|
| Description of the estimation method | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The 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, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
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.
31 October 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
See below.
Not requested. R&D statistics cover national and regional data.
Reference period is the calendar year and the survey is produced annually
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:
- 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.
- 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.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FT), in head count (PS), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
A news release is issued in June/July. This release is also uploaded on the NSO’s website for future reference
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.
